A service based adaptive U-learning system using UX.

PubMed

Jeong, Hwa-Young; Yi, Gangman

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

A Service Based Adaptive U-Learning System Using UX

PubMed Central

Jeong, Hwa-Young

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

Learning in a u-Museum: Developing a Context-Aware Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Chia-Chen; Huang, Tien-Chi

2012-01-01

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the…

eLearning techniques supporting problem based learning in clinical simulation.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Challenges of Using Learning Analytics Techniques to Support Mobile Learning

ERIC Educational Resources Information Center

Arrigo, Marco; Fulantelli, Giovanni; Taibi, Davide

2015-01-01

Evaluation of Mobile Learning remains an open research issue, especially as regards the activities that take place outside the classroom. In this context, Learning Analytics can provide answers, and offer the appropriate tools to enhance Mobile Learning experiences. In this poster we introduce a task-interaction framework, using learning analytics…

SoS Navigator 2.0: A Context-Based Approach to System-of-Systems Challenges

DTIC Science & Technology

2008-06-01

in a Postindustrial Age. MIT Press, 1984. [ Kolb 1984] Kolb , David A. Experiential Learning : Experience as the Source of Learning and Develop- ment...terms of experiential learning , and the work of Rosen [Rosen 1991] in terms of the relational approach to understanding anticipa- tive systems. Our...Supporting Techniques and Tools 17  3.2  The Learning /Transformation Cycle 19  3.3  Summary of SoS Navigator Processes and Techniques 20  4  Case Summaries 22

Computer-Supported Instruction in Enhancing the Performance of Dyscalculics

ERIC Educational Resources Information Center

Kumar, S. Praveen; Raja, B. William Dharma

2010-01-01

The use of instructional media is an essential component of teaching-learning process which contributes to the efficiency as well as effectiveness of the teaching-learning process. Computer-supported instruction has a very important role to play as an advanced technological instruction as it employs different instructional techniques like…

Online Student Induction: A Case Study of the Use of Mass Customization Techniques

ERIC Educational Resources Information Center

Phillips, Marion; Hawkins, Rachel; Lunsford, Jane; Sinclair-Pearson, Andrew

2004-01-01

New technology within Open and Distance learning (ODL) provides new opportunities for the delivery of learner support resources. Mass customization techniques offer the advantages of efficient production combined with the development of a learning experience precisely tailored for the individual's study requirements. In this article, we discuss…

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Discourse-Centric Learning Analytics: Mapping the Terrain

ERIC Educational Resources Information Center

Knight, Simon; Littleton, Karen

2015-01-01

There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high-quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA…

Using Augmented Reality to Support a Software Editing Course for College Students

ERIC Educational Resources Information Center

Wang, Y.-H.

2017-01-01

This study aimed to explore whether integrating augmented reality (AR) techniques could support a software editing course and to examine the different learning effects for students using online-based and AR-based blended learning strategies. The researcher adopted a comparative research approach with a total of 103 college students participating…

Classroom Techniques for Improving Black Male Student Retention.

ERIC Educational Resources Information Center

Gardenhire, John Fouts

Institutions of higher learning must focus on new ways to serve the at-risk student and the black male at-risk student in particular. By developing and implementing a plan, any teacher can foster retention of at-risk students, even in the absence of institutional support. Twenty effective techniques are: (1) learn students' names; (2) assign…

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

Machine learning in heart failure: ready for prime time.

PubMed

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Game Design Narrative for Learning: Appropriating Adventure Game Design Narrative Devices and Techniques for the Design of Interactive Learning Environments

ERIC Educational Resources Information Center

Dickey, Michele D.

2006-01-01

The purpose of this conceptual analysis is to investigate how contemporary video and computer games might inform instructional design by looking at how narrative devices and techniques support problem solving within complex, multimodal environments. Specifically, this analysis presents a brief overview of game genres and the role of narrative in…

Dyads versus Groups: Using Different Social Structures in Peer Review to Enhance Online Collaborative Learning Processes

ERIC Educational Resources Information Center

Pozzi, Francesca; Ceregini, Andrea; Ferlino, Lucia; Persico, Donatella

2016-01-01

The Peer Review (PR) is a very popular technique to support socio-constructivist and connectivist learning processes, online or face-to-face, at all educational levels, in both formal and informal contexts. The idea behind this technique is that sharing views and opinions with others by discussing with peers and receiving and providing formative…

How Effective Is Example Generation for Learning Declarative Concepts?

ERIC Educational Resources Information Center

Rawson, Katherine A.; Dunlosky, John

2016-01-01

Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this…

Writing-to-Learn Strategies in Secondary School Cell Biology: A Mixed Method Study

ERIC Educational Resources Information Center

Hohenshell, Liesl M.; Hand, Brian

2006-01-01

Writing-to-learn techniques can enhance learning, yet a need remains for more empirical research on the quality of learning that results from engaging in particular writing tasks with description of the instructional support for writing situated in context. This report builds on past research linking inquiry, social negotiation, and writing…

Revitalizing the Physics Department: The Use of Interactive Technologies to Improve Student Learning

NASA Astrophysics Data System (ADS)

Sheldon, Peter; Groover, Holly

2002-04-01

The Physics Department at Randolph-Macon Woman's College, a liberal arts women's college of 720, has traditionally turned out approximately 0.6 majors/year. We have invigorated the program by adding community (e.g. SPS, physical space, organized activities), adding a significant technical component (e.g. web-assisted and computerized labs and more technology in the classes [1]), and incorporating new learning techniques (JITT, Physlets, Peer Instruction and Cooperative Learning [2]). Students have responded well as evidenced by significant increases in enrollments as well as strong scores on the FCI. We have seen mixed results in the lab, but increased performance in the class, which is attributed to the interactive learning techniques that are being implemented through new technologies. In this presentation, we will discuss the implementation of the new curricular developments and the specific changes we have seen in student learning. [1] This work is supported in part by the NSF CCLI Program under grant DUE-9980890. Additional support has been from the Virginia Foundation of Private Colleges and AT&T. [2] See, for example, the project Galileo website http://galileo.harvard.edu for a description of all of these techniques.

Student Modeling and Ab Initio Language Learning.

ERIC Educational Resources Information Center

Heift, Trude; Schulze, Mathias

2003-01-01

Provides examples of student modeling techniques that have been employed in computer-assisted language learning over the past decade. Describes two systems for learning German: "German Tutor" and "Geroline." Shows how a student model can support computerized adaptive language testing for diagnostic purposes in a Web-based language learning…

Debriefing after Human Patient Simulation and Nursing Students' Learning

ERIC Educational Resources Information Center

Benhuri, Gloria

2014-01-01

Human Patient Simulation (HPS) exercises with life-like computerized manikins provide clinical experiences for nursing students in a safe environment followed by debriefing that promotes learning. Quantitative research in techniques to support learning from debriefing is limited. The purpose of the quantitative quasi-experimental study using a…

Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification

ERIC Educational Resources Information Center

Emond, Bruno; Buffett, Scott

2015-01-01

This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…

The Strengths of Latina Mothers in Supporting Their Children's Education: "A Cultural Perspective." Research Brief. Publication #2014-29

ERIC Educational Resources Information Center

Ramos, Manica

2014-01-01

A growing body of research indicates that when parents are engaged in their children's learning and development, their children do better in school. This brief reports on what was learned through recent interviews with Latina immigrant mothers about the techniques they used to support their children's education at the most malleable stage of…

Graphical Technique to Support the Teaching/Learning Process of Software Process Reference Models

NASA Astrophysics Data System (ADS)

Espinosa-Curiel, Ismael Edrein; Rodríguez-Jacobo, Josefina; Fernández-Zepeda, José Alberto

In this paper, we propose a set of diagrams to visualize software process reference models (PRM). The diagrams, called dimods, are the combination of some visual and process modeling techniques such as rich pictures, mind maps, IDEF and RAD diagrams. We show the use of this technique by designing a set of dimods for the Mexican Software Industry Process Model (MoProSoft). Additionally, we perform an evaluation of the usefulness of dimods. The result of the evaluation shows that dimods may be a support tool that facilitates the understanding, memorization, and learning of software PRMs in both, software development organizations and universities. The results also show that dimods may have advantages over the traditional description methods for these types of models.

Influences of Learning Environment Characteristics on Student Learning During Authentic Science Inquiry in an Introductory Physical Geology Course

NASA Astrophysics Data System (ADS)

Miller, H. R.; Sell, K. S.; Herbert, B. E.

2004-12-01

Shifts in learning goals in introductory earth science courses to greater emphasis on critical thinking and the nature of science has led to the adoption of new pedagogical techniques, including inquiry-based learning (IBL). IBL is thought to support understanding of the nature of science and foster development of scientific reasoning and critical thinking skills by modeling authentic science inquiry. Implementation of new pedagogical techniques do not occur without influence, instruction and learning occurs in a complex learning environment, referring to the social, physical, mental, and pedagogical contexts. This study characterized the impact of an IBL module verses a traditionally structured laboratory exercise in an introductory physical geology class at Texas A&M University. Student activities in this study included manipulation of large-scale data sets, use of multiple representations, and exposure to ill-constrained problems common to the Texas Gulf Coast system. Formative assessment data collected included an initial survey of self efficacy, student demographics, content knowledge and a pre-mental model expression. Summative data collected included a post-test, post-mental model expression, final laboratory report, and a post-survey on student attitudes toward the module. Mental model expressions and final reports were scored according to a validated rubric instrument (Cronbrach alpha: 0.84-0.98). Nine lab sections were randomized into experimental and control groups. Experimental groups were taught using IBL pedagogical techniques, while the control groups were taught using traditional laboratory "workbook" techniques. Preliminary assessment based on rubric scores for pre-tests using Student's t-test (N ˜ 140) indicated that the experimental and control groups were not significantly different (ρ > 0.05), therefore, the learning environment likely impacted student's ability to succeed. A non-supportive learning environment, including student attitudes, teaching assistant attitudes, the lack of scaffolded learning, limited pedagogical content knowledge, and departmental oversight, which were all encountered during this study, can have an affect on the students' attitudes and achievements during the course. Data collected showed an overall improvement in content knowledge (38% increase); while performance effort clearly declined as seen through post-mental model expressions (a decline in performance by 24.8%) and percentage of assignments turned in (39% of all students turned in the required final report). A non-supportive learning environment was also seen through student comments on the final survey, "I think that all the TA's and the professor have forgotten that we are an intro class". A non-supportive environment clearly does not encourage critical thinking and completion of work. This pilot study showed that the complex learning environment can play a significant role in student learning. It also illustrates the need for future studies in IBL with supportive learning environments in order for students to achieve academic excellence and develop scientific reasoning and critical thinking skills.

Neighborhood graph and learning discriminative distance functions for clinical decision support.

PubMed

Tsymbal, Alexey; Zhou, Shaohua Kevin; Huber, Martin

2009-01-01

There are two essential reasons for the slow progress in the acceptance of clinical case retrieval and similarity search-based decision support systems; the especial complexity of clinical data making it difficult to define a meaningful and effective distance function on them and the lack of transparency and explanation ability in many existing clinical case retrieval decision support systems. In this paper, we try to address these two problems by introducing a novel technique for visualizing inter-patient similarity based on a node-link representation with neighborhood graphs and by considering two techniques for learning discriminative distance function that help to combine the power of strong "black box" learners with the transparency of case retrieval and nearest neighbor classification.

Incorporating Service-Learning, Technology, and Research Supportive Teaching Techniques into the University Chemistry Classroom

ERIC Educational Resources Information Center

Saitta, E. K. H.; Bowdon, M. A.; Geiger, C. L.

2011-01-01

Technology was integrated into service-learning activities to create an interactive teaching method for undergraduate students at a large research institution. Chemistry students at the University of Central Florida partnered with high school students at Crooms Academy of Information Technology in interactive service learning projects. The…

Revisiting E-Learning Effectiveness: Proposing a Conceptual Model

ERIC Educational Resources Information Center

Macgregor, George; Turner, James

2009-01-01

Purpose: The use of e-learning is largely predicated upon the assumption that it can facilitate improvements in student learning and therefore can be more effective than conventional techniques. This assumption has been supported by some in the literature but has been questioned by a continuing body of contrary or indifferent evidence. The purpose…

Self-Regulated Learning and the Role of ePortfolios in Business Studies

ERIC Educational Resources Information Center

Morales, Lucía; Soler-Domínguez, Amparo; Tarkovska, Valentina

2016-01-01

Students' work in ePortfolios was assessed through a case study supported by observation techniques and eQuestionnaires to gather data from a sample of eighty students over a period of 4 years (20 students per academic year). The main purpose of the study was to explore whether ePortfolios can be used efficiently to support the learning process of…

The Impact of Interactive Environment and Metacognitive Support on Academic Achievement and Transactional Distance in Online Learning

ERIC Educational Resources Information Center

Yilmaz, Ramazan; Keser, Hafize

2017-01-01

The aim of the present study is to reveal the impact of the interactive environment and metacognitive support (MS) in online learning on academic achievement and transactional distance (TD). The study is designed as 2 × 2 factorial design, and both qualitative and quantitative research techniques are used. The study was carried out on 127…

Relative Effectiveness of Computer-Supported Jigsaw II, STAD and TAI Cooperative Learning Strategies on Performance, Attitude, and Retention of Secondary School Students in Physics

ERIC Educational Resources Information Center

Gambari, Amosa Isiaka; Yusuf, Mudasiru Olalere

2017-01-01

This study investigated the relative effectiveness of computer-supported cooperative learning strategies on the performance, attitudes, and retention of secondary school students in physics. A purposive sampling technique was used to select four senior secondary schools from Minna, Nigeria. The students were allocated to one of four groups:…

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

2016-08-01

In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach

NASA Technical Reports Server (NTRS)

Das, Santanu; Oza, Nikunj C.

2011-01-01

In this paper we propose an innovative learning algorithm - a variation of One-class nu Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class nu SVMs while reducing both training time and test time by several factors.

A Comparison of Professional-Level Faculty and Student Perceptions of Active Learning: Its Current Use, Effectiveness, and Barriers

ERIC Educational Resources Information Center

Miller, Cynthia J.; Metz, Michael J.

2014-01-01

Active learning is an instructional method in which students become engaged participants in the classroom through the use of in-class written exercises, games, problem sets, audience-response systems, debates, class discussions, etc. Despite evidence supporting the effectiveness of active learning strategies, minimal adoption of the technique has…

Evaluating an Anxiety Group for People with Learning Disabilities Using a Mixed Methodology

ERIC Educational Resources Information Center

Marwood, Hayley; Hewitt, Olivia

2013-01-01

The effectiveness of group therapy for people with learning disabilities and anxiety management issues is reviewed. People with learning disabilities face increased levels of psychological distress compared to the general population, yet are often faced with a lack of social support and poor coping techniques to manage their distress. A 6-week…

Participant Comfort with and Application of Inquiry-Based Learning: Results from 4-H Volunteer Training

ERIC Educational Resources Information Center

Haugen, Heidi; Stevenson, Anne; Meyer, Rebecca L.

2016-01-01

This article explores how a one-time training designed to support learning transfer affected 4-H volunteers' comfort levels with the training content and how comfort levels, in turn, affected the volunteers' application of tools and techniques learned during the training. Results of a follow-up survey suggest that the training participants…

Design of Sensors for Control of Closed Loop Life Support Systems

NASA Technical Reports Server (NTRS)

1990-01-01

A brief summary is presented of a Engineering Design sequence, a cooperation between NASA-Kennedy and the University of Florida on the Controlled Environmental Life Support System (CELSS) program. Part of the class was devoted to learning general principles and techniques of design. The next portion of the class was devoted to learning to design, actually fabricating and testing small components and subsystems of a CELSS.

Analysis of student’s scientific attitude behaviour change effects blended learning supported by I-spring Suite 8 application

NASA Astrophysics Data System (ADS)

Budiharti, Rini; Waras, N. S.

2018-05-01

This article aims to describe the student’s scientific attitude behaviour change as treatment effect of Blended Learning supported by I-Spring Suite 8 application on the material balance and the rotational dynamics. Blended Learning models is learning strategy that integrate between face-to-face learning and online learning by combination of various media. Blended Learning model supported I-Spring Suite 8 media setting can direct learning becomes interactive. Students are guided to actively interact with the media as well as with other students to discuss getting the concept by the phenomena or facts presented. The scientific attitude is a natural attitude of students in the learning process. In interactive learning, scientific attitude is so needed. The research was conducted using a model Lesson Study which consists of the stages Plan-Do-Check-Act (PDCA) and applied to the subject of learning is students at class XI MIPA 2 of Senior High School 6 Surakarta. The validity of the data used triangulation techniques of observation, interviews and document review. Based on the discussion, it can be concluded that the use of Blended Learning supported media I-Spring Suite 8 is able to give the effect of changes in student behaviour on all dimensions of scientific attitude that is inquisitive, respect the data or fact, critical thinking, discovery and creativity, open minded and cooperation, and perseverance. Display e-learning media supported student worksheet makes the students enthusiastically started earlier, the core until the end of learning

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

[Organization development of the public health system].

PubMed

Pfaff, Holger; Klein, Jürgen

2002-05-15

Changes in the German health care system require changes in health care institutions. Organizational development (OD) techniques can help them to cope successfully with their changing environment. OD is defined as a collective process of learning aiming to induce intended organizational change. OD is based on social science methods and conducted by process-oriented consultants. In contrast to techniques of organizational design, OD is characterized by employee participation. One of the most important elements of OD is the so-called "survey-feedback-technique". Five examples illustrate how the survey-feedback-technique can be used to facilitate organisational learning. OD technique supports necessary change in health care organizations. It should be used more frequently.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

Using Elearning techniques to support problem based learning within a clinical simulation laboratory.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2004-01-01

This paper details the results of the first phase of a project that used eLearning to support students' learning within a simulated environment. The locus was a purpose built Clinical Simulation Laboratory (CSL) where the School's newly adopted philosophy of Problem Based Learning (PBL) was challenged through lecturers reverting to traditional teaching methods. The solution, a student-centred, problem-based approach to the acquisition of clinical skills was developed using learning objects embedded within web pages that substituted for lecturers providing instruction and demonstration. This allowed lecturers to retain their facilitator role, and encouraged students to explore, analyse and make decisions within the safety of a clinical simulation. Learning was enhanced through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that an elearning approach can support PBL in delivering a student centred learning experience.

Investigating Cognitive Task Difficulties and Expert Skills in E-Learning Storyboards Using a Cognitive Task Analysis Technique

ERIC Educational Resources Information Center

Yusoff, Nor'ain Mohd; Salim, Siti Salwah

2012-01-01

E-learning storyboards have been a useful approach in distance learning development to support interaction between instructional designers and subject-matter experts. Current works show that researchers are focusing on different approaches for use in storyboards, and there is less emphasis on the effect of design and process difficulties faced by…

Techniques for Programming Visual Demonstrations.

ERIC Educational Resources Information Center

Gropper, George L.

Visual demonstrations may be used as part of programs to deliver both content objectives and process objectives. Research has shown that learning of concepts is easier, more accurate, and more broadly applied when it is accompanied by visual examples. The visual examples supporting content learning should emphasize both discrimination and…

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Inverse Problems in Geodynamics Using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

NASA Astrophysics Data System (ADS)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

Initial Skill Acquisition of Handrim Wheelchair Propulsion: A New Perspective.

PubMed

Vegter, Riemer J K; de Groot, Sonja; Lamoth, Claudine J; Veeger, Dirkjan Hej; van der Woude, Lucas H V

2014-01-01

To gain insight into cyclic motor learning processes, hand rim wheelchair propulsion is a suitable cyclic task, to be learned during early rehabilitation and novel to almost every individual. To propel in an energy efficient manner, wheelchair users must learn to control bimanually applied forces onto the rims, preserving both speed and direction of locomotion. The purpose of this study was to evaluate mechanical efficiency and propulsion technique during the initial stage of motor learning. Therefore, 70 naive able-bodied men received 12-min uninstructed wheelchair practice, consisting of three 4-min blocks separated by 2 min rest. Practice was performed on a motor-driven treadmill at a fixed belt speed and constant power output relative to body mass. Energy consumption and the kinetics of propulsion technique were continuously measured. Participants significantly increased their mechanical efficiency and changed their propulsion technique from a high frequency mode with a lot of negative work to a longer-slower movement pattern with less power losses. Furthermore a multi-level model showed propulsion technique to relate to mechanical efficiency. Finally improvers and non-improvers were identified. The non-improving group was already more efficient and had a better propulsion technique in the first block of practice (i.e., the fourth minute). These findings link propulsion technique to mechanical efficiency, support the importance of a correct propulsion technique for wheelchair users and show motor learning differences.

COMPUTERS FOR EDUCATION.

ERIC Educational Resources Information Center

LEBEDEV, P.D.

ON THE PREMISES THAT THE DEVELOPMENT OF PROGRAMED LEARNING BY RESEARCH TEAMS OF SUBJECT AND TECHNIQUE SPECIALISTS IS INDISPUTABLE, AND THAT THE EXPERIENCED TEACHER IN THE ROLE OF INDIVIDUAL TUTOR IS INDISPENSABLE, THE TECHNOLOGY TO SUPPORT PROGRAMED INSTRUCTION MUST BE ADVANCED. AUTOMATED DEVICES EMPLOYING SEQUENTIAL AND BRANCHING TECHNIQUES FOR…

Teaching English through Open Non-Formal Education (ONFE) in Bangladesh with an Effective Integration of ICT to Support Learning

ERIC Educational Resources Information Center

Rahman, Md. Mizanoor; Panda, Santosh

2012-01-01

The program entitled "English in Action (EIA)", 9 year period DFID funded project in Bangladesh, was launched in 2008, for the desire to bring a change in the learning of English language. EIA works to reach a total of 25 million primary and secondary students and adult learners through communicative language learning techniques and the…

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

PubMed Central

Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice. PMID:28812013

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

PubMed

Banjar, Haneen; Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

Using flashcards to support your learning.

PubMed

Bryson, David

2012-03-01

The idea that if you listen to a lecture and make notes you will, by some mysterious process, have learnt all that the lecturer has covered, is a myth. Unfortunately, the lecture is just the start. The best way to learn is by doing. This can be achieved by completing set practical tasks by reading and creating your own notes, listening again to parts or the whole of a lecture via a screencast or using diagrams and illustrations that you annotate. Similarly creating your own learning materials can be useful as actually putting questions and answers together helps you to learn. One of the many ways to support your learning, especially in difficult topics like anatomy and physiology, learning about bones, medical terminology or indeed any subject where there are a lot of new words and terms to learn, is making use of an old technique brought up to date with new technologies called "flashcards".

Effects of a Peer Evaluation Technique on Nursing Students' Anxiety Levels.

PubMed

Stewart, Patricia; Greene, Debbie; Coke, Sallie

2017-11-16

Techniques to help decrease students' stress and anxiety during a nursing program can be beneficial to their overall health and mental well-being. A quasi-experimental design was used to examine if a peer evaluation technique during clinical skill practice sessions decreases anxiety prior to skill performance evaluation with nursing faculty. Participant feedback supports the integration of a peer evaluation technique when learning clinical skills.

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

NASA Astrophysics Data System (ADS)

Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

2017-11-01

This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

Student Perceptions of Immediate Feedback Testing in Student Centered Chemistry Classes

ERIC Educational Resources Information Center

Schneider, Jamie L.; Ruder, Suzanne M.; Bauer, Christopher F.

2018-01-01

Feedback is an important aspect of the learning process. The immediate feedback assessment technique (IF-AT®) form allows students to receive feedback on their answers during a testing event. Studies with introductory psychology students supported both perceived and real student learning gains when this form was used with testing. Knowing that…

Using the Technique of Journal Writing to Learn Emergency Psychiatry

ERIC Educational Resources Information Center

Bhuvaneswar, Chaya; Stern, Theodore; Beresin, Eugene

2009-01-01

Objective: The authors discuss journal writing in learning emergency psychiatry. Methods: The journal of a psychiatry intern rotating through an emergency department is used as sample material for analysis that could take place in supervision or a resident support group. A range of articles are reviewed that illuminate the relevance of journal…

Typological Asymmetries in Round Vowel Harmony: Support from Artificial Grammar Learning

ERIC Educational Resources Information Center

Finley, Sara

2012-01-01

Providing evidence for the universal tendencies of patterns in the world's languages can be difficult, as it is impossible to sample all possible languages, and linguistic samples are subject to interpretation. However, experimental techniques, such as artificial grammar learning paradigms, make it possible to uncover the psychological reality of…

Computer-Supported Collaborative Learning: Best Practices and Principles for Instructors

ERIC Educational Resources Information Center

Orvis, Kara L., Ed.; Lassiter, Andrea L. R., Ed.

2008-01-01

Decades of research have shown that student collaboration in groups doesn't just happen; rather it needs to be a deliberate process facilitated by the instructor. Promoting collaboration in virtual learning environments presents a variety of challenges. This book answers the demand for a thorough resource on techniques to facilitate effective …

Boeing--A Case Study Example of Enterprise Project Management from a Learning Organization Perspective.

ERIC Educational Resources Information Center

Szymczak, Conrad C.; Walker, Derek H. T.

2003-01-01

The evolution of the Boeing Company illustrates how to achieve an enterprise project management culture through organizational learning. Project management can be a survival technique for adapting to change as well as a proactive mechanism. An organizational culture that supports commitment and enthusiasm and a knowledge management infrastructure…

Ubiquitous and Ambient Intelligence Assisted Learning Environment Infrastructures Development--A Review

ERIC Educational Resources Information Center

Kanagarajan, Sujith; Ramakrishnan, Sivakumar

2018-01-01

Ubiquitous Learning Environment (ULE) has been becoming a mobile and sensor based technology equipped environment that suits the modern world education discipline requirements for the past few years. Ambient Intelligence (AmI) makes much smarter the ULE by the support of optimization and intelligent techniques. Various efforts have been so far…

How to facilitate freshmen learning and support their transition to a university study environment

NASA Astrophysics Data System (ADS)

Kangas, Jari; Rantanen, Elisa; Kettunen, Lauri

2017-11-01

Most freshmen enter universities with high expectations and with good motivation, but too many are driven into performing instead of true learning. The issues are not only related to the challenge of comprehending the substance, social and other factors have an impact as well. All these multifaceted needs should be accounted for to facilitate student learning. Learning is an individual process and remarkable improvement in the learning practices is possible, if proper actions are addressed early enough. We motivate and describe a study of the experience obtained from a set of tailor-made courses that were given alongside standard curriculum. The courses aimed to provide a 'safe community' to address the multifaceted needs. Such support was integrated into regular coursework where active learning techniques, e.g. interactive small groups were incorporated. To assess impact of the courses we employ the feedback obtained during the courses and longitudinal statistical data about students' success.

Methods & Strategies: Put Your Walls to Work

ERIC Educational Resources Information Center

Jackson, Julie; Durham, Annie

2016-01-01

This column provides ideas and techniques to enhance your science teaching. This month's issue discusses planning and using interactive word walls to support science and reading instruction. Many classrooms have word walls displaying vocabulary that students have learned in class. Word walls serve as visual scaffolds to support instruction. To…

Student conceptions about the DNA structure within a hierarchical organizational level: Improvement by experiment- and computer-based outreach learning.

PubMed

Langheinrich, Jessica; Bogner, Franz X

2015-01-01

As non-scientific conceptions interfere with learning processes, teachers need both, to know about them and to address them in their classrooms. For our study, based on 182 eleventh graders, we analyzed the level of conceptual understanding by implementing the "draw and write" technique during a computer-supported gene technology module. To give participants the hierarchical organizational level which they have to draw, was a specific feature of our study. We introduced two objective category systems for analyzing drawings and inscriptions. Our results indicated a long- as well as a short-term increase in the level of conceptual understanding and in the number of drawn elements and their grades concerning the DNA structure. Consequently, we regard the "draw and write" technique as a tool for a teacher to get to know students' alternative conceptions. Furthermore, our study points the modification potential of hands-on and computer-supported learning modules. © 2015 The International Union of Biochemistry and Molecular Biology.

A Re-Examination of the Argument against Problem-Based Learning in the Classroom

ERIC Educational Resources Information Center

Bryant, Lauren H.

2011-01-01

The primary purpose of this study is to examine Kirschner, Sweller, and Clark's (2006) argument against problem-based learning (PBL) by analyzing research used to support their stance. The secondary purpose is to develop a definition of PBL that helps practitioners use this technique. Seven studies were analyzed to determine whether the PBL…

Does Medical Students' Diagnostic Performance Improve by Observing Examples of Self-Explanation Provided by Peers or Experts?

ERIC Educational Resources Information Center

Chamberland, Martine; Mamede, Sílvia; St-Onge, Christina; Setrakian, Jean; Schmidt, Henk G.

2015-01-01

Educational strategies that promote the development of clinical reasoning in students remain scarce. Generating self-explanations (SE) engages students in active learning and has shown to be an effective technique to improve clinical reasoning in clerks. Example-based learning has been shown to support the development of accurate knowledge…

Cognitive Analysis of Meaning and Acquired Mental Representations as an Alternative Measurement Method Technique to Innovate E-Assessment

ERIC Educational Resources Information Center

Morales-Martinez, Guadalupe Elizabeth; Lopez-Ramirez, Ernesto Octavio; Castro-Campos, Claudia; Villarreal-Treviño, Maria Guadalupe; Gonzales-Trujillo, Claudia Jaquelina

2017-01-01

Empirical directions to innovate e-assessments and to support the theoretical development of e-learning are discussed by presenting a new learning assessment system based on cognitive technology. Specifically, this system encompassing trained neural nets that can discriminate between students who successfully integrated new knowledge course…

Ascending Bloom's Pyramid: Fostering Student Creativity and Innovation in Academic Library Spaces

ERIC Educational Resources Information Center

Bieraugel, Mark; Neill, Stern

2017-01-01

Our research examined the degree to which behaviors and learning associated with creativity and innovation were supported in five academic library spaces and three other spaces at a mid-sized university. Based on survey data from 226 students, we apply a number of statistical techniques to measure student perceptions of the types of learning and…

Administrators Supporting School Change. Strategies for Teaching and Learning Professional Library.

ERIC Educational Resources Information Center

Wortman, Robert

This publication is part of a series of monographs on the art of teaching. Each volume, focusing on a specific discipline, explores theory in the context of teaching strategies connected to evaluation of both teachers' and students' learning. Three techniques for making use of the series are offered: dialogues (as self-evaluation and in study…

Indicators of undergraduate nursing students' satisfaction with their learning journey: An integrative review.

PubMed

Walker, Sandra; Rossi, Dolene; Anastasi, Jennifer; Gray-Ganter, Gillian; Tennent, Rebeka

2016-08-01

In Australia Bachelor of Nursing programmes are delivered via both internal and distance modes yet there is little knowledge of the indicators of undergraduate nursing students' satisfaction with the learning journey. This integrative review was undertaken to uncover the indicators of undergraduate nursing students' satisfaction with their learning journey. Integrative review. A review of key papers was undertaken. Only peer-reviewed papers published in scholarly journals from 2008 onwards were included in this integrative review. Pubmed, CINAHL, Google Scholar, Cochrane, Wiley Online and ProQuest Central databases were searched for relevant papers. 49 papers were appraised, by a minimum of two team members. CASP tools were used when evaluating qualitative research, systematic and integrated reviews while survey research was evaluated using a tool specifically developed for this purpose by the research team. All tools used to assess the quality of the research studies contained comprehensive checklists and questions relevant for the particular type of study. Data related to these checklists was extracted and the research team appraised the quality of each article based on its relevance to the topic, internal and external validity, appropriateness of data analysis technique(s), and whether ethical considerations were addressed. Seventeen papers were included in the final analysis. Data analysis involved a systematic approach using content analysis techniques. This integrative review sought to identify indicators of nursing students' satisfaction with their learning journey. Authentic learning, motivation, resilience, support, and collaborative learning were identified by this integrative review as being key to nursing students' satisfaction with their learning journey. Sub themes were identified within each of these themes that assist in explaining nursing students' views of their learning journey. The findings showed that higher satisfaction levels are attained when nursing students feel included and supported during their learning journey. Copyright © 2016 Elsevier Ltd. All rights reserved.

Integration of Video-Based Demonstrations to Prepare Students for the Organic Chemistry Laboratory

ERIC Educational Resources Information Center

Nadelson, Louis S.; Scaggs, Jonathan; Sheffield, Colin; McDougal, Owen M.

2015-01-01

Consistent, high-quality introductions to organic chemistry laboratory techniques effectively and efficiently support student learning in the organic chemistry laboratory. In this work, we developed and deployed a series of instructional videos to communicate core laboratory techniques and concepts. Using a quasi-experimental design, we tested the…

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Collaborative learning in radiologic science education.

PubMed

Yates, Jennifer L

2006-01-01

Radiologic science is a complex health profession, requiring the competent use of technology as well as the ability to function as part of a team, think critically, exercise independent judgment, solve problems creatively and communicate effectively. This article presents a review of literature in support of the relevance of collaborative learning to radiologic science education. In addition, strategies for effective design, facilitation and authentic assessment of activities are provided for educators wishing to incorporate collaborative techniques into their program curriculum. The connection between the benefits of collaborative learning and necessary workplace skills, particularly in the areas of critical thinking, creative problem solving and communication skills, suggests that collaborative learning techniques may be particularly useful in the education of future radiologic technologists. This article summarizes research identifying the benefits of collaborative learning for adult education and identifying the link between these benefits and the necessary characteristics of medical imaging technologists.

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Exploring the CAESAR database using dimensionality reduction techniques

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Raymer, Michael L.

2012-06-01

The Civilian American and European Surface Anthropometry Resource (CAESAR) database containing over 40 anthropometric measurements on over 4000 humans has been extensively explored for pattern recognition and classification purposes using the raw, original data [1-4]. However, some of the anthropometric variables would be impossible to collect in an uncontrolled environment. Here, we explore the use of dimensionality reduction methods in concert with a variety of classification algorithms for gender classification using only those variables that are readily observable in an uncontrolled environment. Several dimensionality reduction techniques are employed to learn the underlining structure of the data. These techniques include linear projections such as the classical Principal Components Analysis (PCA) and non-linear (manifold learning) techniques, such as Diffusion Maps and the Isomap technique. This paper briefly describes all three techniques, and compares three different classifiers, Naïve Bayes, Adaboost, and Support Vector Machines (SVM), for gender classification in conjunction with each of these three dimensionality reduction approaches.

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

PubMed

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

Laryngeal Support Device Enhances the Learning of Laryngeal Anatomy and Voice Physiology

ERIC Educational Resources Information Center

Curcio, Daniella Franco; Behlau, Mara; Barros, Mirna Duarte; Smith, Ricardo Luiz

2012-01-01

Multidisciplinary cooperation in health care requires a solid knowledge in the basic sciences for a common ground of communication. In speech pathology, these fundamentals improve the accuracy of descriptive diagnoses and support the development of new therapeutic techniques and strategies. The aim of this study is to briefly discuss the benefits…

The Role of the Microcomputer-Based Laboratory Display in Supporting the Construction of New Understandings in Kinematics.

ERIC Educational Resources Information Center

Russell, David W.; Lucas, Keith B.; McRobbie, Campbell J.

2003-01-01

Investigates how microcomputer-based laboratory (MBL) activities specifically designed to be consistent with a constructivist theory of learning support or constrain student construction of understanding. Analysis of students' discourse and actions reveal that students invented numerous techniques for manipulating data in the service of their…

Enhancing User Support in Open Problem Solving Environments through Bayesian Network Inference Techniques

ERIC Educational Resources Information Center

Tselios, Nikolaos; Stoica, Adrian; Maragoudakis, Manolis; Avouris, Nikolaos; Komis, Vassilis

2006-01-01

During the last years, development of open learning environments that support effectively their users has been a challenge for the research community of educational technologies. The open interactive nature of these environments results in users experiencing difficulties in coping with the plethora of available functions, especially during their…

Towards a Compositional SPIN

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Giannakopoulou, Dimitra

2006-01-01

This paper discusses our initial experience with introducing automated assume-guarantee verification based on learning in the SPIN tool. We believe that compositional verification techniques such as assume-guarantee reasoning could complement the state-reduction techniques that SPIN already supports, thus increasing the size of systems that SPIN can handle. We present a "light-weight" approach to evaluating the benefits of learning-based assume-guarantee reasoning in the context of SPIN: we turn our previous implementation of learning for the LTSA tool into a main program that externally invokes SPIN to provide the model checking-related answers. Despite its performance overheads (which mandate a future implementation within SPIN itself), this approach provides accurate information about the savings in memory. We have experimented with several versions of learning-based assume guarantee reasoning, including a novel heuristic introduced here for generating component assumptions when their environment is unavailable. We illustrate the benefits of learning-based assume-guarantee reasoning in SPIN through the example of a resource arbiter for a spacecraft. Keywords: assume-guarantee reasoning, model checking, learning.

Collaborative and Multilingual Approach to Learn Database Topics Using Concept Maps

PubMed Central

Calvo, Iñaki

2014-01-01

Authors report on a study using the concept mapping technique in computer engineering education for learning theoretical introductory database topics. In addition, the learning of multilingual technical terminology by means of the collaborative drawing of a concept map is also pursued in this experiment. The main characteristics of a study carried out in the database subject at the University of the Basque Country during the 2011/2012 course are described. This study contributes to the field of concept mapping as these kinds of cognitive tools have proved to be valid to support learning in computer engineering education. It contributes to the field of computer engineering education, providing a technique that can be incorporated with several educational purposes within the discipline. Results reveal the potential that a collaborative concept map editor offers to fulfil the above mentioned objectives. PMID:25538957

Student Conceptions about the DNA Structure within a Hierarchical Organizational Level: Improvement by Experiment- and Computer-Based Outreach Learning

ERIC Educational Resources Information Center

Langheinrich, Jessica; Bogner, Franz X.

2015-01-01

As non-scientific conceptions interfere with learning processes, teachers need both, to know about them and to address them in their classrooms. For our study, based on 182 eleventh graders, we analyzed the level of conceptual understanding by implementing the "draw and write" technique during a computer-supported gene technology module.…

Video Self-Modeling Technique That Can Be Used in Improving the Abilities of Fluent Reading and Fluent Speaking

ERIC Educational Resources Information Center

Sen, Ülker

2016-01-01

The use of technology in the field of education makes the educational process more efficient and motivating. Technological tools are used for developing the communication skills of students and teachers in the learning process increasing the participation, supporting the peer, the realization of collaborative learning. The use of technology is…

Using Wikis to Enhance Website Peer Evaluation in an Online Website Development Course: An Exploratory Study

ERIC Educational Resources Information Center

He, Wu

2011-01-01

Peer evaluations are often used to improve learning in educational settings. As more and more online courses are offered, it is becoming increasingly important to explore new techniques for conducting peer evaluation in online courses. In recent years, wikis have increasingly been used in higher education to support learning and group work.…

The Value of Learning Talk: Applying a Novel Dialogue Scoring Method to Inform Interaction Design in an Open-Ended, Embodied Museum Exhibit

ERIC Educational Resources Information Center

Roberts, Jessica; Lyons, Leilah

2017-01-01

Museum researchers have long acknowledged the importance of dialogue in informal learning, particularly for open-ended exploratory exhibits. Novel interaction techniques like full-body interaction are appealing for these exploratory exhibits, but designers have not had a metric for determining how their designs are supporting productive learning…

Discussion of the enabling environments for decentralised water systems.

PubMed

Moglia, M; Alexander, K S; Sharma, A

2011-01-01

Decentralised water supply systems are becoming increasingly affordable and commonplace in Australia and have the potential to alleviate urban water shortages and reduce pollution into natural receiving marine and freshwater streams. Learning processes are necessary to support the efficient implementation of decentralised systems. These processes reveal the complex socio-technical and institutional factors to be considered when developing an enabling environment supporting decentralised water and wastewater servicing solutions. Critical to the technological transition towards established decentralised systems is the ability to create strategic and adaptive capacity to promote learning and dialogue. Learning processes require institutional mechanisms to ensure the lessons are incorporated into the formulation of policy and regulation, through constructive involvement of key government institutions. Engagement of stakeholders is essential to the enabling environment. Collaborative learning environments using systems analysis with communities (social learning) and adaptive management techniques are useful in refining and applying scientists' and managers' knowledge (knowledge management).

Make Program Failures Work for You.

ERIC Educational Resources Information Center

Keller, M. Jean; Mills, Helen H.

1984-01-01

Recreation program planners can learn from program failures. Failures should not be viewed as negative statements about personnel. Examining feelings in a supportive staff environment is suggested as a technique for developing competence. (DF)

Integrating Testing into Software Engineering Courses Supported by a Collaborative Learning Environment

ERIC Educational Resources Information Center

Clarke, Peter J.; Davis, Debra; King, Tariq M.; Pava, Jairo; Jones, Edward L.

2014-01-01

As software becomes more ubiquitous and complex, the cost of software bugs continues to grow at a staggering rate. To remedy this situation, there needs to be major improvement in the knowledge and application of software validation techniques. Although there are several software validation techniques, software testing continues to be one of the…

MO-DE-202-00: Image-Guided Interventions: Advances in Intraoperative Imaging, Guidance, and An Emerging Role for Medical Physics in Surgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guidedmore » neurosurgery” Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: Understand the principles and applications of HIFU in surgical ablation. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants.« less

MO-DE-202-03: Image-Guided Surgery and Interventions in the Advanced Multimodality Image-Guided Operating (AMIGO) Suite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kapur, T.

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guidedmore » neurosurgery” Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: Understand the principles and applications of HIFU in surgical ablation. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants.« less

MO-DE-202-01: Image-Guided Focused Ultrasound Surgery and Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farahani, K.

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guidedmore » neurosurgery” Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: Understand the principles and applications of HIFU in surgical ablation. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants.« less

MO-DE-202-04: Multimodality Image-Guided Surgery and Intervention: For the Rest of Us

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shekhar, R.

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guidedmore » neurosurgery” Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: Understand the principles and applications of HIFU in surgical ablation. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants.« less

MO-DE-202-02: Advances in Image Registration and Reconstruction for Image-Guided Neurosurgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Siewerdsen, J.

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guidedmore » neurosurgery” Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: Understand the principles and applications of HIFU in surgical ablation. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants.« less

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Impact of Self-Explanation and Analogical Comparison Support on Learning Processes, Motivation, Metacognition, and Transfer

NASA Astrophysics Data System (ADS)

Richey, J. Elizabeth

Research examining analogical comparison and self-explanation has produced a robust set of findings about learning and transfer supported by each instructional technique. However, it is unclear how the types of knowledge generated through each technique differ, which has important implications for cognitive theory as well as instructional practice. I conducted a pair of experiments to directly compare the effects of instructional prompts supporting self-explanation, analogical comparison, and the study of instructional explanations across a number of fine-grained learning process, motivation, metacognition, and transfer measures. Experiment 1 explored these questions using sequence extrapolation problems, and results showed no differences between self-explanation and analogical comparison support conditions on any measure. Experiment 2 explored the same questions in a science domain. I evaluated condition effects on transfer outcomes; self-reported self-explanation, analogical comparison, and metacognitive processes; and achievement goals. I also examined relations between transfer and self-reported processes and goals. Receiving materials with analogical comparison support and reporting greater levels of analogical comparison were both associated with worse transfer performance, while reporting greater levels of self-explanation was associated with better performance. Learners' self-reports of self-explanation and analogical comparison were not related to condition assignment, suggesting that the questionnaires did not measure the same processes promoted by the intervention, or that individual differences in processing are robust even when learners are instructed to engage in self-explanation or analogical comparison.

Application of Metamorphic Testing to Supervised Classifiers

PubMed Central

Xie, Xiaoyuan; Ho, Joshua; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2010-01-01

Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no “test oracle” to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called “metamorphic testing”, which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well. PMID:21243103

Involving Users to Improve the Collaborative Logical Framework

PubMed Central

2014-01-01

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors' workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants. PMID:24592196

Guiding the creation of knowledge and understanding in a virtual learning environment.

PubMed

Littleton, Karen; Whitelock, Denise

2004-04-01

This article reports findings from an in-depth case study investigating processes of teaching and learning within one tutorial group studying an e-learning course presented as part of the Open University's MA in Open and Distance Education. Drawing on contemporary sociocultural theory and research, the instructional techniques used by the tutor-moderator to guide the creation of "common knowledge" and the construction of understanding are explored. The significance of tutor contributions for fostering a supportive culture of enquiry is also discussed.

Effectiveness of Podcasts Delivered on Mobile Devices as a Support for Student Learning during General Chemistry Laboratories

ERIC Educational Resources Information Center

Powell, Cynthia B.; Mason, Diana S.

2013-01-01

Chemistry instructors in teaching laboratories provide expert modeling of techniques and cognitive processes and provide assistance to enrolled students that may be described as scaffolding interaction. Such student support is particularly essential in laboratories taught with an inquiry-based curriculum. In a teaching laboratory with a high…

TRAC Innovative Visualization Techniques

DTIC Science & Technology

2016-11-14

Therefore, TRAC analysts need a way to analyze the effectiveness of their visualization design choices. Currently, TRAC does not have a methodology ...to analyze visualizations used to support an analysis story. Our research team developed a visualization design methodology to create effective...visualizations that support an analysis story. First, we based our methodology on the latest research on design thinking, cognitive learning, and

Machine Learning for Biological Trajectory Classification Applications

NASA Technical Reports Server (NTRS)

Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

2002-01-01

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Developing a Teaching Model Using an Online Collaboration Approach for a Digital Technique Practical Work

ERIC Educational Resources Information Center

Muchlas

2015-01-01

This research is aimed to produce a teaching model and its supporting instruments using a collaboration approach for a digital technique practical work attended by higher education students. The model is found to be flexible and relatively low cost. Through this research, feasibility and learning impact of the model will be determined. The model…

Techniques to Collect and Analyze the Cognitive Map Knowledge of Persons with Visual Impairment or Blindness: Issues of Validity.

ERIC Educational Resources Information Center

Kitchin, R. M.; Jacobson, R. D.

1997-01-01

Assesses techniques used by researchers to collect and analyze data on how people with visual impairments or blindness learn, understand, and think about geographic space. Recommendations are made for increasing the validity of studies, including the use of multiple, mutually supportive tests; larger samples; and real-world environments.…

In search for instructional techniques to maximize the use of germane cognitive resources: A case of teaching complex tasks in physics

NASA Astrophysics Data System (ADS)

Sliva, Yekaterina

The purpose of this study was to introduce an instructional technique for teaching complex tasks in physics, test its effectiveness and efficiency, and understand cognitive processes taking place in learners' minds while they are exposed to this technique. The study was based primarily on cognitive load theory (CLT). CLT determines the amount of total cognitive load imposed on a learner by a learning task as combined intrinsic (invested in comprehending task complexity) and extraneous (wasteful) cognitive load. Working memory resources associated with intrinsic cognitive load are defined as germane resources caused by element interactivity that lead to learning, in contrast to extraneous working memory resources that are devoted to dealing with extraneous cognitive load. However, the amount of learner's working memory resources actually devoted to a task depends on how well the learner is engaged in the learning environment. Since total cognitive load has to stay within limits of working memory capacity, both extraneous and intrinsic cognitive load need to be reduced. In order for effective learning to occur, the use of germane cognitive resources should be maximized. In this study, the use of germane resources was maximized for two experimental groups by providing a learning environment that combined problem-solving procedure with prompts to self-explain with and without completion problems. The study tested three hypotheses and answered two research questions. The first hypothesis predicting that experimental treatments would reduce total cognitive load was not supported. The second hypothesis predicting that experimental treatments would increase performance was supported for the self-explanation group only. The third hypothesis that tested efficiency measure as adopted from Paas and van Merrienboer (1993) was not supported. As for the research question of whether the quality of self-explanations would change with time for the two experimental conditions, it was determined that time had a positive effect on such quality. The research question that investigated learners' attitudes towards the instructions revealed that experimental groups understood the main idea behind the suggested technique and positively reacted to it. The results of the study support the conclusions that (a) prompting learners to self-explain while independently solving problems can increase performance, especially on far transfer questions; (b) better performance is achieved in combination with increased mental effort; (c) self-explanations do not increase time on task; and (d) quality of self-explanations can be improved with time. Results based on the analyses of learners' attitudes further support that learners in the experimental groups understood the main idea behind the suggested techniques and positively reacted to them. The study also raised concern about application of efficiency formula for instructional conditions that increase both performance and mental effort in CLT. As a result, an alternative model was suggested to explain the relationship between performance and mental effort based on Yerkes-Dodson law (1908). Keywords: instructional design, cognitive load, complex tasks, problem-solving, self-explanation.

Basic Strategies of Dynamic Supportive Therapy

PubMed Central

Misch, Donald A.

2000-01-01

Supportive therapy is the psychotherapeutic approach employed with the majority of mentally ill individuals. Nevertheless, most mental health professional training programs dedicate little time and effort to the teaching and learning of supportive therapy, and many mental health professionals are unable to clearly and concisely articulate the nature or process of supportive work. Although supportive therapy incorporates many specific techniques from a wide variety of psychotherapy schools, it can be conceptualized as consisting of a more limited number of underlying strategies. The fundamental strategies that underpin effective supportive therapy with mentally ill individuals are described. PMID:11069130

Predicting Flavonoid UGT Regioselectivity

PubMed Central

Jackson, Rhydon; Knisley, Debra; McIntosh, Cecilia; Pfeiffer, Phillip

2011-01-01

Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities. PMID:21747849

Machine learning models in breast cancer survival prediction.

PubMed

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Cooperative m-learning with nurse practitioner students.

PubMed

Wyatt, Tami H; Krauskopf, Patricia B; Gaylord, Nan M; Ward, Andrew; Huffstutler-Hawkins, Shelley; Goodwin, Linda

2010-01-01

New technologies give nurse academicians the opportunity to incorporate innovative teaching-learning strategies into the nursing curricula. Mobile technology for learning, or m-learning, has considerable potential for the nursing classroom but lacks sufficient empirical evidence to support its use. Based on Mayer's multimedia learning theory, the effect of using cooperative and interactive m-learning techniques in enhancing classroom and clinical learning was explored. The relationship between m-learning and students' learning styles was determined through a multimethod educational research study involving nurse practitioner students at two mid-Atlantic universities. During the 16-month period, nurse practitioner students and their faculty used personal digital assistants (PDAs) to participate in various m-learning activities. Findings from focus group and survey responses concluded that PDAs, specifically the Pocket PC, are useful reference tools in the clinical setting and that all students, regardless of learning style, benefited from using PDAs. It was also demonstrated that connecting students with classmates and other nurse practitioner students at distant universities created a cooperative learning community providing additional support and knowledge acquisition. The authors concluded that in order to successfully prepare nurse practitioner graduates with the skills necessary to function in the present and future health care system, nurse practitioner faculty must be creative and innovative, incorporating various revolutionary technologies into their nurse practitioner curricula.

Overcoming Hurdles Implementing Multi-skilling Policies

DTIC Science & Technology

2015-03-26

skilled workforce? Chapter II will communicate important concepts found in the literature on skill proficiency topics. These topics include skill...training methods that might improve learning and retention during the acquisition phase. 10 The active interlock modeling (AIM) protocol is a dyadic ...retention, as found in 43 Chapter 2. These techniques include dyadic training methods, overlearning, feedback, peer support, and managerial support

Technology-Mediated Engagement with Nature: Sensory and Social Engagement with the Outdoors Supported through an e-Trailguide

ERIC Educational Resources Information Center

McClain, Lucy R.; Zimmerman, Heather Toomey

2016-01-01

This study describes the implementation of a self-guiding mobile learning tool designed to support youths' engagement with the natural world as they explored the flora and fauna along one nature trail at an environmental center. Using qualitative video-based data collection and analysis techniques, we conducted two design-based research study…

Lions (Panthera leo) solve, learn, and remember a novel resource acquisition problem.

PubMed

Borrego, Natalia; Dowling, Brian

2016-09-01

The social intelligence hypothesis proposes that the challenges of complex social life bolster the evolution of intelligence, and accordingly, advanced cognition has convergently evolved in several social lineages. Lions (Panthera leo) offer an ideal model system for cognitive research in a highly social species with an egalitarian social structure. We investigated cognition in lions using a novel resource task: the suspended puzzle box. The task required lions (n = 12) to solve a novel problem, learn the techniques used to solve the problem, and remember techniques for use in future trials. The majority of lions demonstrated novel problem-solving and learning; lions (11/12) solved the task, repeated success in multiple trials, and significantly reduced the latency to success across trials. Lions also demonstrated cognitive abilities associated with memory and solved the task after up to a 7-month testing interval. We also observed limited evidence for social facilitation of the task solution. Four of five initially unsuccessful lions achieved success after being partnered with a successful lion. Overall, our results support the presence of cognition associated with novel problem-solving, learning, and memory in lions. To date, our study is only the second experimental investigation of cognition in lions and further supports expanding cognitive research to lions.

Twelve tips to promote successful development of a learner performance dashboard within a medical education program.

PubMed

Boscardin, Christy; Fergus, Kirkpatrick B; Hellevig, Bonnie; Hauer, Karen E

2017-11-09

Easily accessible and interpretable performance data constitute critical feedback for learners that facilitate informed self-assessment and learning planning. To provide this feedback, there has been a proliferation of educational dashboards in recent years. An educational (learner) dashboard systematically delivers timely and continuous feedback on performance and can provide easily visualized and interpreted performance data. In this paper, we provide practical tips for developing a functional, user-friendly individual learner performance dashboard and literature review of dashboard development, assessment theory, and users' perspectives. Considering key design principles and maximizing current technological advances in data visualization techniques can increase dashboard utility and enhance the user experience. By bridging current technology with assessment strategies that support learning, educators can continue to improve the field of learning analytics and design of information management tools such as dashboards in support of improved learning outcomes.

JUST in time health emergency interventions: an innovative approach to training the citizen for emergency situations using virtual reality techniques and advanced IT tools (the VR Tool).

PubMed

Manganas, A; Tsiknakis, M; Leisch, E; Ponder, M; Molet, T; Herbelin, B; Magnetat-Thalmann, N; Thalmann, D; Fato, M; Schenone, A

2004-01-01

This paper reports the results of the second of the two systems developed by JUST, a collaborative project supported by the European Union under the Information Society Technologies (IST) Programme. The most innovative content of the project has been the design and development of a complementary training course for non-professional health emergency operators, which supports the traditional learning phase, and which purports to improve the retention capability of the trainees. This was achieved with the use of advanced information technology techniques, which provide adequate support and can help to overcome the present weaknesses of the existing training mechanisms.

Patients' views of receiving lessons in the Alexander technique and an exercise prescription for managing back pain in the ATEAM trial.

PubMed

Yardley, Lucy; Dennison, Laura; Coker, Rebecca; Webley, Frances; Middleton, Karen; Barnett, Jane; Beattie, Angela; Evans, Maggie; Smith, Peter; Little, Paul

2010-04-01

Lessons in the Alexander Technique and exercise prescription proved effective for managing low back pain in primary care in a clinical trial. To understand trial participants' expectations and experiences of the Alexander Technique and exercise prescription. A questionnaire assessing attitudes to the intervention, based on the Theory of Planned Behaviour, was completed at baseline and 3-month follow-up by 183 people assigned to lessons in the Alexander Technique and 176 people assigned to exercise prescription. Semi-structured interviews to assess the beliefs contributing to attitudes to the intervention were carried out at baseline with14 people assigned to the lessons in the Alexander Technique and 16 to exercise prescription, and at follow-up with 15 members of the baseline sample. Questionnaire responses indicated that attitudes to both interventions were positive at baseline but became more positive at follow-up only in those assigned to lessons in the Alexander Technique. Thematic analysis of the interviews suggested that at follow-up many patients who had learned the Alexander Technique felt they could manage back pain better. Whereas many obstacles to exercising were reported, few barriers to learning the Alexander Technique were described, since it 'made sense', could be practiced while carrying out everyday activities or relaxing, and the teachers provided personal advice and support. Using the Alexander Technique was viewed as effective by most patients. Acceptability may have been superior to exercise because of a convincing rationale and social support and a better perceived fit with the patient's particular symptoms and lifestyle.

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

DOE PAGES

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

2016-08-09

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Bridging the gap between self-directed learning of nurse educators and effective student support.

PubMed

Van Rensburg, Gisela H; Botma, Yvonne

2015-11-26

Self-directed learning requires the ability to identify one's own learning needs, develop and implement a plan to gain knowledge and to monitor one's own progress. A lifelong learning approach cannot be forced, since it is in essence an internally driven process. Nurse educators can, however, act as role models to empower their students to become independent learners by modelling their own self-directed learning and applying a number of techniques in supporting their students in becoming ready for self-directed learning.  The aim of the article is to describe the manifestations and implications of the gap between self-directed learning readiness of nurse educators and educational trends in supporting students.  An instrumental case study design was used to gain insight into the manifestations and implications of self-directed learning of nurse educators. Based on the authentic foci of various critical incidents and literature, data were collected and constructed into a fictitious case. The authors then deductively analysed the case by using the literature on self-directed learning readiness as departure point. Four constructs of self-directed learning were identified, namely internal motivation, planning and implementation, self-monitoring and interpersonal communication. Supportive strategies were identified from the available literature.  Nine responses by nurse educators based on the fictitious case were analysed.Analysis showed that readiness for self-directed learning in terms of the identified constructswas interrelated and not mutually exclusive of one other.  The success of lifelong learning is the ability to engage in self-directed learning which requires openness to learning opportunities, good self-concept, taking initiative and illustrating independence in learning. Conscientiousness, an informed acceptance of a responsibility for one's own learning and creativity, is vital to one's future orientation towards goal-directed learning. Knowledge and understanding of one's own and students' selfdirected learning abilities are critical for nurse educators. In the nursing profession, it has been shown that self-directed learning by the nurse educators has a direct relationship towards the development of a lifelong learning approach by their students. Supporting students towards becoming self-directed learners throughout their professional life, in turn, will impact directly on the quality of nursing and midwifery practice. (Article to follow).

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Improve Student Understanding Ability Through Gamification in Instructional Media Based Explicit Instruction

NASA Astrophysics Data System (ADS)

Firdausi, N.; Prabawa, H. W.; Sutarno, H.

2017-02-01

In an effort to maximize a student’s academic growth, one of the tools available to educators is the explicit instruction. Explicit instruction is marked by a series of support or scaffold, where the students will be guided through the learning process with a clear statement of purpose and a reason for learning new skills, a clear explanation and demonstration of learning targets, supported and practiced with independent feedback until mastery has been achieved. The technology development trend of todays, requires an adjustment in the development of learning object that supports the achievement of explicit instruction targets. This is where the gamification position is. In the role as a pedagogical strategy, the use of gamification preformance study class is still relatively new. Gamification not only use the game elements and game design techniques in non-game contexts, but also to empower and engage learners with the ability of motivation on learning approach and maintains a relaxed atmosphere. With using Reseach and Development methods, this paper presents the integration of technology (which in this case using the concept of gamification) in explicit instruction settings and the impact on the improvement of students’ understanding.

Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation

NASA Technical Reports Server (NTRS)

Margaria, Tiziana (Inventor); Hinchey, Michael G. (Inventor); Rouff, Christopher A. (Inventor); Rash, James L. (Inventor); Steffen, Bernard (Inventor)

2010-01-01

Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.

Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

NASA Astrophysics Data System (ADS)

Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

2016-07-01

Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification technique to the Global Ionospheric Map (GIM) TEC data which is provided by the NASA Jet Propulsion Laboratory (JPL), it will be shown that SVM can be a suitable learning method to detect the anomalies in Total Electron Content (TEC) variations. This study is supported by TUBITAK 114E541 project as a part of the Scientific and Technological Research Projects Funding Program (1001).

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

PubMed

Fraccaro, Paolo; Nicolo, Massimo; Bonetto, Monica; Giacomini, Mauro; Weller, Peter; Traverso, Carlo Enrico; Prosperi, Mattia; OSullivan, Dympna

2015-01-27

To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

Mapping as a learning strategy in health professions education: a critical analysis.

PubMed

Pudelko, Beatrice; Young, Meredith; Vincent-Lamarre, Philippe; Charlin, Bernard

2012-12-01

Mapping is a means of representing knowledge in a visual network and is becoming more commonly used as a learning strategy in medical education. The assumption driving the development and use of concept mapping is that it supports and furthers meaningful learning. The goal of this paper was to examine the effectiveness of concept mapping as a learning strategy in health professions education. The authors conducted a critical analysis of recent literature on the use of concept mapping as a learning strategy in the area of health professions education. Among the 65 studies identified, 63% were classified as empirical work, the majority (76%) of which used pre-experimental designs. Only 24% of empirical studies assessed the impact of mapping on meaningful learning. Results of the analysis do not support the hypothesis that mapping per se furthers and supports meaningful learning, memorisation or factual recall. When documented improvements in learning were found, they often occurred when mapping was used in concert with other strategies, such as collaborative learning or instructor modelling, scaffolding and feedback. Current empirical research on mapping as a learning strategy presents methodological shortcomings that limit its internal and external validity. The results of our analysis indicate that mapping strategies that make use of feedback and scaffolding have beneficial effects on learning. Accordingly, we see a need to expand the process of reflection on the characteristics of representational guidance as it is provided by mapping techniques and tools based on field of knowledge, instructional objectives, and the characteristics of learners in health professions education. © Blackwell Publishing Ltd 2012.

Quality in Web-Supported Learning.

ERIC Educational Resources Information Center

Fresen, Jill

2002-01-01

Discusses quality assurance for Web-based courses, based on experiences at the University of Pretoria. Topics include evaluation of courseware; the concept of quality, including quality control, quality assurance, and total quality management; implementing a quality management system; measurement techniques; and partnerships. (LRW)

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

PubMed

Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

2017-12-19

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.

SHERLOCK: A Coached Practice Environment for an Electronics Troubleshooting Job

DTIC Science & Technology

1988-03-01

context. At the most abstract level, it Is the cognitive version of earlier approaches to errorless learning (Terrace, 1964). With support from a...not yet learned and Is the basis for Interactions with the student . Sherlock does not use simulation techniques to model student pedormnce. Its...annotation of how well the student is expected to do at each point of the abstracted problem space. The object (microprogram) corresponding to each node

Student perceptions about learning anatomy

NASA Astrophysics Data System (ADS)

Notebaert, Andrew John

This research study was conducted to examine student perceptions about learning anatomy and to explore how these perceptions shape the learning experience. This study utilized a mixed-methods design in order to better understand how students approach learning anatomy. Two sets of data were collected at two time periods; one at the beginning and one at the end of the academic semester. Data consisted of results from a survey instrument that contained open-ended questions and a questionnaire and individual student interviews. The questionnaire scored students on a surface approach to learning (relying on rote memorization and knowing factual information) scale and a deep approach to learning (understanding concepts and deeper meaning behind the material) scale. Students were asked to volunteer from four different anatomy classes; two entry-level undergraduate courses from two different departments, an upper-level undergraduate course, and a graduate level course. Results indicate that students perceive that they will learn anatomy through memorization regardless of the level of class being taken. This is generally supported by the learning environment and thus students leave the classroom believing that anatomy is about memorizing structures and remembering anatomical terminology. When comparing this class experience to other academic classes, many students believed that anatomy was more reliant on memorization techniques for learning although many indicated that memorization is their primary learning method for most courses. Results from the questionnaire indicate that most students had decreases in both their deep approach and surface approach scores with the exception of students that had no previous anatomy experience. These students had an average increase in surface approach and so relied more on memorization and repetition for learning. The implication of these results is that the learning environment may actually amplify students' perceptions of the anatomy course at all levels and experiences of enrolled students. Instructors wanting to foster deeper approaches to learning may need to apply instructional techniques that both support deeper approaches to learning and strive to change students' perceptions away from believing that anatomy is strictly memorization and thus utilizing surface approaches to learning.

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

PubMed

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The PEL technique is capable of addressing imbalanced sample learning associated with acute appendicitis diagnosis. Our evaluation results suggest PEL is less biased toward a positive or negative class than the investigated benchmark techniques. In addition, our results indicate the overall effectiveness of the proposed technique, compared with prevalent scoring systems or salient classification techniques that follow the resampling approach. Copyright © 2013 Elsevier B.V. All rights reserved.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Observational learning from tool using models by human-reared and mother-reared capuchin monkeys (Cebus apella).

PubMed

Fredman, Tamar; Whiten, Andrew

2008-04-01

Studies of wild capuchins suggest an important role for social learning, but experiments with captive subjects have generally not supported this. Here we report social learning in two quite different populations of capuchin monkeys (Cebus apella). In experiment 1, human-raised monkeys observed a familiar human model open a foraging box using a tool in one of two alternative ways: levering versus poking. In experiment 2, mother-raised monkeys viewed similar techniques demonstrated by monkey models. A control group in each population saw no model. In both experiments, independent coders detected which technique experimental subjects had seen, thus confirming social learning. Further analyses examined fidelity of copying at three levels of resolution. The human-raised monkeys exhibited fidelity at the highest level, the specific tool use technique witnessed. The lever technique was seen only in monkeys exposed to a levering model, by contrast with controls and those witnessing poke. Mother-reared monkeys instead typically ignored the tool and exhibited fidelity at a lower level, tending only to re-create whichever result the model had achieved by either levering or poking. Nevertheless this level of social learning was associated with significantly greater levels of success in monkeys witnessing a model than in controls, an effect absent in the human-reared population. Results in both populations are consistent with a process of canalization of the repertoire in the direction of the approach witnessed, producing a narrower, socially shaped behavioural profile than among controls who saw no model.

Classification of older adults with/without a fall history using machine learning methods.

PubMed

Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

2015-01-01

Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

Building Interactivity in Higher Education to Support Student Engagement in Spatial Problem Solving and Programming

NASA Astrophysics Data System (ADS)

Gulland, E.-K.; Veenendaal, B.; Schut, A. G. T.

2012-07-01

Problem-solving knowledge and skills are an important attribute of spatial sciences graduates. The challenge of higher education is to build a teaching and learning environment that enables students to acquire these skills in relevant and authentic applications. This study investigates the effectiveness of traditional face-to-face teaching and online learning technologies in supporting the student learning of problem-solving and computer programming skills, techniques and solutions. The student cohort considered for this study involves students in the surveying as well as geographic information science (GISc) disciplines. Also, students studying across a range of learning modes including on-campus, distance and blended, are considered in this study. Student feedback and past studies reveal a lack of student interest and engagement in problem solving and computer programming. Many students do not see such skills as directly relevant and applicable to their perceptions of what future spatial careers hold. A range of teaching and learning methods for both face-to-face teaching and distance learning were introduced to address some of the perceived weaknesses of the learning environment. These included initiating greater student interaction in lectures, modifying assessments to provide greater feedback and student accountability, and the provision of more interactive and engaging online learning resources. The paper presents and evaluates the teaching methods used to support the student learning environment. Responses of students in relation to their learning experiences were collected via two anonymous, online surveys and these results were analysed with respect to student pass and retention rates. The study found a clear distinction between expectations and engagement of surveying students in comparison to GISc students. A further outcome revealed that students who were already engaged in their learning benefited the most from the interactive learning resources and opportunities provided.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

Blended learning in situated contexts: 3-year evaluation of an online peer review project.

PubMed

Bridges, S; Chang, J W W; Chu, C H; Gardner, K

2014-08-01

Situated and sociocultural perspectives on learning indicate that the design of complex tasks supported by educational technologies holds potential for dental education in moving novices towards closer approximation of the clinical outcomes of their expert mentors. A cross-faculty-, student-centred, web-based project in operative dentistry was established within the Universitas 21 (U21) network of higher education institutions to support university goals for internationalisation in clinical learning by enabling distributed interactions across sites and institutions. This paper aims to present evaluation of one dental faculty's project experience of curriculum redesign for deeper student learning. A mixed-method case study approach was utilised. Three cohorts of second-year students from a 5-year bachelor of dental surgery (BDS) programme were invited to participate in annual surveys and focus group interviews on project completion. Survey data were analysed for differences between years using multivariate logistical regression analysis. Thematic analysis of questionnaire open responses and interview transcripts was conducted. Multivariate logistic regression analysis noted significant differences across items over time indicating learning improvements, attainment of university aims and the positive influence of redesign. Students perceived the enquiry-based project as stimulating and motivating, and building confidence in operative techniques. Institutional goals for greater understanding of others and lifelong learning showed improvement over time. Despite positive scores, students indicated global citizenship and intercultural understanding were conceptually challenging. Establishment of online student learning communities through a blended approach to learning stimulated motivation and intellectual engagement, thereby supporting a situated approach to cognition. Sociocultural perspectives indicate that novice-expert interactions supported student development of professional identities. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Feedforward interview technique in obstetrics and gynaecology residents: a fact or fallacy.

PubMed

Sami, Shehla; Ahmad, Amina

2015-01-01

To determine the role of Feedforward Interview (FFI) technique in motivating residents of Obstetrics and Gynaecology for better learning and performance. An explorative study with mixed method approach being employed. Department of Obstetrics and Gynaecology, Sandeman (Provincial) Hospital, Quetta, from November 2010 till May 2013. Feedforward interview technique was complimented by survey questionnaire employing similar philosophy of FFI to triangulate data through two methods. Survey questionnaire was filled-up by 21 residents and analysed by SPSS version 17. Fourteen of these participants were identified for in-depth Feedforward Interviews (FFI), based on nonprobability purposive sampling after informed consent, and content analysis was done. Feedforward interview technique enabled majority of residents in recalling minimum of 3 positive experiences, mainly related to surgical experiences, which enhanced their motivation to aspire for further improvement in this area. Hard work was the main personal contributing factor both in FFI and survey. In addition to identifying clinical experiences enhancing desire to learn, residents also reported need for more academic support as an important factor which could also boost motivation to attain better performance. Feedforward interview technique not only helps residents in recalling positive learning experiences during their training but it also has a significant influence on developing insight about one's performance and motivating residents to achieve higher academic goals.

Machine learning techniques applied to the determination of road suitability for the transportation of dangerous substances.

PubMed

Matías, J M; Taboada, J; Ordóñez, C; Nieto, P G

2007-08-17

This article describes a methodology to model the degree of remedial action required to make short stretches of a roadway suitable for dangerous goods transport (DGT), particularly pollutant substances, using different variables associated with the characteristics of each segment. Thirty-one factors determining the impact of an accident on a particular stretch of road were identified and subdivided into two major groups: accident probability factors and accident severity factors. Given the number of factors determining the state of a particular road segment, the only viable statistical methods for implementing the model were machine learning techniques, such as multilayer perceptron networks (MLPs), classification trees (CARTs) and support vector machines (SVMs). The results produced by these techniques on a test sample were more favourable than those produced by traditional discriminant analysis, irrespective of whether dimensionality reduction techniques were applied. The best results were obtained using SVMs specifically adapted to ordinal data. This technique takes advantage of the ordinal information contained in the data without penalising the computational load. Furthermore, the technique permits the estimation of the utility function that is latent in expert knowledge.

Factors associated with the effectiveness of continuing education in long-term care.

PubMed

Stolee, Paul; Esbaugh, Jacquelin; Aylward, Sandra; Cathers, Tamzin; Harvey, David P; Hillier, Loretta M; Keat, Nancy; Feightner, John W

2005-06-01

This article examines factors within the long-term-care work environment that impact the effectiveness of continuing education. In Study 1, focus group interviews were conducted with staff and management from urban and rural long-term-care facilities in southwestern Ontario to identify their perceptions of the workplace factors that affect transfer of learning into practice. Thirty-five people were interviewed across six focus groups. In Study 2, a Delphi technique was used to refine our list of factors. Consensus was achieved in two survey rounds involving 30 and 27 participants, respectively. Management support was identified as the most important factor impacting the effectiveness of continuing education. Other factors included resources (staff, funding, space) and the need for ongoing expert support. Organizational support is necessary for continuing education programs to be effective and ongoing expert support is needed to enable and reinforce learning.

Machine Learning Intermolecular Potentials for 1,3,5-Triamino-2,4,6-trinitrobenzene (TATB) Using Symmetry-Adapted Perturbation Theory

DTIC Science & Technology

2018-04-25

unlimited. NOTICES Disclaimers The findings in this report are not to be construed as an official Department of the Army position unless so...this report, intermolecular potentials for 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) are developed using machine learning techniques. Three...potentials based on support vector regression, kernel ridge regression, and a neural network are fit using symmetry-adapted perturbation theory. The

CALM: Complex Adaptive System (CAS)-Based Decision Support for Enabling Organizational Change

NASA Astrophysics Data System (ADS)

Adler, Richard M.; Koehn, David J.

Guiding organizations through transformational changes such as restructuring or adopting new technologies is a daunting task. Such changes generate workforce uncertainty, fear, and resistance, reducing morale, focus and performance. Conventional project management techniques fail to mitigate these disruptive effects, because social and individual changes are non-mechanistic, organic phenomena. CALM (for Change, Adaptation, Learning Model) is an innovative decision support system for enabling change based on CAS principles. CALM provides a low risk method for validating and refining change strategies that combines scenario planning techniques with "what-if" behavioral simulation. In essence, CALM "test drives" change strategies before rolling them out, allowing organizations to practice and learn from virtual rather than actual mistakes. This paper describes the CALM modeling methodology, including our metrics for measuring organizational readiness to respond to change and other major CALM scenario elements: prospective change strategies; alternate futures; and key situational dynamics. We then describe CALM's simulation engine for projecting scenario outcomes and its associated analytics. CALM's simulator unifies diverse behavioral simulation paradigms including: adaptive agents; system dynamics; Monte Carlo; event- and process-based techniques. CALM's embodiment of CAS dynamics helps organizations reduce risk and improve confidence and consistency in critical strategies for enabling transformations.

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Codeswitching techniques: evidence-based instructional practices for the ASL/English bilingual classroom.

PubMed

Andrews, Jean F; Rusher, Melissa

2010-01-01

The authors present a perspective on emerging bilingual deaf students who are exposed to, learning, and developing two languages--American Sign Language (ASL) and English (spoken English, manually coded English, and English reading and writing). The authors suggest that though deaf children may lack proficiency or fluency in either language during early language-learning development, they still engage in codeswitching activities, in which they go back and forth between signing and English to communicate. The authors then provide a second meaning of codeswitching--as a purpose-driven instructional technique in which the teacher strategically changes from ASL to English print for purposes of vocabulary and reading comprehension. The results of four studies are examined that suggest that certain codeswitching strategies support English vocabulary learning and reading comprehension. These instructional strategies are couched in a five-pronged approach to furthering the development of bilingual education for deaf students.

Machine Learning Toolkit for Extreme Scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-03-31

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are consideredmore » in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

Perceived Emotional Intelligence and Learning Strategies in Spanish University Students: A New Perspective from a Canonical Non-symmetrical Correspondence Analysis.

PubMed

Vega-Hernández, María C; Patino-Alonso, María C; Cabello, Rosario; Galindo-Villardón, María P; Fernández-Berrocal, Pablo

2017-01-01

Recent studies have revealed that emotional competences are relevant to the student's learning process and, more specifically, in the use of learning strategies (LSs). The aim of this study is twofold. First, we aim to analyze the relationship between perceived emotional intelligence (PEI) and LSs applying the scales TMMS-24 and Abridged ACRA to a sample of 2334 Spanish university students, whilst also exploring possible gender differences. Second, we aim to propose a methodological alternative based on the Canonical non-symmetrical correspondence analysis (CNCA), as an alternative to the methods traditionally used in Psychology and Education. Our results show that PEI has an impact on the LS of the students. Male participants with high scores on learning support strategies are positively related to high attention, clarity, and emotional repair. However, the use of cognitive and control LS is related to low values on the PEI dimensions. For women, high scores on cognitive, control, and learning support LS are related to high emotional attention, whereas dimensions such as study habits and learning support are related to adequate emotional repair. Participants in the 18-19 and 22-23 years age groups showed similar behavior. High scores on learning support strategies are related to high values on three dimensions of the PEI, and high values of study habits show high values for clarity and low values for attention and repair. The 20-21 and older than 24 years age groups behaved similarly. High scores on learning support strategies are related to low values on clarity, and study habits show high values for clarity and repair. This article presents the relationship between PEI and LS in university students, the differences by gender and age, and CNCA as an alternative method to techniques used in this field to study this association.

Perceived Emotional Intelligence and Learning Strategies in Spanish University Students: A New Perspective from a Canonical Non-symmetrical Correspondence Analysis

PubMed Central

Vega-Hernández, María C.; Patino-Alonso, María C.; Cabello, Rosario; Galindo-Villardón, María P.; Fernández-Berrocal, Pablo

2017-01-01

Recent studies have revealed that emotional competences are relevant to the student’s learning process and, more specifically, in the use of learning strategies (LSs). The aim of this study is twofold. First, we aim to analyze the relationship between perceived emotional intelligence (PEI) and LSs applying the scales TMMS-24 and Abridged ACRA to a sample of 2334 Spanish university students, whilst also exploring possible gender differences. Second, we aim to propose a methodological alternative based on the Canonical non-symmetrical correspondence analysis (CNCA), as an alternative to the methods traditionally used in Psychology and Education. Our results show that PEI has an impact on the LS of the students. Male participants with high scores on learning support strategies are positively related to high attention, clarity, and emotional repair. However, the use of cognitive and control LS is related to low values on the PEI dimensions. For women, high scores on cognitive, control, and learning support LS are related to high emotional attention, whereas dimensions such as study habits and learning support are related to adequate emotional repair. Participants in the 18–19 and 22–23 years age groups showed similar behavior. High scores on learning support strategies are related to high values on three dimensions of the PEI, and high values of study habits show high values for clarity and low values for attention and repair. The 20–21 and older than 24 years age groups behaved similarly. High scores on learning support strategies are related to low values on clarity, and study habits show high values for clarity and repair. This article presents the relationship between PEI and LS in university students, the differences by gender and age, and CNCA as an alternative method to techniques used in this field to study this association. PMID:29163272

Machine-Learning Techniques for the Determination of Attrition of Forces Due to Atmospheric Conditions

DTIC Science & Technology

2018-02-01

the possibility of a correlation between aircraft incidents in the National Transportation Safety Board database and meteorological conditions. If a...strong correlation could be found, it could be used to derive a model to predict aircraft incidents and become part of a decision support tool for...techniques, primarily the random forest algorithm, were used to explore the possibility of a correlation between aircraft incidents in the National

Leveraging Experiential Learning Techniques for Transfer

ERIC Educational Resources Information Center

Furman, Nate; Sibthorp, Jim

2013-01-01

Experiential learning techniques can be helpful in fostering learning transfer. Techniques such as project-based learning, reflective learning, and cooperative learning provide authentic platforms for developing rich learning experiences. In contrast to more didactic forms of instruction, experiential learning techniques foster a depth of learning…

Applied learning-based color tone mapping for face recognition in video surveillance system

NASA Astrophysics Data System (ADS)

Yew, Chuu Tian; Suandi, Shahrel Azmin

2012-04-01

In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

How clinical medical students perceive others to influence their self-regulated learning.

PubMed

Berkhout, Joris J; Helmich, Esther; Teunissen, Pim W; van der Vleuten, Cees P M; Jaarsma, A Debbie C

2017-03-01

Undergraduate medical students are prone to struggle with learning in clinical environments. One of the reasons may be that they are expected to self-regulate their learning, which often turns out to be difficult. Students' self-regulated learning is an interactive process between person and context, making a supportive context imperative. From a socio-cultural perspective, learning takes place in social practice, and therefore teachers and other hospital staff present are vital for students' self-regulated learning in a given context. Therefore, in this study we were interested in how others in a clinical environment influence clinical students' self-regulated learning. We conducted a qualitative study borrowing methods from grounded theory methodology, using semi-structured interviews facilitated by the visual Pictor technique. Fourteen medical students were purposively sampled based on age, gender, experience and current clerkship to ensure maximum variety in the data. The interviews were transcribed verbatim and were, together with the Pictor charts, analysed iteratively, using constant comparison and open, axial and interpretive coding. Others could influence students' self-regulated learning through role clarification, goal setting, learning opportunities, self-reflection and coping with emotions. We found large differences in students' self-regulated learning and their perceptions of the roles of peers, supervisors and other hospital staff. Novice students require others, mainly residents and peers, to actively help them to navigate and understand their new learning environment. Experienced students who feel settled in a clinical environment are less susceptible to the influence of others and are better able to use others to their advantage. Undergraduate medical students' self-regulated learning requires context-specific support. This is especially important for more novice students learning in a clinical environment. Their learning is influenced most heavily by peers and residents. Supporting novice students' self-regulated learning may be improved by better equipping residents and peers for this role. © 2016 The Authors. Medical Education Published by John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Features of an effective operative dentistry learning environment: students' perceptions and relationship with performance.

PubMed

Suksudaj, N; Lekkas, D; Kaidonis, J; Townsend, G C; Winning, T A

2015-02-01

Students' perceptions of their learning environment influence the quality of outcomes they achieve. Learning dental operative techniques in a simulated clinic environment is characterised by reciprocal interactions between skills training, staff- and student-related factors. However, few studies have examined how students perceive their operative learning environments and whether there is a relationship between their perceptions and subsequent performance. Therefore, this study aimed to clarify which learning activities and interactions students perceived as supporting their operative skills learning and to examine relationships with their outcomes. Longitudinal data about examples of operative laboratory sessions that were perceived as effective or ineffective for learning were collected twice a semester, using written critical incidents and interviews. Emergent themes from these data were identified using thematic analysis. Associations between perceptions of learning effectiveness and performance were analysed using chi-square tests. Students indicated that an effective learning environment involved interactions with tutors and peers. This included tutors arranging group discussions to clarify processes and outcomes, providing demonstrations and constructive feedback. Feedback focused on mistakes, and not improvement, was reported as being ineffective for learning. However, there was no significant association between students' perceptions of the effectiveness of their learning experiences and subsequent performance. It was clear that learning in an operative technique setting involved various factors related not only to social interactions and observational aspects of learning but also to cognitive, motivational and affective processes. Consistent with studies that have demonstrated complex interactions between students, their learning environment and outcomes, other factors need investigation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments

NASA Technical Reports Server (NTRS)

Birisan, Mihnea; Beling, Peter

2011-01-01

New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.

Motivating and Evaluating Growth in Ballet Technique

ERIC Educational Resources Information Center

White, Julie Hammond

2012-01-01

In teaching young dancers ballet, the utilization of effective assessments in partnership with supportive and creative teaching strategies can transform not only the learning experience, but the dancer as well. In this article, the author shares a "growth grade rubric" that specifically addresses three areas in ballet training: (1) skills and…

What the Computer Taught Me About My Students...or Is Binary Search "Natural"?

ERIC Educational Resources Information Center

Pasquino, Anne

1978-01-01

Several examples of student-written programs "teaching" a computer to guess systematically in finding a number between 0 and 10,000 are illustrated. These lend support to the contention that rather than being a "natural" application, using a binary search is a learned technique. (MN)

Professional Development in Statistics, Technology, and Cognitively Demanding Tasks: Classroom Implementation and Obstacles

ERIC Educational Resources Information Center

Foley, Gregory D.; Khoshaim, Heba Bakr; Alsaeed, Maha; Er, S. Nihan

2012-01-01

Attending professional development programmes can support teachers in applying new strategies for teaching mathematics and statistics. This study investigated (a) the extent to which the participants in a professional development programme subsequently used the techniques they had learned when teaching mathematics and statistics and (b) the…

Inquiry in the Physical Geology Classroom: Supporting Students' Conceptual Model Development

ERIC Educational Resources Information Center

Miller, Heather R.; McNeal, Karen S.; Herbert, Bruce E.

2010-01-01

This study characterizes the impact of an inquiry-based learning (IBL) module versus a traditionally structured laboratory exercise. Laboratory sections were randomized into experimental and control groups. The experimental group was taught using IBL pedagogical techniques and included manipulation of large-scale data-sets, use of multiple…

Engineering Encounters: Reverse Engineering

ERIC Educational Resources Information Center

McGowan, Veronica Cassone; Ventura, Marcia; Bell, Philip

2017-01-01

This column presents ideas and techniques to enhance your science teaching. This month's issue shares information on how students' everyday experiences can support science learning through engineering design. In this article, the authors outline a reverse-engineering model of instruction and describe one example of how it looked in our fifth-grade…

Using an Online Portfolio Course in Assessing Students' Work

ERIC Educational Resources Information Center

Yilmaz, Harun; Cetinkaya, Bulent

2007-01-01

New developments and advancements in informational technology bring about several alternative avenues for educators to select in supporting and evaluating their students' learning. Online portfolio is a fairly new technique in this regard. As the online education grows, use of online portfolio becomes more vital for educational programs. At…

Long-range Perspectives in Environmental Education: Producing Practical Problem-solvers.

ERIC Educational Resources Information Center

Barratt, Rod

1997-01-01

Addresses postgraduate environmental education by supported distance learning as offered by the Open University in Great Britain. Refers to techniques for regularly updating material in rapidly developing areas as well as integrating teaching and research. Also refers to the modular course Integrated Safety, Health and Environmental Management.…

The Promise of Open Educational Resources

ERIC Educational Resources Information Center

Smith, Marshall S.; Casserly, Catherine M.

2006-01-01

Open educational resources (OER) include full courses, course materials, modules, textbooks, streaming videos, tests, software, and any other tools, materials, or techniques used to either support access to knowledge, or have an impact on teaching, learning, and research. At the heart of the OER movement is the simple and powerful idea that the…

Supporting Mathematical Discourse in the Early Grades. Interactive STEM Research + Practice Brief

ERIC Educational Resources Information Center

Stiles, Jennifer

2016-01-01

This research brief discusses the benefits of teachers using mathematical discourse--allowing students to explain, justify, and debate their individual techniques for solving math problems--to enhance learning. Using this strategy requires educators to discard traditional teacher-centered modes of instruction and adopt new student-centered modes…

A decision-based perspective for the design of methods for systems design

NASA Technical Reports Server (NTRS)

Mistree, Farrokh; Muster, Douglas; Shupe, Jon A.; Allen, Janet K.

1989-01-01

Organization of material, a definition of decision based design, a hierarchy of decision based design, the decision support problem technique, a conceptual model design that can be manufactured and maintained, meta-design, computer-based design, action learning, and the characteristics of decisions are among the topics covered.

Integrating Thinking, Art and Language in Teaching Young Children

ERIC Educational Resources Information Center

Liu, Ping

2009-01-01

This study investigates learning outcomes of four-year-old children at a preschool in P. R. China. The children are educated in a school ecology designed to address cognitive, social, linguistic and psychological development, where an instructional technique, "Integrating Thinking, Art and Language" (ITAL), is applied to support them in…

Tuberculosis diagnosis support analysis for precarious health information systems.

PubMed

Orjuela-Cañón, Alvaro David; Camargo Mendoza, Jorge Eliécer; Awad García, Carlos Enrique; Vergara Vela, Erika Paola

2018-04-01

Pulmonary tuberculosis is a world emergency for the World Health Organization. Techniques and new diagnosis tools are important to battle this bacterial infection. There have been many advances in all those fields, but in developing countries such as Colombia, where the resources and infrastructure are limited, new fast and less expensive strategies are increasingly needed. Artificial neural networks are computational intelligence techniques that can be used in this kind of problems and offer additional support in the tuberculosis diagnosis process, providing a tool to medical staff to make decisions about management of subjects under suspicious of tuberculosis. A database extracted from 105 subjects with precarious information of people under suspect of pulmonary tuberculosis was used in this study. Data extracted from sex, age, diabetes, homeless, AIDS status and a variable with clinical knowledge from the medical personnel were used. Models based on artificial neural networks were used, exploring supervised learning to detect the disease. Unsupervised learning was used to create three risk groups based on available information. Obtained results are comparable with traditional techniques for detection of tuberculosis, showing advantages such as fast and low implementation costs. Sensitivity of 97% and specificity of 71% where achieved. Used techniques allowed to obtain valuable information that can be useful for physicians who treat the disease in decision making processes, especially under limited infrastructure and data. Copyright © 2018 Elsevier B.V. All rights reserved.

The Use of Interactive Technologies to Improve Student Learning of Physics from Middle School to College

NASA Astrophysics Data System (ADS)

Sheldon, Peter; Wellington, Tracey

2003-03-01

The Physics Department at Randolph-Macon Woman's College, a liberal arts women's college of 720, has traditionally turned out approximately 0.6 majors/year. We have invigorated the program by adding community (e.g. SPS, physical space, organized activities), adding a significant technical component (e.g. web-assisted and computer interfaced labs and more technology in the classes [1]), and incorporating new learning techniques (JITT, Physlets, Peer Instruction [2], Interactive DVD's, and using the Personal Response System [3]). Students have responded well as evidenced by significant increases in enrollments as well as strong scores on the FCI. As an offshoot of this original project supported by the NSF, we have applied some of these teaching methods to teach younger children and teachers of younger children. In this presentation, we will discuss the implementation of the new curricular developments and the specific changes we have seen and hope to see in student learning. [1] This work is supported in part by the NSF CCLI Program under grant DUE-9980890. [2] See, for example, the project Galileo website http://galileo.harvard.edu for a description of all of these techniques. [3] The Personal Response System is a wireless response system made by Educue, www.educue.com.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

PubMed

Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

2017-01-01

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

The Field-tested Learning Assessment Guide (FLAG): A Community Repository of Proven Alternative Assessment Instruments for STEM Education

NASA Astrophysics Data System (ADS)

Zeilik, M.; Garvin-Doxas, K.

2003-12-01

FLAG, the Field-tested Learning Assessment Guide (http://www.flaguide.org/) is a NSF funded website that offers broadly-applicable, self-contained modular classroom assessment techniques (CATs) and discipline-specific tools for STEM instructors creating new approaches to evaluate student learning, attitudes and performance. In particular, the FLAG contains proven techniques for alterative assessments---those needed for reformed, innovative STEM courses. Each tool has been developed, tested and refined in real classrooms at colleges and universities. The FLAG also contains an assessment primer, a section to help you select the most appropriate assessment technique(s) for your course goals, and other resources. In addition to references on instrument development and field-tested instruments on attitudes towards science, the FLAG also includes discipline-specific tools in Physics, Astronomy, Biology, and Mathematics. Building of the Geoscience collection is currently under way with the development of an instrument for detecting misconceptions of incoming freshmen on Space Science, which is being developed with the help of the Committee on Space Science and Astronomy of the American Association of Physics Teachers. Additional field-tested resources from the Geosciences are solicited from the community. Contributions should be sent to Michael Zeilik, zeilik@la.unm.edu. This work has been supported in part by NSF grant DUE 99-81155.

Retinal blood vessel segmentation using fully convolutional network with transfer learning.

PubMed

Jiang, Zhexin; Zhang, Hao; Wang, Yi; Ko, Seok-Bum

2018-04-26

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging. Meanwhile, additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result. Extensive experiments have been conducted on DRIVE, STARE, CHASE_DB1 and HRF databases, and the accuracy of the cross-database test on these four databases is state-of-the-art, which also presents the high robustness of the proposed approach. This successful result has not only contributed to the area of automated retinal blood vessel segmentation but also supports the effectiveness of transfer learning when applying deep learning technique to medical imaging. Copyright © 2018 Elsevier Ltd. All rights reserved.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation

PubMed Central

Song, Yongsoo; Wang, Shuang; Xia, Yuhou; Jiang, Xiaoqian

2018-01-01

Background Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis. Objective The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). Methods We adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques. Results Using real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset. Conclusions We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still. PMID:29666041

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Is Order the Defining Feature of Magnitude Representation? An ERP Study on Learning Numerical Magnitude and Spatial Order of Artificial Symbols

PubMed Central

Zhao, Hui; Chen, Chuansheng; Zhang, Hongchuan; Zhou, Xinlin; Mei, Leilei; Chen, Chunhui; Chen, Lan; Cao, Zhongyu; Dong, Qi

2012-01-01

Using an artificial-number learning paradigm and the ERP technique, the present study investigated neural mechanisms involved in the learning of magnitude and spatial order. 54 college students were divided into 2 groups matched in age, gender, and school major. One group was asked to learn the associations between magnitude (dot patterns) and the meaningless Gibson symbols, and the other group learned the associations between spatial order (horizontal positions on the screen) and the same set of symbols. Results revealed differentiated neural mechanisms underlying the learning processes of symbolic magnitude and spatial order. Compared to magnitude learning, spatial-order learning showed a later and reversed distance effect. Furthermore, an analysis of the order-priming effect showed that order was not inherent to the learning of magnitude. Results of this study showed a dissociation between magnitude and order, which supports the numerosity code hypothesis of mental representations of magnitude. PMID:23185363

Prediction of antiepileptic drug treatment outcomes using machine learning.

PubMed

Colic, Sinisa; Wither, Robert G; Lang, Min; Zhang, Liang; Eubanks, James H; Bardakjian, Berj L

2017-02-01

Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC ) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Prediction of antiepileptic drug treatment outcomes using machine learning

NASA Astrophysics Data System (ADS)

Colic, Sinisa; Wither, Robert G.; Lang, Min; Zhang, Liang; Eubanks, James H.; Bardakjian, Berj L.

2017-02-01

Objective. Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Approach. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. Main results. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Significance. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Application of microlearning technique and Twitter for educational purposes

NASA Astrophysics Data System (ADS)

Aitchanov, B. H.; Satabaldiyev, A. B.; Latuta, K. N.

2013-04-01

The current paper reviews the usage of social resource such as Twitter in microlearning technique for educational purposes. The problem is that most of instructors are unaware that with the help of social networks the students' productivity can increase. The research is applied on CS205 Advanced Programming in C++ course at Suleyman Demirel University (Kazakhstan). The collected results show that in a modern world of emerging mobile technologies, we are as educators should improve the way of teaching by adding electronically supported learning methods. In this study, the significance of microlearning technique is proposed.

Examining Residents' Strategic Mindfulness During Self-Regulated Learning of a Simulated Procedural Skill.

PubMed

Brydges, Ryan; Hatala, Rose; Mylopoulos, Maria

2016-07-01

Simulation-based training is currently embedded in most health professions education curricula. Without evidence for how trainees think about their simulation-based learning, some training techniques may not support trainees' learning strategies. This study explored how residents think about and self-regulate learning during a lumbar puncture (LP) training session using a simulator. In 2010, 20 of 45 postgraduate year 1 internal medicine residents attended a mandatory procedural skills training boot camp. Independently, residents practiced the entire LP skill on a part-task trainer using a clinical LP tray and proper sterile technique. We interviewed participants regarding how they thought about and monitored their learning processes, and then we conducted a thematic analysis of the interview data. The analysis suggested that participants considered what they could and could not learn from the simulator; they developed their self-confidence by familiarizing themselves with the LP equipment and repeating the LP algorithmic steps. Participants articulated an idiosyncratic model of learning they used to interpret the challenges and successes they experienced. Participants reported focusing on obtaining cerebrospinal fluid and memorizing the "routine" version of the LP procedure. They did not report much thinking about their learning strategies (eg, self-questioning). During simulation-based training, residents described assigning greater weight to achieving procedural outcomes and tended to think that the simulated task provided them with routine, generalizable skills. Over this typical 1-hour session, trainees did not appear to consider their strategic mindfulness (ie, awareness and use of learning strategies).

Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support

PubMed Central

Kraut, Robert E; Levine, John M

2015-01-01

Background Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. Objective The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Methods Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Results Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=–.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=–.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Conclusions Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities. PMID:25896033

Eliciting and receiving online support: using computer-aided content analysis to examine the dynamics of online social support.

PubMed

Wang, Yi-Chia; Kraut, Robert E; Levine, John M

2015-04-20

Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=-.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=-.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities.

Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies.

NASA Astrophysics Data System (ADS)

Avetisyan, H.; Bruna, O.; Holub, J.

2016-11-01

A numerous techniques and algorithms are dedicated to extract emotions from input data. In our investigation it was stated that emotion-detection approaches can be classified into 3 following types: Keyword based / lexical-based, learning based, and hybrid. The most commonly used techniques, such as keyword-spotting method, Support Vector Machines, Naïve Bayes Classifier, Hidden Markov Model and hybrid algorithms, have impressive results in this sphere and can reach more than 90% determining accuracy.

Improvements in Students' Understanding from Increased Implementation of Active Learning Strategies

NASA Astrophysics Data System (ADS)

Hayes-Gehrke, Melissa N.; Prather, E. E.; Rudolph, A. L.; Collaboration of Astronomy Teaching Scholars CATS

2011-01-01

Many instructors are hesitant to implement active learning strategies in their introductory astronomy classrooms because they are not sure which techniques they should use, how to implement those techniques, and question whether the investment in changing their course will really bring the advertised learning gains. We present an example illustrating how thoughtful and systematic implementation of active learning strategies into a traditionally taught Astro 101 class can translate into significant increases in students' understanding. We detail the journey of one instructor, over several years, as she changes the instruction and design of her course from one that focuses almost exclusively on lecture to a course that provides an integrated use of several active learning techniques such as Lecture-Tutorials and Think-Pair-Share questions. The students in the initial lecture-only course achieved a low normalized gain score of only 0.2 on the Light and Spectroscopy Concept Inventory (LSCI), while the students in the re-designed learner-centered course achieved a significantly better normalized gain of 0.43. This material is based upon work supported by the National Science Foundation under Grant No. 0715517, a CCLI Phase III Grant for the Collaboration of Astronomy Teaching Scholars (CATS), and Grant No. 0847170, a PAARE Grant for the Calfornia-Arizona Minority Partnership for Astronomy Research and Education (CAMPARE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Classifying Structures in the ISM with Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

PubMed

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.

The Development of Instruments to Measure Motivational Interviewing Skill Acquisition for School-Based Personnel

ERIC Educational Resources Information Center

Small, Jason W.; Lee, Jon; Frey, Andy J.; Seeley, John R.; Walker, Hill M.

2014-01-01

As specialized instructional support personnel begin learning and using motivational interviewing (MI) techniques in school-based settings, there is growing need for context-specific measures to assess initial MI skill development. In this article, we describe the iterative development and preliminary evaluation of two measures of MI skill adapted…

Using Nonlinear Programming in International Trade Theory: The Factor-Proportions Model

ERIC Educational Resources Information Center

Gilbert, John

2004-01-01

Students at all levels benefit from a multi-faceted approach to learning abstract material. The most commonly used technique in teaching the pure theory of international trade is a combination of geometry and algebraic derivations. Numerical simulation can provide a valuable third support to these approaches. The author describes a simple…

Considering the Efficacy of Web-Based Worked Examples in Introductory Chemistry

ERIC Educational Resources Information Center

Crippen, Kent J.; Earl, Boyd L.

2004-01-01

Theory suggests that studying worked examples and engaging in self-explanation will improve learning and problem solving. A growing body of evidence supports the use of web-based assessments for improving undergraduate performance in traditional large enrollment courses. This article describes a study designed to investigate these techniques in a…

Research-Proven Instructional Strategies Engage, Motivate and Support Students in Meeting Higher Standards

ERIC Educational Resources Information Center

Southern Regional Education Board (SREB), 2011

2011-01-01

Instructional strategies make a difference in whether students are engaged in learning and are profiting from their time in class. High schools, technology centers and middle grades schools are encouraging teachers to adopt new teaching techniques and are providing opportunities for teachers to work together to improve their instructional skills…

Seeing Cells: Teaching the Visual/Verbal Rhetoric of Biology

ERIC Educational Resources Information Center

Dinolfo, John; Heifferon, Barbara; Temesvari, Lesly A.

2007-01-01

This pilot study obtained baseline information on verbal and visual rhetorics to teach microscopy techniques to college biology majors. We presented cell images to students in cell biology and biology writing classes and then asked them to identify textual, verbal, and visual cues that support microscopy learning. Survey responses suggest that…

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.

PubMed

Yu, Hwanjo; Kim, Taehoon; Oh, Jinoh; Ko, Ilhwan; Kim, Sungchul; Han, Wook-Shin

2010-04-16

Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

PubMed Central

2010-01-01

Background Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user’s feedback and efficiently processes the function to return relevant articles in real time. PMID:20406504

Service Learning In Physics: The Consultant Model

NASA Astrophysics Data System (ADS)

Guerra, David

2005-04-01

Each year thousands of students across the country and across the academic disciplines participate in service learning. Unfortunately, with no clear model for integrating community service into the physics curriculum, there are very few physics students engaged in service learning. To overcome this shortfall, a consultant based service-learning program has been developed and successfully implemented at Saint Anselm College (SAC). As consultants, students in upper level physics courses apply their problem solving skills in the service of others. Most recently, SAC students provided technical and managerial support to a group from Girl's Inc., a national empowerment program for girls in high-risk, underserved areas, who were participating in the national FIRST Lego League Robotics competition. In their role as consultants the SAC students provided technical information through brainstorming sessions and helped the girls stay on task with project management techniques, like milestone charting. This consultant model of service-learning, provides technical support to groups that may not have a great deal of resources and gives physics students a way to improve their interpersonal skills, test their technical expertise, and better define the marketable skill set they are developing through the physics curriculum.

Moral learning: Psychological and philosophical perspectives.

PubMed

Cushman, Fiery; Kumar, Victor; Railton, Peter

2017-10-01

The past 15years occasioned an extraordinary blossoming of research into the cognitive and affective mechanisms that support moral judgment and behavior. This growth in our understanding of moral mechanisms overshadowed a crucial and complementary question, however: How are they learned? As this special issue of the journal Cognition attests, a new crop of research into moral learning has now firmly taken root. This new literature draws on recent advances in formal methods developed in other domains, such as Bayesian inference, reinforcement learning and other machine learning techniques. Meanwhile, it also demonstrates how learning and deciding in a social domain-and especially in the moral domain-sometimes involves specialized cognitive systems. We review the contributions to this special issue and situate them within the broader contemporary literature. Our review focuses on how we learn moral values and moral rules, how we learn about personal moral character and relationships, and the philosophical implications of these emerging models. Copyright © 2017 Elsevier B.V. All rights reserved.

Peer coaching as a technique to foster professional development in clinical ambulatory settings.

PubMed

Sekerka, Leslie E; Chao, Jason

2003-01-01

Few studies have examined how peer coaching is an effective educational and development technique in contexts outside the classroom. This research focused on peer coaching as a platform to study the process of professional development for physicians. The purpose was to identify perceived benefits coaches received from a coaching encounter and how this relates to their own process of professional development. Critical incident interviews with 13 physician coaches were conducted and tape recorded. Themes were identified using a thematic analysis technique. Themes emerged clustering around two distinct benefit orientations. Group 1, reflection and teaching coaches, tended to focus on others and discuss how positively they experienced the encounter. Group 2, personal learning and change coaches, expressed benefits along more personal lines. Peer coaching contributes to physicians' professional development by encouraging reflection time and learning. Peer coaching affords positive impact to those who coach in addition to those who receive the coaching. The two clusters of benefits support the performance, learning, and development theory in that there are multiple modes to describe adult growth and development. Programs of this type should be considered in medical faculty development activities associated with medical education.

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Undergraduate nursing students' transformational learning during clinical training.

PubMed

Melin-Johansson, Christina; Österlind, Jane; Hagelin, Carina Lundh; Henoch, Ingela; Ek, Kristina; Bergh, Ingrid; Browall, Maria

2018-04-02

Undergraduate nursing students encounter patients at the end of life during their clinical training. They need to confront dying and death under supportive circumstances in order to be prepared for similar situations in their future career. To explore undergraduate nursing students' descriptions of caring situations with patients at the end of life during supervised clinical training. A qualitative study using the critical incident technique was chosen. A total of 85 students wrote a short text about their experiences of caring for patients at the end of life during their clinical training. These critical incident reports were then analysed using deductive and inductive content analysis. The theme 'students' transformational learning towards becoming a professional nurse during clinical training' summarises how students relate to patients and relatives, interpret the transition from life to death, feel when caring for a dead body and learn end-of-life caring actions from their supervisors. As a preparation for their future profession, students undergoing clinical training need to confront death and dying while supported by trained supervisors and must learn how to communicate about end-of-life issues and cope with emotional stress and grief.

Pattern Activity Clustering and Evaluation (PACE)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Banas, Christopher; Paul, Michael; Bussjager, Becky; Seetharaman, Guna

2012-06-01

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Unintended consequences of machine learning in medicine?

PubMed

McDonald, Laura; Ramagopalan, Sreeram V; Cox, Andrew P; Oguz, Mustafa

2017-01-01

Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care.

Analysis of blocking of flavor-preference conditioning based on nutrients and palatable tastes in rats.

PubMed

González, Felisa; Garcia-Burgos, David; Hall, Geoffrey

2014-09-01

In Experiment 1 rats were given training in which a mixture of two flavors was paired with sucrose. This established a substantial preference for each of the flavors; however, when rats were given prior experience with just one of the flavors paired with sucrose, training with the compound produced only a weak preference for the other - an example of the blocking effect, well known in other associative learning paradigms. Both the palatable taste of sucrose and its nutrient properties contribute to its ability to reinforce preference acquisition. The role of these two forms of learning was examined in two further experiments in which the reinforcer used was fructose (which is considered to support preference learning because it is palatable but not through its nutrient properties) or maltodextrin (thought to support preference learning by way of its nutrient properties). In neither case was blocking observed. At the theoretical level, this outcome constitutes a challenge to the attempt to explain flavor-preference learning in terms of the standard principles of associative learning theory. Its implication at the level of application is that the potential of the blocking procedure as a technique for preventing the development of unwanted flavor preferences may be limited. Copyright © 2014 Elsevier Ltd. All rights reserved.

UK health-care professionals' experience of on-line learning techniques: a systematic review of qualitative data.

PubMed

Carroll, Christopher; Booth, Andrew; Papaioannou, Diana; Sutton, Anthea; Wong, Ruth

2009-01-01

Continuing professional development and education is vital to the provision of better health services and outcomes. The aim of this study is to contribute to the evidence base by performing a systematic review of qualitative data from studies reporting health professionals' experience of e-learning. No such previous review has been published. A systematic review of qualitative data reporting UK health professionals' experiences of the ways in which on-line learning is delivered by higher education and other relevant institutions. Evidence synthesis was performed with the use of thematic analysis grounded in the data. Literature searches identified 19 relevant studies. The subjects of the studies were nurses, midwives, and allied professions (8 studies), general practitioners and hospital doctors (6 studies), and a range of different health practitioners (5 studies). The majority of courses were stand-alone continuing professional development modules. Five key themes emerged from the data: peer communication, flexibility, support, knowledge validation, and course presentation and design. The effectiveness of on-line learning is mediated by the learning experience. If they are to enhance health professionals' experience of e-learning, courses need to address presentation and course design; they must be flexible, offer mechanisms for both support and rapid assessment, and develop effective and efficient means of communication, especially among the students themselves.

Developing an instrument to measure emotional behaviour abilities of meaningful learning through the Delphi technique.

PubMed

Cadorin, Lucia; Bagnasco, Annamaria; Tolotti, Angela; Pagnucci, Nicola; Sasso, Loredana

2017-09-01

To identify items for a new instrument that measures emotional behaviour abilities of meaningful learning, according to Fink's Taxonomy. Meaningful learning is an active process that promotes a wider and deeper understanding of concepts. It is the result of an interaction between new and previous knowledge and produces a long-term change of knowledge and skills. To measure meaningful learning capability, it is very important in the education of health professionals to identify problems or special learning needs. For this reason, it is necessary to create valid instruments. A Delphi Study technique was implemented in four phases by means of e-mail. The study was conducted from April-September 2015. An expert panel consisting of ten researchers with experience in Fink's Taxonomy was established to identify the items of the instrument. Data were analysed for conceptual description and item characteristics and attributes were rated. Expert consensus was sought in each of these phases. An 87·5% consensus cut-off was established. After four rounds, consensus was obtained for validation of the content of the instrument 'Assessment of Meaningful learning Behavioural and Emotional Abilities'. This instrument consists of 56 items evaluated on a 6-point Likert-type scale. Foundational Knowledge, Application, Integration, Human Dimension, Caring and Learning How to Learn were the six major categories explored. This content validated tool can help educators (teachers, trainers and tutors) to identify and improve the strategies to support students' learning capability, which could increase their awareness of and/or responsibility in the learning process. © 2017 John Wiley & Sons Ltd.

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Collaborative Occupational Therapy: Teachers' Impressions of the Partnering for Change (P4C) Model.

PubMed

Wilson, A L; Harris, S R

2018-05-01

Occupational therapists (OTs) often face barriers when trying to collaborate with teachers in school-based settings. Partnering for change (P4C), a collaborative practice model designed to support children with developmental coordination disorder, could potentially support all students with special needs. Therefore, the aim of this study was to explore how teachers experience OT services delivered using the P4C model to support children with a variety of special needs. P4C was implemented at one elementary school in Courtenay, British Columbia. Eleven teachers participated in two focus groups and a one-on-one interview to gather descriptive, qualitative data. Grounded theory techniques were used for data analysis. Four themes (collaborating in the thick of it all, learning and taking risks, managing limited time and resources, and appreciating responsive OT support) represented teachers' experiences of P4C. Teachers strongly preferred collaborative OT services based on the P4C model. Students with a variety of special needs were supported within their classrooms as teachers learned new strategies from the OT and found ways to embed these strategies into their daily routines.

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Smart Training, Smart Learning: The Role of Cooperative Learning in Training for Youth Services.

ERIC Educational Resources Information Center

Doll, Carol A.

1997-01-01

Examines cooperative learning in youth services and adult education. Discusses characteristics of cooperative learning techniques; specific cooperative learning techniques (brainstorming, mini-lecture, roundtable technique, send-a-problem problem solving, talking chips technique, and three-step interview); and the role of the trainer. (AEF)

Instructable autonomous agents. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Huffman, Scott Bradley

1994-01-01

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible tutorial instruction, Instructo-Soar demonstrates a breadth of interaction and learning capabilities that goes beyond previous instructable systems, such as learning apprentice systems. Instructo-Soar's techniques could form the basis for future 'instructable technologies' that come equipped with basic capabilities, and can be taught by novice users to perform any number of desired tasks.

Learning to Apply Algebra in the Community for Adults With Intellectual Developmental Disabilities.

PubMed

Rodriguez, Anthony M

2016-02-01

Students with intellectual and developmental disabilities (IDD) are routinely excluded from algebra and other high-level mathematics courses. High school students with IDD take courses in arithmetic and life skills rather than having an opportunity to learn algebra. Yet algebra skills can support the learning of money and budgeting skills. This study explores the feasibility of algebra instruction for adults with IDD through an experimental curriculum. Ten individuals with IDD participated in a 6-week course framing mathematics concepts within the context of everyday challenges in handling money. The article explores classroom techniques, discusses student strategies, and proposes possible avenues for future research analyzing mathematics instructional design strategies for individuals with IDD.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

High-Fidelity Simulation for Advanced Cardiac Life Support Training

PubMed Central

Davis, Lindsay E.; Storjohann, Tara D.; Spiegel, Jacqueline J.; Beiber, Kellie M.

2013-01-01

Objective. To determine whether a high-fidelity simulation technique compared with lecture would produce greater improvement in advanced cardiac life support (ACLS) knowledge, confidence, and overall satisfaction with the training method. Design. This sequential, parallel-group, crossover trial randomized students into 2 groups distinguished by the sequence of teaching technique delivered for ACLS instruction (ie, classroom lecture vs high-fidelity simulation exercise). Assessment. Test scores on a written examination administered at baseline and after each teaching technique improved significantly from baseline in all groups but were highest when lecture was followed by simulation. Simulation was associated with a greater degree of overall student satisfaction compared with lecture. Participation in a simulation exercise did not improve pharmacy students’ knowledge of ACLS more than attending a lecture, but it was associated with improved student confidence in skills and satisfaction with learning and application. Conclusions. College curricula should incorporate simulation to complement but not replace lecture for ACLS education. PMID:23610477

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; Neoh, Siew-Chin

2015-01-01

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

High-fidelity simulation for advanced cardiac life support training.

PubMed

Davis, Lindsay E; Storjohann, Tara D; Spiegel, Jacqueline J; Beiber, Kellie M; Barletta, Jeffrey F

2013-04-12

OBJECTIVE. To determine whether a high-fidelity simulation technique compared with lecture would produce greater improvement in advanced cardiac life support (ACLS) knowledge, confidence, and overall satisfaction with the training method. DESIGN. This sequential, parallel-group, crossover trial randomized students into 2 groups distinguished by the sequence of teaching technique delivered for ACLS instruction (ie, classroom lecture vs high-fidelity simulation exercise). ASSESSMENT. Test scores on a written examination administered at baseline and after each teaching technique improved significantly from baseline in all groups but were highest when lecture was followed by simulation. Simulation was associated with a greater degree of overall student satisfaction compared with lecture. Participation in a simulation exercise did not improve pharmacy students' knowledge of ACLS more than attending a lecture, but it was associated with improved student confidence in skills and satisfaction with learning and application. CONCLUSIONS. College curricula should incorporate simulation to complement but not replace lecture for ACLS education.

Planning for rover opportunistic science

NASA Technical Reports Server (NTRS)

Gaines, Daniel M.; Estlin, Tara; Forest, Fisher; Chouinard, Caroline; Castano, Rebecca; Anderson, Robert C.

2004-01-01

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Evidence-Based Higher Education – Is the Learning Styles ‘Myth’ Important?

PubMed Central

Newton, Philip M.; Miah, Mahallad

2017-01-01

The basic idea behind the use of ‘Learning Styles’ is that learners can be categorized into one or more ‘styles’ (e.g., Visual, Auditory, Converger) and that teaching students according to their style will result in improved learning. This idea has been repeatedly tested and there is currently no evidence to support it. Despite this, belief in the use of Learning Styles appears to be widespread amongst schoolteachers and persists in the research literature. This mismatch between evidence and practice has provoked controversy, and some have labeled Learning Styles a ‘myth.’ In this study, we used a survey of academics in UK Higher Education (n = 114) to try and go beyond the controversy by quantifying belief and, crucially, actual use of Learning Styles. We also attempted to understand how academics view the potential harms associated with the use of Learning Styles. We found that general belief in the use of Learning Styles was high (58%), but lower than in similar previous studies, continuing an overall downward trend in recent years. Critically the percentage of respondents who reported actually using Learning Styles (33%) was much lower than those who reported believing in their use. Far more reported using a number of techniques that are demonstrably evidence-based. Academics agreed with all the posited weaknesses and harms of Learning Styles theory, agreeing most strongly that the basic theory of Learning Styles is conceptually flawed. However, a substantial number of participants (32%) stated that they would continue to use Learning Styles despite being presented with the lack of an evidence base to support them, suggesting that ‘debunking’ Learning Styles may not be effective. We argue that the interests of all may be better served by promoting evidence-based approaches to Higher Education. PMID:28396647

Evidence-Based Higher Education - Is the Learning Styles 'Myth' Important?

PubMed

Newton, Philip M; Miah, Mahallad

2017-01-01

The basic idea behind the use of 'Learning Styles' is that learners can be categorized into one or more 'styles' (e.g., Visual, Auditory, Converger) and that teaching students according to their style will result in improved learning. This idea has been repeatedly tested and there is currently no evidence to support it. Despite this, belief in the use of Learning Styles appears to be widespread amongst schoolteachers and persists in the research literature. This mismatch between evidence and practice has provoked controversy, and some have labeled Learning Styles a 'myth.' In this study, we used a survey of academics in UK Higher Education ( n = 114) to try and go beyond the controversy by quantifying belief and, crucially, actual use of Learning Styles. We also attempted to understand how academics view the potential harms associated with the use of Learning Styles. We found that general belief in the use of Learning Styles was high (58%), but lower than in similar previous studies, continuing an overall downward trend in recent years. Critically the percentage of respondents who reported actually using Learning Styles (33%) was much lower than those who reported believing in their use. Far more reported using a number of techniques that are demonstrably evidence-based. Academics agreed with all the posited weaknesses and harms of Learning Styles theory, agreeing most strongly that the basic theory of Learning Styles is conceptually flawed. However, a substantial number of participants (32%) stated that they would continue to use Learning Styles despite being presented with the lack of an evidence base to support them, suggesting that 'debunking' Learning Styles may not be effective. We argue that the interests of all may be better served by promoting evidence-based approaches to Higher Education.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

PubMed

Chen, Huan-Yuan; Chen, Chih-Chang; Hwang, Wen-Jyi

2017-09-28

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

PubMed Central

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.

PubMed

Kim, Miran; Song, Yongsoo; Wang, Shuang; Xia, Yuhou; Jiang, Xiaoqian

2018-04-17

Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis. The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). We adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques. Using real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset. We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still. ©Miran Kim, Yongsoo Song, Shuang Wang, Yuhou Xia, Xiaoqian Jiang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 17.04.2018.

Rigor Plus Support: How Science Teachers Use Literacy Techniques to Get Students Ready for College

ERIC Educational Resources Information Center

Bayerl, Katie

2007-01-01

Schoolwide literacy--the teaching of reading, writing, speaking, and thinking practices in all content areas--is generally considered an effective, even necessary, approach to addressing the learning needs of adolescents. In early college high schools, which blend high school and college for students who are underserved in higher education, the…

Mathematics Problem Solving, Literacy, and ELL for Alternative Certification Teachers

ERIC Educational Resources Information Center

Evans, Brian R.; Ardito, Gerald; Kim, Soonhyang

2017-01-01

New teachers who entered the profession through alternative pathways often teach in high-need urban environments, which means there may be a significant number of English Language Learner (ELL) students in their classrooms. In order to best support these students, techniques can be employed to best facilitate learning for students who do not have…

The Instructional Instrument SL-EDGE Student Library-Educational DiGital Environment.

ERIC Educational Resources Information Center

Kyriakopoulou, Antonia; Kalamboukis, Theodore

An educational digital environment that will provide appropriate methods and techniques for the support and enhancement of the educational and learning process is a valuable tool for both educators and learners. In the context of such a mission, the educational tool SL-EDGE (Student Library-Educational DiGital Environment) has been developed. The…

Orff-Schulwerk: Design for Creativity. A Report of the Project "Creativity and Participation in Music Education."

ERIC Educational Resources Information Center

Bellflower Unified School District, CA.

The objectives of this program were to: (1) engender a class environment in which invention and improvisation of student composition will be encouraged, (2) provide supporting learning experiences with fundamental movement techniques, and (3) illuminate basic elements of composition connecting the organization of space and sound in artistic…

Bibliotherapy for Children: Using Books and Other Media to Help Children Cope.

ERIC Educational Resources Information Center

Weinstein, Stuart H.

Children are able to cope with a majority of problems of learning and growing; some problems, however, may require guidance and support. A technique available to all people, trained or untrained in counseling or guidance is bibliotherapy--a process of dynamic interaction between the personality of the reader and literature which may be used for…

Developing a Diagnosis System of Work-Related Capabilities for Students: A Computer-Assisted Assessment

ERIC Educational Resources Information Center

Liao, C. H.; Yang, M. H.; Yang, B. C.

2013-01-01

A gap exists between students' employment needs and higher education offerings. Thus, developing the capability to meet the learning needs of students in supporting their future aspirations should be facilitated. To bridge this gap in practice, this study uses multiple methods (i.e., nominal group technique and instructional systems development)…

Using a Strategy of "Structured Conversation" to Enhance the Quality of Tutorial Time

ERIC Educational Resources Information Center

Robinson, Stephanie

2008-01-01

This article considers the impact of a technique of structured conversation to enhance a student-centred approach to tutorial time. It is suggested that the development of such an approach can provide enhanced learning support in the current challenge of widening diversity in the learner population. Many students in modern tertiary education show…

Turning Teachers into Academics? The Role of Educational Development in Fostering Synergy between Teaching and Research

ERIC Educational Resources Information Center

Macfarlane, Bruce; Hughes, Gwyneth

2009-01-01

The history of educational development is rooted in the improvement of teaching techniques. As a result, centres or units have normally been located in central registrars or human resources departments, library, learning and technical support services, or established as semi-autonomous entities. The alignment of educational development with…

With a Little Help from My Friends: Scaffolding Techniques in Problem Solving

ERIC Educational Resources Information Center

Frederick, Michelle L.; Courtney, Scott; Caniglia, Joanne

2014-01-01

The purpose of this study was to explore middle grade mathematics students' uses of scaffolding and its effectiveness in helping students solve non-routine problems. Students were given two different types of scaffolds to support their learning of sixth grade geometry concepts. First, students solved a math task by using a four square graphic…

Scaffolding and Mediating for Creativity: Suggestions from Reflecting on Practice in Order to Develop the Teaching and Learning of Gymnastics

ERIC Educational Resources Information Center

Griggs, Gerald; McGregor, Debra

2012-01-01

This article takes a reflective stance on the development of practice in scaffolding and mediating for creativity and potentially better performance in gymnastics. The pedagogical approach outlined illustrates how an experienced practitioner can adopt mediational (rather than meddling) and scaffolding techniques to focus on supporting the…

Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques

ERIC Educational Resources Information Center

McLaren, Bruce M.; Scheuer, Oliver; Miksatko, Jan

2010-01-01

An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about topics presented by a teacher. These tools are aimed at teaching students how to discuss and argue, important skills not often taught in traditional classrooms. But how…

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.

PubMed

Gehrmann, Sebastian; Dernoncourt, Franck; Li, Yeran; Carlson, Eric T; Wu, Joy T; Welt, Jonathan; Foote, John; Moseley, Edward T; Grant, David W; Tyler, Patrick D; Celi, Leo A

2018-01-01

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

Group work: Facilitating the learning of international and domestic undergraduate nursing students.

PubMed

Shaw, Julie; Mitchell, Creina; Del Fabbro, Letitia

2015-01-01

Devising innovative strategies to address internationalization is a contemporary challenge for universities. A Participatory Action Research (PAR) project was undertaken to identify issues for international nursing students and their teachers. The findings identified group work as a teaching strategy potentially useful to facilitate international student learning. The educational intervention of structured group work was planned and implemented in one subject of a Nursing degree. Groups of four to five students were formed with one or two international students per group. Structural support was provided by the teacher until the student was learning independently, the traditional view of scaffolding. The group work also encouraged students to learn from one another, a contemporary understanding of scaffolding. Evaluation of the group work teaching strategy occurred via anonymous, self-completed student surveys. The student experience data were analysed using descriptive statistical techniques, and free text comments were analysed using content analysis. Over 85% of respondents positively rated the group work experience. Overwhelmingly, students reported that class discussions and sharing nursing experiences positively influenced their learning and facilitated exchange of knowledge about nursing issues from an international perspective. This evaluation of a structured group work process supports the use of group work in engaging students in learning, adding to our understanding of purposeful scaffolding as a pathway to enhance learning for both international and domestic students. By explicitly using group work within the curriculum, educators can promote student learning, a scholarly approach to teaching and internationalization of the curriculum.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Can social semantic web techniques foster collaborative curriculum mapping in medicine?

PubMed

Spreckelsen, Cord; Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-08-15

Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives ("LOs"). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 ("good") in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system's ability to support curriculum revision. The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems.

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach

NASA Astrophysics Data System (ADS)

Lin, Y.; Harp, D. R.; Chen, B.; Pawar, R.

2017-12-01

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including pressure. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning technique based on support vector regression to effectively and efficiently predict the leakage locations and leakage rates based on limited number of pressure observations. Compared to the conventional data-driven approaches, which can be usually seem as a "black box" procedure, we develop a physics-guided machine learning method to incorporate the governing physics into the learning procedure. To validate the performance of our proposed leakage detection method, we employ our method to both 2D and 3D synthetic subsurface models. Our novel CO2 leakage detection method has shown high detection accuracy in the example problems.

Obstetric ultrasound education for the developing world: A learning partnership with the World Federation for Ultrasound in Medicine and Biology

PubMed Central

Dubbins, P; Evans, JA

2015-01-01

The ultrasound techniques in pregnancy e-learning project is an online resource commissioned and supported by the Education Committee of the World Federation for Ultrasound in Medicine and Biology (WFUMB). This currently consists of 10 e-learning sessions aimed at midwives and other health workers in developing countries where WFUMB has Educational Centres of Excellence, and in particular at those based mainly in rural communities at considerable distance from urban training centres. The project covers all of the basics of obstetric ultrasound such as fetal and maternal anatomy, ultrasound techniques, assessment in both early and late pregnancy, prediction of pregnancy complications and identification of common abnormalities that might interfere with delivery. The e-learning project complements a wider training programme which covers operator skills and machine controls, in order to minimise the time that the professional has to leave their rural, often poorly staffed, workplace to attend classroom-based courses in the city. Each session outlines often complex concepts using simple diagrams, interactive exercises and cine clips. Tips, tricks and best practice guidelines are provided in simple terms. PMID:27433236

New pathways to physics instruction: Blending a MOOC and in-person discussion to train physics graduate students and postdocs in evidence-based teaching

NASA Astrophysics Data System (ADS)

Goldberg, Bennett

A challenge facing physics education is how to encourage and support the adoption of evidence-based instructional practices that decades of physics education research has shown to be effective. Like many STEM departments, physics departments struggle to overcome the barriers of faculty knowledge, motivation and time; institutional cultures and reward systems; and disciplinary traditions. Research has demonstrated successful transformation of department-level approaches to instruction through local learning communities, in-house expertise, and department administrative support. In this talk, I will discuss how physics and other STEM departments can use a MOOC on evidence-based instruction together with in-person seminar discussions to create a learning community of graduate students and postdocs, and how such communities can affect departmental change in teaching and learning. Four university members of the 21-university network working to prepare future faculty to be both excellent researchers and excellent teachers collaborated on an NSF WIDER project to develop and deliver two massive open online courses (MOOCs) in evidence-based STEM instruction. A key innovation is a new blended mode of delivery where groups of participants engaged with the online content and then meet weekly in local learning communities to discuss content, communicate current experiences, and delve deeper into particular techniques of local interest. The MOOC team supported these so-called MOOC-Centered Learning Communities, or MCLCs, with detailed facilitator guides complete with synopses of online content, learning goals and suggested activities for in-person meetings, as well as virtual MCLC communities for sharing and feedback. In the initial run of the first MOOC, 40 MCLCs were created; in the second run this past fall, more than 80 MCLCs formed. Further, target audiences of STEM graduate students and postdocs completed at a 40-50% rate, indicating the value they place in building their knowledge in evidence-based instruction. We will present data on the impact of being in an MCLC on completion and learning outcomes, as well as data on departmental change in physics supported by MCLCs. Work supported by NSF DUE-1347605.

Metrics in method engineering

NASA Astrophysics Data System (ADS)

Brinkkemper, S.; Rossi, M.

1994-12-01

As customizable computer aided software engineering (CASE) tools, or CASE shells, have been introduced in academia and industry, there has been a growing interest into the systematic construction of methods and their support environments, i.e. method engineering. To aid the method developers and method selectors in their tasks, we propose two sets of metrics, which measure the complexity of diagrammatic specification techniques on the one hand, and of complete systems development methods on the other hand. Proposed metrics provide a relatively fast and simple way to analyze the technique (or method) properties, and when accompanied with other selection criteria, can be used for estimating the cost of learning the technique and the relative complexity of a technique compared to others. To demonstrate the applicability of the proposed metrics, we have applied them to 34 techniques and 15 methods.

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques can successfully predict the magnitude of the mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex problems in mantle dynamics by employing deep learning algorithms for estimation of mantle properties such as viscosity, elastic parameters, and thermal and chemical anomalies.

Analyzing the Effects of Various Concept Mapping Techniques on Learning Achievement under Different Learning Styles

ERIC Educational Resources Information Center

Chiou, Chei-Chang; Lee, Li-Tze; Tien, Li-Chu; Wang, Yu-Min

2017-01-01

This study explored the effectiveness of different concept mapping techniques on the learning achievement of senior accounting students and whether achievements attained using various techniques are affected by different learning styles. The techniques are computer-assisted construct-by-self-concept mapping (CACSB), computer-assisted…

Using Active Learning to Identify Health Information Technology Related Patient Safety Events.

PubMed

Fong, Allan; Howe, Jessica L; Adams, Katharine T; Ratwani, Raj M

2017-01-18

The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.

The role of information and communication technology in developing smart education

NASA Astrophysics Data System (ADS)

Roslina; Zarlis, Muhammad; Mawengkang, Herman; Sembiring, R. W.

2017-09-01

The right to get a proper education for every citizen had been regulated by the government, but not all citizens have the same opportunity. This is due to the other factors in the nation's infrastructure, Frontier, Outermost, and Disadvantaged (3T) which have not beenaccomodatedto access information and communication technology (ICT), and the ideal learning environment in order to pursue knowledge. This condition could be achieved by reforming higher education. Such reforms include the provision of educational services in the form of a flexible learner-oriented, and to change the curriculum with market based.These changes would include the provision of lecturers, professors, and professional teaching force. Another important effort is to update the quality of higher education with resource utilization. This paper proposes a new education business model to realize the Smart Education (SE), with an orientation on the proven skills and competitive.SE is the higher education system to optimize output (outcome) learning with combine individual learning and collaboration techniques based network system, informal practice learning and formal theory. UtilizingICT resources can improve the quality and access to higher education in supporting activities of higher education.This paper shows that ICT resources can support virtual connected with the use of shared resources, such as resource of information, learning resources, computing resources, and human resources.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Accurate monitoring leads to effective control and greater learning of patient education materials.

PubMed

Rawson, Katherine A; O'Neil, Rochelle; Dunlosky, John

2011-09-01

Effective management of chronic diseases (e.g., diabetes) can depend on the extent to which patients can learn and remember disease-relevant information. In two experiments, we explored a technique motivated by theories of self-regulated learning for improving people's learning of information relevant to managing a chronic disease. Materials were passages from patient education booklets on diabetes from NIDDK. Session 1 included an initial study trial, Session 2 included self-regulated restudy, and Session 3 included a final memory test. The key manipulation concerned the kind of support provided for self-regulated learning during Session 2. In Experiment 1, participants either were prompted to self-test and then evaluate their learning before selecting passages to restudy, were shown the prompt questions but did not overtly self-test or evaluate learning prior to selecting passages, or were not shown any prompts and were simply given the menu for selecting passages to restudy. Participants who self-tested and evaluated learning during Session 2 had a small but significant advantage over the other groups on the final test. Secondary analyses provided evidence that the performance advantage may have been modest because of inaccurate monitoring. Experiment 2 included a group who also self-tested but who evaluated their learning using idea-unit judgments (i.e., by checking their responses against a list of key ideas from the correct response). Participants who self-tested and made idea-unit judgments exhibited a sizable advantage on final test performance. Secondary analyses indicated that the performance advantage was attributable in part to more accurate monitoring and more effective self-regulated learning. An important practical implication is that learning of patient education materials can be enhanced by including appropriate support for learners' self-regulatory processes. (c) 2011 APA, all rights reserved.

Novel associative-memory-based self-learning neurocontrol model

NASA Astrophysics Data System (ADS)

Chen, Ke

1992-09-01

Intelligent control is an important field of AI application, which is closely related to machine learning, and the neurocontrol is a kind of intelligent control that controls actions of a physical system or a plant. Linear associative memory model is a good analytic tool for artificial neural networks. In this paper, we present a novel self-learning neurocontrol on the basis of the linear associative memory model to support intelligent control. Using our self-learning neurocontrol model, the learning process is viewed as an extension of one of J. Piaget's developmental stages. After a particular linear associative model developed by us is presented, a brief introduction to J. Piaget's cognitive theory is described as the basis of our self-learning style control. It follows that the neurocontrol model is presented, which usually includes two learning stages, viz. primary learning and high-level learning. As a demonstration of our neurocontrol model, an example is also presented with simulation techniques, called that `bird' catches an aim. The tentative experimental results show that the learning and controlling performance of this approach is surprisingly good. In conclusion, future research is pointed out to improve our self-learning neurocontrol model and explore other areas of application.

Extending human potential in a technical learning environment

NASA Astrophysics Data System (ADS)

Fielden, Kay A.

This thesis is a report of a participatory inquiry process looking at enhancing the learning process in a technical academic field in high education by utilising tools and techniques which go beyond the rational/logical, intellectual domain in a functional, objective world. By empathising with, nurturing and sustaining the whole person, and taking account of past patterning as well as future visions including technological advances to augment human awareness, the scene is set for depth learning. Depth learning in a tertiary environment can only happen as a result of the dynamic that exists between the dominant, logical/rational, intellectual paradigm and the experiential extension of the boundaries surrounding this domain. Any experiences which suppress the full, holistic expression of our being alienate us from the fullness of the expression and hence from depth learning. Depth learning is indicated by intrinsic motivation, which is more likely to occur in a trusting and supporting environment. The research took place within a systemic intellectual framework, where emergence is the prime characteristic used to evaluate results.

Machine learning and data science in soft materials engineering

NASA Astrophysics Data System (ADS)

Ferguson, Andrew L.

2018-01-01

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ‘de-jargonizing’ data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Machine learning and data science in soft materials engineering.

PubMed

Ferguson, Andrew L

2018-01-31

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Embedding learning from adverse incidents: a UK case study.

PubMed

Eshareturi, Cyril; Serrant, Laura

2017-04-18

Purpose This paper reports on a regionally based UK study uncovering what has worked well in learning from adverse incidents in hospitals. The purpose of this paper is to review the incident investigation methodology used in identifying strengths or weaknesses and explore the use of a database as a tool to embed learning. Design/methodology/approach Documentary examination was conducted of all adverse incidents reported between 1 June 2011 and 30 June 2012 by three UK National Health Service hospitals. One root cause analysis report per adverse incident for each individual hospital was sent to an advisory group for a review. Using terms of reference supplied, the advisory group feedback was analysed using an inductive thematic approach. The emergent themes led to the generation of questions which informed seven in-depth semi-structured interviews. Findings "Time" and "work pressures" were identified as barriers to using adverse incident investigations as tools for quality enhancement. Methodologically, a weakness in approach was that no criteria influenced the techniques which were used in investigating adverse incidents. Regarding the sharing of learning, the use of a database as a tool to embed learning across the region was not supported. Practical implications Softer intelligence from adverse incident investigations could be usefully shared between hospitals through a regional forum. Originality/value The use of a database as a tool to facilitate the sharing of learning from adverse incidents across the health economy is not supported.

Deep learning of unsteady laminar flow over a cylinder

NASA Astrophysics Data System (ADS)

Lee, Sangseung; You, Donghyun

2017-11-01

Unsteady flow over a circular cylinder is reconstructed using deep learning with a particular emphasis on elucidating the potential of learning the solution of the Navier-Stokes equations. A deep neural network (DNN) is employed for deep learning, while numerical simulations are conducted to produce training database. Instantaneous and mean flow fields which are reconstructed by deep learning are compared with the simulation results. Fourier transform of flow variables has been conducted to validate the ability of DNN to capture both amplitudes and frequencies of flow motions. Basis decomposition of learned flow is performed to understand the underlying mechanisms of learning flow through DNN. The present study suggests that a deep learning technique can be utilized for reconstruction and, potentially, for prediction of fluid flow instead of solving the Navier-Stokes equations. This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korea government(Ministry of Science, ICT and Future Planning) (No. 2014R1A2A1A11049599, No. 2015R1A2A1A15056086, No. 2016R1E1A2A01939553).

Manipulatives Implementation For Supporting Learning Of Mathematics For Prospective Teachers

NASA Astrophysics Data System (ADS)

Sulistyaningsih, D.; Mawarsari, V. D.; Hidayah, I.; Dwijanto

2017-04-01

Manipulatives are needed by teachers to facilitate students understand of mathematics which is abstract. As a prospective mathematics teacher, the student must have good skills in making manipulatives. Aims of this study is to describe the implementation of learning courses of manipulative workshop in mathematics education courses by lecturer at Universitas Muhammadiyah Semarang which includes the preparation of learning, general professional ability, the professional capacity specifically, ability of self-development, development class managing, planning and implementation of learning, a way of delivering the material, and evaluation of learning outcomes. Data collection techniques used were questionnaires, interviews, and observation. The research instrument consisted of a questionnaire sheet, sheet observation and interview guides. Validity is determined using data triangulation and triangulation methods. Data were analyzed using an interactive model. The results showed that the average value of activities in preparation for learning, fosters capabilities of general professional, specialized professional, self-development, manage the classroom, implementing the learning, how to deliver the material, and how to evaluate learning outcomes are 79%, 73%, 67%, 75%, 83%, 72%, 64%, and 54%, respectively

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data.

PubMed

Li, Yachun; Charalampaki, Patra; Liu, Yong; Yang, Guang-Zhong; Giannarou, Stamatia

2018-06-13

Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures. The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods. We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%. This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

PubMed

Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

2017-08-15

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

Big Data Analytics with Datalog Queries on Spark.

PubMed

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2016-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.

Big Data Analytics with Datalog Queries on Spark

PubMed Central

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2017-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics. PMID:28626296

Ecological footprint model using the support vector machine technique.

PubMed

Ma, Haibo; Chang, Wenjuan; Cui, Guangbai

2012-01-01

The per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It aims to quantify the Earth's biological resources required to support human activity. In this paper, we summarize relevant previous literature, and present five factors that influence per capita EF. These factors are: National gross domestic product (GDP), urbanization (independent of economic development), distribution of income (measured by the Gini coefficient), export dependence (measured by the percentage of exports to total GDP), and service intensity (measured by the percentage of service to total GDP). A new ecological footprint model based on a support vector machine (SVM), which is a machine-learning method based on the structural risk minimization principle from statistical learning theory was conducted to calculate the per capita EF of 24 nations using data from 123 nations. The calculation accuracy was measured by average absolute error and average relative error. They were 0.004883 and 0.351078% respectively. Our results demonstrate that the EF model based on SVM has good calculation performance.

An Experimental, Hands-on Approach to Epithelial Ion Transport: A Simple Technique for Introducing Students to Ion Transport in Epithelia

ERIC Educational Resources Information Center

Bagdadi, Andrea; Orona, Nadia; Fernandez, Eugenio; Altamirano, Anibal; Amorena, Carlos

2010-01-01

We have realized that our Biology undergraduate students learn biological concepts as established truths without awareness of the body of experimental evidence supporting the emerging models as usually presented in handbooks and texts in general. Therefore, we have implemented a laboratory practice in our course of Physiology and Biophysics, aimed…

The Concept of C2 Communication and Information Support

DTIC Science & Technology

2004-06-01

communication and information literacy , • Sensors: technology and systematic development as a branch, • Military prognosis research (combat models...intelligence, • Visualization of actions, suitable forms of information presentation, • Techniques of learning CIS users communication and information ... literacy , • Sensors: technology and systematic development as a branch, • Military prognosis research (combat models), • Man - machine interface. CISu

Integration of a Zebrafish Research Project into a Molecular Biology Course to Support Critical Thinking and Course Content Goals

ERIC Educational Resources Information Center

Felzien, Lisa K.

2016-01-01

Engaging undergraduates in research is essential for teaching them to think like scientists, and it has become a desired component of classroom and laboratory instruction. Research projects that span an entire semester expose students to a variety of concepts and techniques and allow students to use experiments to learn scientific principles,…

Public Relations for School Library Media Programs: 500 Ways To Influence People and Win Friends for Your School Library Media Center.

ERIC Educational Resources Information Center

Flowers, Helen F.

This informative how-to guide explains public relations strategies and the audiences they target, with tips for customizing these tactics for particular libraries. Helpful techniques are recommended for promoting the use of library media services by students, faculty, building administrators, and school support staff. Readers will also learn how…

A Web-Based Learning Support System for Inquiry-Based Learning

NASA Astrophysics Data System (ADS)

Kim, Dong Won; Yao, Jingtao

The emergence of the Internet and Web technology makes it possible to implement the ideals of inquiry-based learning, in which students seek truth, information, or knowledge by questioning. Web-based learning support systems can provide a good framework for inquiry-based learning. This article presents a study on a Web-based learning support system called Online Treasure Hunt. The Web-based learning support system mainly consists of a teaching support subsystem, a learning support subsystem, and a treasure hunt game. The teaching support subsystem allows instructors to design their own inquiry-based learning environments. The learning support subsystem supports students' inquiry activities. The treasure hunt game enables students to investigate new knowledge, develop ideas, and review their findings. Online Treasure Hunt complies with a treasure hunt model. The treasure hunt model formalizes a general treasure hunt game to contain the learning strategies of inquiry-based learning. This Web-based learning support system empowered with the online-learning game and founded on the sound learning strategies furnishes students with the interactive and collaborative student-centered learning environment.

ISS Logistics Hardware Disposition and Metrics Validation

NASA Technical Reports Server (NTRS)

Rogers, Toneka R.

2010-01-01

I was assigned to the Logistics Division of the International Space Station (ISS)/Spacecraft Processing Directorate. The Division consists of eight NASA engineers and specialists that oversee the logistics portion of the Checkout, Assembly, and Payload Processing Services (CAPPS) contract. Boeing, their sub-contractors and the Boeing Prime contract out of Johnson Space Center, provide the Integrated Logistics Support for the ISS activities at Kennedy Space Center. Essentially they ensure that spares are available to support flight hardware processing and the associated ground support equipment (GSE). Boeing maintains a Depot for electrical, mechanical and structural modifications and/or repair capability as required. My assigned task was to learn project management techniques utilized by NASA and its' contractors to provide an efficient and effective logistics support infrastructure to the ISS program. Within the Space Station Processing Facility (SSPF) I was exposed to Logistics support components, such as, the NASA Spacecraft Services Depot (NSSD) capabilities, Mission Processing tools, techniques and Warehouse support issues, required for integrating Space Station elements at the Kennedy Space Center. I also supported the identification of near-term ISS Hardware and Ground Support Equipment (GSE) candidates for excessing/disposition prior to October 2010; and the validation of several Logistics Metrics used by the contractor to measure logistics support effectiveness.

What We Do and Do Not Know about Teaching Medical Image Interpretation.

PubMed

Kok, Ellen M; van Geel, Koos; van Merriënboer, Jeroen J G; Robben, Simon G F

2017-01-01

Educators in medical image interpretation have difficulty finding scientific evidence as to how they should design their instruction. We review and comment on 81 papers that investigated instructional design in medical image interpretation. We distinguish between studies that evaluated complete offline courses and curricula, studies that evaluated e-learning modules, and studies that evaluated specific educational interventions. Twenty-three percent of all studies evaluated the implementation of complete courses or curricula, and 44% of the studies evaluated the implementation of e-learning modules. We argue that these studies have encouraging results but provide little information for educators: too many differences exist between conditions to unambiguously attribute the learning effects to specific instructional techniques. Moreover, concepts are not uniformly defined and methodological weaknesses further limit the usefulness of evidence provided by these studies. Thirty-two percent of the studies evaluated a specific interventional technique. We discuss three theoretical frameworks that informed these studies: diagnostic reasoning, cognitive schemas and study strategies. Research on diagnostic reasoning suggests teaching students to start with non-analytic reasoning and subsequently applying analytic reasoning, but little is known on how to train non-analytic reasoning. Research on cognitive schemas investigated activities that help the development of appropriate cognitive schemas. Finally, research on study strategies supports the effectiveness of practice testing, but more study strategies could be applicable to learning medical image interpretation. Our commentary highlights the value of evaluating specific instructional techniques, but further evidence is required to optimally inform educators in medical image interpretation.

Integrating machine learning techniques and high-resolution imagery to generate GIS-ready information for urban water consumption studies

NASA Astrophysics Data System (ADS)

Wolf, Nils; Hof, Angela

2012-10-01

Urban sprawl driven by shifts in tourism development produces new suburban landscapes of water consumption on Mediterranean coasts. Golf courses, ornamental, 'Atlantic' gardens and swimming pools are the most striking artefacts of this transformation, threatening the local water supply systems and exacerbating water scarcity. In the face of climate change, urban landscape irrigation is becoming increasingly important from a resource management point of view. This paper adopts urban remote sensing towards a targeted mapping approach using machine learning techniques and highresolution satellite imagery (WorldView-2) to generate GIS-ready information for urban water consumption studies. Swimming pools, vegetation and - as a subgroup of vegetation - turf grass are extracted as important determinants of water consumption. For image analysis, the complex nature of urban environments suggests spatial-spectral classification, i.e. the complementary use of the spectral signature and spatial descriptors. Multiscale image segmentation provides means to extract the spatial descriptors - namely object feature layers - which can be concatenated at pixel level to the spectral signature. This study assesses the value of object features using different machine learning techniques and amounts of labeled information for learning. The results indicate the benefit of the spatial-spectral approach if combined with appropriate classifiers like tree-based ensembles or support vector machines, which can handle high dimensionality. Finally, a Random Forest classifier was chosen to deliver the classified input data for the estimation of evaporative water loss and net landscape irrigation requirements.

Recontextualizing Dance Skills: Overcoming Impediments to Motor Learning and Expressivity in Ballet Dancers

PubMed Central

Karin, Janet

2016-01-01

The process of transmitting ballet’s complex technique to young dancers can interfere with the innate processes that give rise to efficient, expressive and harmonious movement. With the intention of identifying possible solutions, this article draws on research across the fields of neurology, psychology, motor learning, and education, and considers their relevance to ballet as an art form, a technique, and a training methodology. The integration of dancers’ technique and expressivity is a core theme throughout the paper. A brief outline of the historical development of ballet’s aesthetics and training methods leads into factors that influence dancers’ performance. An exploration of the role of the neuromotor system in motor learning and the acquisition of expert skills reveals the roles of sensory awareness, imagery, and intention in cuing efficient, expressive movement. It also indicates potentially detrimental effects of conscious muscle control, explicit learning and persistent naïve beliefs. Finally, the paper presents a new theory regarding the acquisition of ballet skills. Recontextualization theory proposes that placing a problematic task within a new context may engender a new conceptual approach and/or sensory intention, and hence the genesis of new motor programs; and that these new programs may lead to performance that is more efficient, more rewarding for the dancer, more pleasing aesthetically, and more expressive. From an anecdotal point of view, this theory appears to be supported by the progress of many dancers at various stages of their dancing lives. PMID:27047437

Recontextualizing Dance Skills: Overcoming Impediments to Motor Learning and Expressivity in Ballet Dancers.

PubMed

Karin, Janet

2016-01-01

The process of transmitting ballet's complex technique to young dancers can interfere with the innate processes that give rise to efficient, expressive and harmonious movement. With the intention of identifying possible solutions, this article draws on research across the fields of neurology, psychology, motor learning, and education, and considers their relevance to ballet as an art form, a technique, and a training methodology. The integration of dancers' technique and expressivity is a core theme throughout the paper. A brief outline of the historical development of ballet's aesthetics and training methods leads into factors that influence dancers' performance. An exploration of the role of the neuromotor system in motor learning and the acquisition of expert skills reveals the roles of sensory awareness, imagery, and intention in cuing efficient, expressive movement. It also indicates potentially detrimental effects of conscious muscle control, explicit learning and persistent naïve beliefs. Finally, the paper presents a new theory regarding the acquisition of ballet skills. Recontextualization theory proposes that placing a problematic task within a new context may engender a new conceptual approach and/or sensory intention, and hence the genesis of new motor programs; and that these new programs may lead to performance that is more efficient, more rewarding for the dancer, more pleasing aesthetically, and more expressive. From an anecdotal point of view, this theory appears to be supported by the progress of many dancers at various stages of their dancing lives.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Changing teaching techniques and adapting new technologies to improve student learning in an introductory meteorology and climate course

NASA Astrophysics Data System (ADS)

Cutrim, E. M.; Rudge, D.; Kits, K.; Mitchell, J.; Nogueira, R.

2006-06-01

Responding to the call for reform in science education, changes were made in an introductory meteorology and climate course offered at a large public university. These changes were a part of a larger project aimed at deepening and extending a program of science content courses that model effective teaching strategies for prospective middle school science teachers. Therefore, revisions were made to address misconceptions about meteorological phenomena, foster deeper understanding of key concepts, encourage engagement with the text, and promote inquiry-based learning. Techniques introduced include: use of a flash cards, student reflection questionnaires, writing assignments, and interactive discussions on weather and forecast data using computer technology such as Integrated Data Viewer (IDV). The revision process is described in a case study format. Preliminary results (self-reflection by the instructor, surveys of student opinion, and measurements of student achievement), suggest student learning has been positively influenced. This study is supported by three grants: NSF grant No. 0202923, the Unidata Equipment Award, and the Lucia Harrison Endowment Fund.

Effective and ineffective supervision in postgraduate dental education: a qualitative study.

PubMed

Subramanian, J; Anderson, V R; Morgaine, K C; Thomson, W M

2013-02-01

Research suggests that students' perceptions should be considered in any discussion of their education, but there has been no systematic examination of New Zealand postgraduate dental students' learning experiences. This study aimed to obtain in-depth qualitative insights into student and graduate perceptions of effective and ineffective learning in postgraduate dental education. Data were collected in 2010 using semi-structured individual interviews. Participants included final-year students and graduates of the University of Otago Doctor of Clinical Dentistry programme. Using the Critical Incident Technique, participants were asked to describe atleast one effective and one ineffective learning experience in detail. Interview transcripts were analysed using a general inductive approach. Broad themes which emerged included supervisory approaches, characteristics of the learning process, and the physical learning environment. This paper considers students' and graduates' perceptions of postgraduate supervision in dentistry as it promotes or precludes effective learning. Effective learning was associated by participants with approachable and supportive supervisory practices, and technique demonstrations accompanied by explicit explanations. Ineffective learning was associated with minimal supervisor demonstrations and guidance (particularly when beginning postgraduate study), and aggressive, discriminatory and/or culturally insensitive supervisory approaches. Participants' responses provided rich, in-depth insights into their reflections and understandings of effective and ineffective approaches to supervision as it influenced their learning in the clinical and research settings. These findings provide a starting point for the development of curriculum and supervisory practices, enhancement of supervisory and mentoring approaches, and the design of continuing education programmes for supervisors at an institutional level. Additionally, these findings might also stimulate topics for reflection and discussion amongst dental educators and administrators more broadly. © 2012 John Wiley & Sons A/S.

Active learning-based information structure analysis of full scientific articles and two applications for biomedical literature review.

PubMed

Guo, Yufan; Silins, Ilona; Stenius, Ulla; Korhonen, Anna

2013-06-01

Techniques that are capable of automatically analyzing the information structure of scientific articles could be highly useful for improving information access to biomedical literature. However, most existing approaches rely on supervised machine learning (ML) and substantial labeled data that are expensive to develop and apply to different sub-fields of biomedicine. Recent research shows that minimal supervision is sufficient for fairly accurate information structure analysis of biomedical abstracts. However, is it realistic for full articles given their high linguistic and informational complexity? We introduce and release a novel corpus of 50 biomedical articles annotated according to the Argumentative Zoning (AZ) scheme, and investigate active learning with one of the most widely used ML models-Support Vector Machines (SVM)-on this corpus. Additionally, we introduce two novel applications that use AZ to support real-life literature review in biomedicine via question answering and summarization. We show that active learning with SVM trained on 500 labeled sentences (6% of the corpus) performs surprisingly well with the accuracy of 82%, just 2% lower than fully supervised learning. In our question answering task, biomedical researchers find relevant information significantly faster from AZ-annotated than unannotated articles. In the summarization task, sentences extracted from particular zones are significantly more similar to gold standard summaries than those extracted from particular sections of full articles. These results demonstrate that active learning of full articles' information structure is indeed realistic and the accuracy is high enough to support real-life literature review in biomedicine. The annotated corpus, our AZ classifier and the two novel applications are available at http://www.cl.cam.ac.uk/yg244/12bioinfo.html

Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging

PubMed Central

Gholami, Behnood; Tannenbaum, Allen R.

2011-01-01

Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent “pure” facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners. PMID:20172803

Nursing and midwifery students' experiences with the course of infertility and assisted reproductive techniques: A focus group study from Turkey.

PubMed

Isbir, Gozde Gokçe; Ozan, Yeter Durgun

2018-01-01

Nurses and midwifes without sufficient knowledge of infertilitare not likely to provide counseling and support for people suffering from infertility. This study aimed to evaluate nursing and midwifery students' experiences with the Course on Infertility and Assisted Reproductive Techniques. Our study had a qualitative descriptive design. Total number of the participants was 75. The analysis revealed five primary themes and twenty-one sub-themes. The themes were (1) action, (2) learner centered method, (3) interaction, (4) nursing competencies, and (5) evaluation. The active learning techniques enabled the students to retrieve the knowledge that they obtained for a long time, contributed to social and cultural development and improved skills required for selfevaluation, communication and leadership, enhanced critical thinking, skills increased motivation and satisfaction and helped with knowledge integration. Infertility is a biopsychosocial condition, and it may be difficult for students to understand what infertile individuals experience. The study revealed that active learning techniques enabled the students to acquire not only theoretical knowledge but also an emotional and psychosocial viewpoint and attitude regarding infertility. The content of an infertility course should be created in accordance with changes in the needs of a given society and educational techniques. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artificial intelligence in healthcare: past, present and future.

PubMed

Jiang, Fei; Jiang, Yong; Zhi, Hui; Dong, Yi; Li, Hao; Ma, Sufeng; Wang, Yilong; Dong, Qiang; Shen, Haipeng; Wang, Yongjun

2017-12-01

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Integration of Video-Based Demonstrations to Prepare Students for the Organic Chemistry Laboratory

NASA Astrophysics Data System (ADS)

Nadelson, Louis S.; Scaggs, Jonathan; Sheffield, Colin; McDougal, Owen M.

2015-08-01

Consistent, high-quality introductions to organic chemistry laboratory techniques effectively and efficiently support student learning in the organic chemistry laboratory. In this work, we developed and deployed a series of instructional videos to communicate core laboratory techniques and concepts. Using a quasi-experimental design, we tested the videos in five traditional laboratory experiments by integrating them with the standard pre-laboratory student preparation presentations and instructor demonstrations. We assessed the influence of the videos on student laboratory knowledge and performance, using sections of students who did not view the videos as the control. Our analysis of pre-quizzes revealed the control group had equivalent scores to the treatment group, while the post-quiz results show consistently greater learning gains for the treatment group. Additionally, the students who watched the videos as part of their pre-laboratory instruction completed their experiments in less time.

Artificial intelligence in healthcare: past, present and future

PubMed Central

Jiang, Fei; Jiang, Yong; Zhi, Hui; Dong, Yi; Li, Hao; Ma, Sufeng; Wang, Yilong; Dong, Qiang; Shen, Haipeng; Wang, Yongjun

2017-01-01

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. PMID:29507784

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases.

PubMed

Tulay, Emine Elif; Metin, Barış; Tarhan, Nevzat; Arıkan, Mehmet Kemal

2018-06-01

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Deep learning with convolutional neural network in radiology.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

(Machine-)Learning to analyze in vivo microscopy: Support vector machines.

PubMed

Wang, Michael F Z; Fernandez-Gonzalez, Rodrigo

2017-11-01

The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and subcellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches present several challenges. In addition to unprecedented requirements for data storage, the analysis of high resolution, time-lapse images is too complex to be done manually. Machine learning techniques are ideally suited for the (semi-)automated analysis of multidimensional image data. In particular, support vector machines (SVMs), have emerged as an efficient method to analyze microscopy images obtained from animals. Here, we discuss the use of SVMs to analyze in vivo microscopy data. We introduce the mathematical framework behind SVMs, and we describe the metrics used by SVMs and other machine learning approaches to classify image data. We discuss the influence of different SVM parameters in the context of an algorithm for cell segmentation and tracking. Finally, we describe how the application of SVMs has been critical to study protein localization in yeast screens, for lineage tracing in C. elegans, or to determine the developmental stage of Drosophila embryos to investigate gene expression dynamics. We propose that SVMs will become central tools in the analysis of the complex image data that novel microscopy modalities have made possible. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman. Copyright © 2017 Elsevier B.V. All rights reserved.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. ©Jose Juan Dominguez Veiga, Martin O'Reilly, Darragh Whelan, Brian Caulfield, Tomas E Ward. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.08.2017.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. PMID:28778851

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

PubMed Central

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. PMID:26065018

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

PubMed

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

The Effects of Practice-Based Training on Graduate Teaching Assistants’ Classroom Practices

PubMed Central

Becker, Erin A.; Easlon, Erin J.; Potter, Sarah C.; Guzman-Alvarez, Alberto; Spear, Jensen M.; Facciotti, Marc T.; Igo, Michele M.; Singer, Mitchell; Pagliarulo, Christopher

2017-01-01

Evidence-based teaching is a highly complex skill, requiring repeated cycles of deliberate practice and feedback to master. Despite existing well-characterized frameworks for practice-based training in K–12 teacher education, the major principles of these frameworks have not yet been transferred to instructor development in higher educational contexts, including training of graduate teaching assistants (GTAs). We sought to determine whether a practice-based training program could help GTAs learn and use evidence-based teaching methods in their classrooms. We implemented a weekly training program for introductory biology GTAs that included structured drills of techniques selected to enhance student practice, logic development, and accountability and reduce apprehension. These elements were selected based on their previous characterization as dimensions of active learning. GTAs received regular performance feedback based on classroom observations. To quantify use of target techniques and levels of student participation, we collected and coded 160 h of video footage. We investigated the relationship between frequency of GTA implementation of target techniques and student exam scores; however, we observed no significant relationship. Although GTAs adopted and used many of the target techniques with high frequency, techniques that enforced student participation were not stably adopted, and their use was unresponsive to formal feedback. We also found that techniques discussed in training, but not practiced, were not used at quantifiable frequencies, further supporting the importance of practice-based training for influencing instructional practices. PMID:29146664

The Analysis of Physics Learning in Senior High School of Semarang Based on The Scientific Approach and Assessment

NASA Astrophysics Data System (ADS)

Hardyanti, R. C.; Hartono; Fianti

2018-03-01

Physics Learning in Curriculum of 2013 is closely related to the implementation of scientific approach and authentic assessment in learning. This study aims to analyze the implementation of scientific approaches and authentic assessment in physics learning, as well as to analyze the constraints of scientific approach and authentic assessment in physics learning. The data collection techniques used in this study are questionnaires, observations, interviews, and documentation. The calculation results used are percentage techniques and analyzed by using qualitative descriptive approach. Based on the results of research and discussion, the implementation of physics learning based on the scientific approach goes well with the percentage of 84.60%. Physical learning activity based on authentic assessment also goes well with the percentage of 88%. The results of the percentage of scientific approaches and authentic assessment approaches are less than 100%. It shows that there are obstacles to the implementation of the scientific approach and the constraints of authentic assessment. The obstacles to the implementation of scientific approach include time, heavy load of material, input or ability of learners, the willingness of learners in asking questions, laboratory support, and the ability of students to process data. While the obstacles to the implementation of authentic assessment include the limited time for carrying out of authentic assessment, the components of the criteria in carrying out the authentic assessment, the lack of discipline in administering the administration, the difficulty of changing habits in carrying out the assessment from traditional assessment to the authentic assessment, the obstacle to process the score in accordance with the format Curriculum of 2013.

Exploring the use of student-led simulated practice learning in pre-registration nursing programmes.

PubMed

Brown, Jo; Collins, Guy; Gratton, Olivia

2017-09-20

Simulated practice learning is used in pre-registration nursing programmes to replicate situations that nursing students are likely to encounter in clinical practice, but in a safe and protected academic environment. However, lecturer-led simulated practice learning has been perceived as detached from contemporary nursing practice by some nursing students. Therefore, a pilot project was implemented in the authors' university to explore the use of student-led simulated practice learning and its potential benefits for nursing students. To evaluate the effectiveness of student-led simulated practice learning in pre-registration nursing programmes. The authors specifically wanted to: enhance the students' skills; improve their critical thinking and reflective strategies; and develop their leadership and management techniques. A literature review was undertaken to examine the evidence supporting student-led simulated practice learning. A skills gap analysis was then conducted with 35 third-year nursing students to identify their learning needs, from which suitable simulated practice learning scenarios and sessions were developed and undertaken. These sessions were evaluated using debriefs following each of the sessions, as well as informal discussions with the nursing students. The pilot project identified that student-led simulated learning: developed nursing students' ability to plan and facilitate colleagues' practice learning; enabled nursing students to develop their mentoring skills; reinforced the nursing students' self-awareness, which contributed to their personal development; and demonstrated the importance of peer feedback and support through the debriefs. Challenges included overcoming some students' resistance to the project and that some lecturers were initially concerned that nursing students may not have the clinical expertise to lead the simulated practice learning sessions effectively. This pilot project has demonstrated how student-led simulated practice learning sessions could be used to engage nursing students as partners in their learning, enhance their knowledge and skills, and promote self-directed learning. ©2012 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

Expanding the Caring Lens: Nursing and Medical Students Reflecting on Images of Older People.

PubMed

Brand, Gabrielle; Miller, Karen; Saunders, Rosemary; Dugmore, Helen; Etherton-Beer, Christopher

2016-01-01

In changing higher education environments, health profession's educators have been increasingly challenged to prepare future health professionals to care for aging populations. This article reports on an exploratory, mixed-method research study that used an innovative photo-elicitation technique and interprofessional small-group work in the classroom to enhance the reflective learning experience of medical and nursing students. Data were collected from pre- and postquestionnaires and focus groups to explore shifts in perceptions toward older persons following the reflective learning session. The qualitative data revealed how using visual images of older persons provides a valuable learning space for reflection. Students found meaning in their own learning by creating shared storylines that challenged their perceptions of older people and themselves as future health professionals. These data support the use of visual methodologies to enhance engagement, reflection, and challenge students to explore and deepen their understanding in gerontology.

[Multifamily therapy in children with learning disabilities].

PubMed

Retzlaff, Rüdiger; Brazil, Susanne; Goll-Kopka, Andrea

2008-01-01

Multifamily therapy is an evidence-based method used in the treatment and prevention of severe psychiatric disorders, behavioral problems and physical illnesses in children, adolescents and adults. For preventive family-oriented work with children with learning disorders there is a lack of therapeutic models. This article presents results from an innovative pilot project--multiple family groups for families with a learning disabled child of primary school age (six to eleven years old). Based on a systemic approach, this resource-oriented program integrates creative, activity-based interventions and group therapy techniques and conveys a comprehensive understanding of the challenges associated with learning disorders. Because of the pilot character of the study and the small sample size, the results have to be interpreted with care. The results do however clearly support the wider implementation and evaluation of the program in child guidance clinics, social-pediatric centers, as well as child and adolescent clinics and schools.

Teaching Inquiry using NASA Earth-System Science: Preparing Pre- and Inservice K-12 Educators to Use Authentic Inquiry in the Classroom

NASA Astrophysics Data System (ADS)

Ellis, T. D.; Tebockhorst, D.

2012-12-01

Teaching Inquiry using NASA Earth-System Science (TINES) is a comprehensive program to train and support pre-service and in-service K-12 teachers, and to provide them with an opportunity to use NASA Earth Science mission data and Global Learning and Observations to Benefit the Environment (GLOBE) observations to incorporate scientific inquiry-based learning in the classroom. It uses an innovative blended-learning professional development approach that combines a peer-reviewed pedagogical technique called backward-faded scaffolding (BFS), which provides a more natural entry path to understanding the scientific process, with pre-workshop online content learning and in-situ and online data resources from NASA and GLOBE. This presentation will describe efforts to date, share our impressions and evaluations, and discuss the effectiveness of the BFS approach to both professional development and classroom pedagogy.

University Students with Dyslexia: A Qualitative Exploratory Study of Learning Practices, Challenges and Strategies.

PubMed

MacCullagh, Lois; Bosanquet, Agnes; Badcock, Nicholas A

2017-02-01

People with dyslexia are vastly under-represented in universities (Katusic et al., , Richardson & Wydell, ; Stampoltzis & Polychronopoulou, ). This situation is of concern for modern societies that value social justice. This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success. Thirteen students with dyslexia and 20 non-dyslexic peers were interviewed about their university learning experiences using a semi-structured qualitative approach. Students with dyslexia described engaging in learning activities intensively, frequently and strategically. They reported challenges and strengths relating to study skills, lectures, assessments, technology and support services. They also described helpful strategies including self-directed adaptive techniques, provisions from lecturers and assistance from the university. These findings suggest that students with dyslexia experience broad challenges at university, but helpful strategies may be available. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A Measurement of Vocational Education’s Student Satisfaction in Learning Electronic Appliance Repair and Maintenance Course with CCTV Trainer Kit

NASA Astrophysics Data System (ADS)

Rakhmawati, Lusia; Irwansyah Febriyanto, Fariz

2018-04-01

This study aimed to investigate satisfaction levels, among of the eleventh-grade students majoring in audio-video technique, towards the instructional media CCTV trainer kit: The CCTV Prototype and job sheet on to and analyzed perspectives of the practical approach to learning using CCTV prototype to improve installation skills in the electronic appliance repair and maintenance class. Survey questionnaires and instructional media were applied to data collection. The students’ satisfaction towards the instructional media in teaching and learning process was presented in positive responses, 91.94 % satisfied. The finding reveals that the instructional media using trainer could better to overcome the need a practical approach to learning. Furthermore, using trainer kit media creates an environment where students can support each other and receive feedback from their peers. They performed practice activities that help them apply the new information from the steps on the job sheet.

Is it worth changing pattern recognition methods for structural health monitoring?

NASA Astrophysics Data System (ADS)

Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

2017-05-01

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

White House Maker Faire

NASA Image and Video Library

2014-06-18

President Barack Obama delivers his remarks at the first ever White House Maker Faire, which brings together students, entrepreneurs, and everyday citizens who are using new tools and techniques to launch new businesses, learn vital skills in science, technology, engineering, and math (STEM), and fuel the renaissance in American manufacturing, at the White House, Wednesday, June 18, 2014 in Washington. The President announced new steps the Administration and its partners are taking to support the ability of more Americans, young and old, to have to access to these tools and techniques and brings their ideas to life. Photo Credit: (NASA/Bill Ingalls)

White House Maker Faire

NASA Image and Video Library

2014-06-18

The Maker Faire trailer is seen outside the rose garden during the first ever White House Maker Faire, which brings together students, entrepreneurs, and everyday citizens who are using new tools and techniques to launch new businesses, learn vital skills in science, technology, engineering, and math (STEM), and fuel the renaissance in American manufacturing, at the White House, Wednesday, June 18, 2014 in Washington. The President announced new steps the Administration and its partners are taking to support the ability of more Americans, young and old, to have to access to these tools and techniques and brings their ideas to life. Photo Credit: (NASA/Bill Ingalls)

Example Based Image Analysis and Synthesis

DTIC Science & Technology

1993-11-01

Technology, 1993 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and...Fellowship from the Hughes Aircraft Company. A. Shashua is supported by a McDonnell-Pew postdoctoral fellowship from the department of Brain and...graphics has developed sophis- can be estimated from one or more images and then used ticated 3D models and rendering techniques - effectively to

Scaling Support Vector Machines On Modern HPC Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

You, Yang; Fu, Haohuan; Song, Shuaiwen

2015-02-01

We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.

Visual Purple, the Next Generation Crisis Management Decision Training Tool

DTIC Science & Technology

2001-09-01

talents of professional Hollywood screenwriters during the scripting and writing process of the simulations. Additionally, cinematic techniques learned...cultural, and language experts for research development. Additionally, GTA provides country specific support in script writing and cinematic resources as...The result is an entirely new dimension of realism that traditional exercises often fail to capture. The scenario requires the participant to make the

A Proposed Methodology to Classify Frontier Capital Markets

DTIC Science & Technology

2011-07-31

out of charity, but because it is the surest route to our common good.” -Inaugural Speech by President Barack Obama, Jan 2009 This project...identification, and machine learning. The algorithm consists of a unique binary classifier mechanism that combines three methods: k-Nearest Neighbors ( kNN ...Support Through kNN Ensemble Classification Techniques E. Capital Market Classification Based on Capital Flows and Trading Architecture F

Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

NASA Astrophysics Data System (ADS)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future face of post-industrial cities are revealed. Finally, the advantages and limitations of linking pixels and people by combining AI and machine learning techniques in a multi-scale geosimulation approach are to be discussed.

Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques.

PubMed

Eitrich, T; Kless, A; Druska, C; Meyer, W; Grotendorst, J

2007-01-01

In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

NASA Technical Reports Server (NTRS)

Jani, Yashvant

1993-01-01

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

Learning temporal rules to forecast instability in continuously monitored patients

PubMed Central

Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

2017-01-01

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. PMID:27274020

Is that really my movement? - Students' experiences of a video-supported interactive learning model for movement awareness.

PubMed

Backåberg, Sofia; Gummesson, Christina; Brunt, David; Rask, Mikael

2015-01-01

Healthcare staff and students have a great risk of developing musculoskeletal symptoms. One cause of this is heavy load related work activities such as manual handling, in which the quality of individual work technique may play a major role. Preventive interventions and well-defined educational strategies to support movement awareness and long-lasting movement changes need to be developed. The aim of the present study was to explore nursing students' experiences of a newly developed interactive learning model for movement awareness. The learning model, which is based on a life-world perspective with focus on interpersonal interaction, has been used with 11 undergraduate students from the second and final year. Each student participated in three individual video sessions with a facilitator. Two individual interviews were carried out with each student during the learning process and one interview 12-18 months after the last session. The interviews were audio-recorded and transcribed verbatim, and a phenomenological hermeneutic method inspired by Paul Ricoeur and described by Lindseth and Norberg was used to interpret the interviews and diary notes. The interpretation resulted in three key themes and nine subthemes. The key themes were; "Obtaining better preconditions for bodily awareness," "Experiencing changes in one's own movement," and "Experiencing challenges in the learning process." The interactive learning model entails a powerful and challenging experience that develops movement awareness. The experience of meaningfulness and usefulness emerges increasingly and alternates with a feeling of discomfort. The learning model may contribute to the body of knowledge of well-defined educational strategies in movement awareness and learning in, for example, preventive interventions and ergonomic education. It may also be valuable in other practical learning situations where movement awareness is required.

Physics Laboratory in UEC

NASA Astrophysics Data System (ADS)

Takada, Tohru; Nakamura, Jin; Suzuki, Masaru

All the first-year students in the University of Electro-Communications (UEC) take "Basic Physics I", "Basic Physics II" and "Physics Laboratory" as required subjects; Basic Physics I and Basic Physics II are calculus-based physics of mechanics, wave and oscillation, thermal physics and electromagnetics. Physics Laboratory is designed mainly aiming at learning the skill of basic experimental technique and technical writing. Although 95% students have taken physics in the senior high school, they poorly understand it by connecting with experience, and it is difficult to learn Physics Laboratory in the university. For this reason, we introduced two ICT (Information and Communication Technology) systems of Physics Laboratory to support students'learning and staff's teaching. By using quantitative data obtained from the ICT systems, we can easily check understanding of physics contents in students, and can improve physics education.

Learning Multiple Band-Pass Filters for Sleep Stage Estimation: Towards Care Support for Aged Persons

NASA Astrophysics Data System (ADS)

Takadama, Keiki; Hirose, Kazuyuki; Matsushima, Hiroyasu; Hattori, Kiyohiko; Nakajima, Nobuo

This paper proposes the sleep stage estimation method that can provide an accurate estimation for each person without connecting any devices to human's body. In particular, our method learns the appropriate multiple band-pass filters to extract the specific wave pattern of heartbeat, which is required to estimate the sleep stage. For an accurate estimation, this paper employs Learning Classifier System (LCS) as the data-mining techniques and extends it to estimate the sleep stage. Extensive experiments on five subjects in mixed health confirm the following implications: (1) the proposed method can provide more accurate sleep stage estimation than the conventional method, and (2) the sleep stage estimation calculated by the proposed method is robust regardless of the physical condition of the subject.

Effectiveness of multimedia-supported education in practical sports courses.

PubMed

Leser, Roland; Baca, Arnold; Uhlig, Johannes

2011-01-01

Multimedia-assisted teaching and learning have become standard forms of education. In sports, multimedia material has been used to teach practical aspects of courses, such as motor skills. The main goal of this study is to examine if multimedia technology impacts learning in the field of sport motor skill acquisition. This question was investigated during a practical sports education course involving 35 students who participated in a university soccer class. The whole course was split into two groups: Group A was taught traditionally with no assistance of multimedia and Group B was prepared with multimedia-assisted instructional units. To quantify selected skills of soccer technique and tactic, the test subjects performed a specific passing test and a tactical assessment. Furthermore, a ques-tionnaire was used to assess the subjective impressions of the test subjects. All testing instruments were applied before and after a six-week-long teaching period. A comparison of the gathered data between the two groups resulted in no significant differences, neither concerning the results of the technique test nor concerning the tactic test. However, the results of the ques-tionnaire showed a positive agreement among the participants in the usability and assistance of multimedia for the sports practical course. Considering the reviewed conditions, it can be concluded that the use of multimedia content doesn't affect the learning effects. Key pointsMultimedia-assisted learning showed no positive learning effects on technical skills in soccer.Multimedia-assisted learning showed no positive learning effects on tactical skills in soccer.Students participating in practical sports courses have very good attitudes towards the use of multi-media learning material. This may be considered for motivational effects.

Effectiveness of Multimedia-Supported Education in Practical Sports Courses

PubMed Central

Leser, Roland; Baca, Arnold; Uhlig, Johannes

2011-01-01

Multimedia-assisted teaching and learning have become standard forms of education. In sports, multimedia material has been used to teach practical aspects of courses, such as motor skills. The main goal of this study is to examine if multimedia technology impacts learning in the field of sport motor skill acquisition. This question was investigated during a practical sports education course involving 35 students who participated in a university soccer class. The whole course was split into two groups: Group A was taught traditionally with no assistance of multimedia and Group B was prepared with multimedia-assisted instructional units. To quantify selected skills of soccer technique and tactic, the test subjects performed a specific passing test and a tactical assessment. Furthermore, a ques-tionnaire was used to assess the subjective impressions of the test subjects. All testing instruments were applied before and after a six-week-long teaching period. A comparison of the gathered data between the two groups resulted in no significant differences, neither concerning the results of the technique test nor concerning the tactic test. However, the results of the ques-tionnaire showed a positive agreement among the participants in the usability and assistance of multimedia for the sports practical course. Considering the reviewed conditions, it can be concluded that the use of multimedia content doesn’t affect the learning effects. Key points Multimedia-assisted learning showed no positive learning effects on technical skills in soccer. Multimedia-assisted learning showed no positive learning effects on tactical skills in soccer. Students participating in practical sports courses have very good attitudes towards the use of multi-media learning material. This may be considered for motivational effects. PMID:24149313

Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach

NASA Astrophysics Data System (ADS)

Bellas-Velidis, Ioannis; Kontizas, Mary; Dapergolas, Anastasios; Livanou, Evdokia; Kontizas, Evangelos; Karampelas, Antonios

A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.

Structural damage detection using deep learning of ultrasonic guided waves

NASA Astrophysics Data System (ADS)

Melville, Joseph; Alguri, K. Supreet; Deemer, Chris; Harley, Joel B.

2018-04-01

Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy.

ezTag: tagging biomedical concepts via interactive learning.

PubMed

Kwon, Dongseop; Kim, Sun; Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2018-05-18

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

Team Collaboration: Lessons Learned Report

NASA Technical Reports Server (NTRS)

Arterberrie, Rhonda Y.; Eubanks, Steven W.; Kay, Dennis R.; Prahst, Stephen E.; Wenner, David P.

2005-01-01

An Agency team collaboration pilot was conducted from July 2002 until June 2003 and then extended for an additional year. The objective of the pilot was to assess the value of collaboration tools and adoption processes as applied to NASA teams. In an effort to share knowledge and experiences, the lessons that have been learned thus far are documented in this report. Overall, the pilot has been successful. An entire system has been piloted - tools, adoption, and support. The pilot consisted of two collaboration tools, a team space and a virtual team meeting capability. Of the two tools that were evaluated, the team meeting tool has been more widely accepted. Though the team space tool has been met with a lesser degree of acceptance, the need for such a tool in the NASA environment has been evidenced. Both adoption techniques and support were carefully developed and implemented in a way that has been well received by the pilot participant community.

Paid carers' experiences of caring for mechanically ventilated children at home: implications for services and training.

PubMed

Maddox, Christina; Pontin, David

2013-06-01

UK survival rates for long-term mechanically ventilated children have increased and paid carers are trained to care for them at home, however there is limited literature on carers' training needs and experience of sharing care. Using a qualitative abductive design, we purposively sampled experienced carers to generate data via diaries, semi-structured interviews, and researcher reflexive notes. Research ethics approval was granted from NHS and University committees. Five analytical themes emerged - Parent as expert; Role definition tensions; Training and Continuing Learning Needs; Mixed Emotions; Support Mechanisms highlighting the challenges of working in family homes for carers and their associated learning needs. Further work on preparing carers to share feelings with parents, using burnout prevention techniques, and building confidence is suggested. Carers highlight the lack of clinical supervision during their night-working hours. One solution may be to provide access to registered nurse support when working out-of-office hours.

For the Love of the Game: Game- Versus Lecture-Based Learning With Generation Z Patients.

PubMed

Adamson, Mary A; Chen, Hengyi; Kackley, Russell; Micheal, Alicia

2018-02-01

The current study evaluated adolescent patients' enjoyment of and knowledge gained from game-based learning compared with an interactive lecture format on the topic of mood disorders. It was hypothesized that game-based learning would be statistically more effective than a lecture in knowledge acquisition and satisfaction scores. A pre-post design was implemented in which a convenience sample of 160 adolescent patients were randomized to either a lecture (n = 80) or game-based (n = 80) group. Both groups completed a pretest/posttest and satisfaction survey. Results showed that both groups had significant improvement in knowledge from pretest compared to posttest. Game-based learning was statistically more effective than the interactive lecture in knowledge achievement and satisfaction scores. This finding supports the contention that game-based learning is an active technique that may be used with patient education. [Journal of Psychosocial Nursing and Mental Health Services, 56(2), 29-36.]. Copyright 2018, SLACK Incorporated.

Local Learning Strategies for Wake Identification

NASA Astrophysics Data System (ADS)

Colvert, Brendan; Alsalman, Mohamad; Kanso, Eva

2017-11-01

Swimming agents, biological and engineered alike, must navigate the underwater environment to survive. Tasks such as autonomous navigation, foraging, mating, and predation require the ability to extract critical cues from the hydrodynamic environment. A substantial body of evidence supports the hypothesis that biological systems leverage local sensing modalities, including flow sensing, to gain knowledge of their global surroundings. The nonlinear nature and high degree of complexity of fluid dynamics makes the development of algorithms for implementing localized sensing in bioinspired engineering systems essentially intractable for many systems of practical interest. In this work, we use techniques from machine learning for training a bioinspired swimmer to learn from its environment. We demonstrate the efficacy of this strategy by learning how to sense global characteristics of the wakes of other swimmers measured only from local sensory information. We conclude by commenting on the advantages and limitations of this data-driven, machine learning approach and its potential impact on broader applications in underwater sensing and navigation.

Accuracy comparison among different machine learning techniques for detecting malicious codes

NASA Astrophysics Data System (ADS)

Narang, Komal

2016-03-01

In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

NASA Astrophysics Data System (ADS)

Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

The 5-HT7 receptor in learning and memory. Importance of the hippocampus

PubMed Central

Roberts, Amanda J.; Hedlund, Peter B.

2011-01-01

The 5-HT7 receptor is a more recently discovered G-protein-coupled receptor for serotonin. The functions and possible clinical relevance of this receptor are not yet fully understood. The present paper reviews to what extent the use of animal models of learning and memory and other techniques have implicated the 5-HT7 receptor in such processes. The studies have used a combination of pharmacological and genetic tools targeting the receptor to evaluate effects on behavior and cellular mechanisms. In tests such as the Barnes maze, contextual fear conditioning and novel location recognition that involve spatial learning and memory there is a considerable amount of evidence supporting an involvement of the 5-HT7 receptor. Supporting evidence has also been obtained in studies of mRNA expression and cellular signaling as well as in electrophysiological experiments. Especially interesting are the subtle but distinct effects observed in hippocampus-dependent models of place learning where impairments have been described in mice lacking the 5-HT7 receptor or after administration of a selective antagonist. While more work is required, it appears that 5-HT7 receptors are particularly important in allocentric representation processes. In instrumental learning tasks both procognitive effects and impairments in memory have been observed using pharmacological tools targeting the 5-HT7 receptor. In conclusion, the use of pharmacological and genetic tools in animal studies of learning and memory suggest a potentially important role for the 5-HT7 receptor in cognitive processes. PMID:21484935

Cosmic Concepts: A Video Series for Scaffolded Learning

NASA Astrophysics Data System (ADS)

Eisenhamer, Bonnie; Summers, Frank; Maple, John

2016-01-01

Scaffolding is widely considered to be an essential element of effective teaching and is used to help bridge knowledge gaps for learners. Scaffolding is especially important for distance-learning programs and computer-based learning environments. Preliminary studies are showing that when students learn about complex topics within computer-based learning environments without scaffolding, they fail to gain a conceptual understanding of the topic. As a result, researchers have begun to emphasize the importance of scaffolding for web-based as well as in-person instruction.To support scaffolded teaching practices and techniques, while addressing the needs of life-long learners, we have created the Cosmic Concepts video series. The series consists of short, one-topic videos that address scientific concepts with a special emphasis on those that traditionally cause confusion or are layered with misconceptions. Each video focuses on one idea at a time and provides a clear explanation of phenomena that is succinct enough for on-demand reference usage by all types of learners. Likewise, the videos can be used by educators to scaffold the scientific concepts behind astronomical images, or can be sequenced together to create well-structured pathways for presenting deeper and more layered ideas. This approach is critical for communicating information about astronomical discoveries that are often dense with unfamiliar concepts, complex ideas, and highly technical details. Additionally, learning tools in video formats support multi-sensory presentation approaches that can make astronomy more accessible to a variety of learners.

Can Social Semantic Web Techniques Foster Collaborative Curriculum Mapping In Medicine?

PubMed Central

Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-01-01

Background Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. Objective The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. Methods A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives (“LOs”). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. Results At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 (“good”) in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system’s ability to support curriculum revision. Conclusions The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems. PMID:23948519

The Challenges and Opportunities for International Cooperative Radio Science; Experience with Mars Express and Venus Express Missions

NASA Technical Reports Server (NTRS)

Holmes, Dwight P.; Thompson, Tommy; Simpson, Richard; Tyler, G. Leonard; Dehant, Veronique; Rosenblatt, Pascal; Hausler, Bernd; Patzold, Martin; Goltz, Gene; Kahan, Daniel;

Imbalanced learning for pattern recognition: an empirical study

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

PubMed

Hussain, Lal; Ahmed, Adeel; Saeed, Sharjil; Rathore, Saima; Awan, Imtiaz Ahmed; Shah, Saeed Arif; Majid, Abdul; Idris, Adnan; Awan, Anees Ahmed

2018-02-06

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

PubMed

Gao, Chao; Sun, Hanbo; Wang, Tuo; Tang, Ming; Bohnen, Nicolaas I; Müller, Martijn L T M; Herman, Talia; Giladi, Nir; Kalinin, Alexandr; Spino, Cathie; Dauer, William; Hausdorff, Jeffrey M; Dinov, Ivo D

2018-05-08

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.

Opportunities to Create Active Learning Techniques in the Classroom

ERIC Educational Resources Information Center

Camacho, Danielle J.; Legare, Jill M.

2015-01-01

The purpose of this article is to contribute to the growing body of research that focuses on active learning techniques. Active learning techniques require students to consider a given set of information, analyze, process, and prepare to restate what has been learned--all strategies are confirmed to improve higher order thinking skills. Active…

TH-E-19A-01: Quality and Safety in Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ford, E; Ezzell, G; Miller, B

2014-06-15

Clinical radiotherapy data clearly demonstrate the link between the quality and safety of radiation treatments and the outcome for patients. The medical physicist plays an essential role in this process. To ensure the highest quality treatments, the medical physicist must understand and employ modern quality improvement techniques. This extends well beyond the duties traditionally associated with prescriptive QA measures. This session will review the current best practices for improving quality and safety in radiation therapy. General elements of quality management will be reviewed including: what makes a good quality management structure, the use of prospective risk analysis such as FMEA,more » and the use of incident learning. All of these practices are recommended in society-level documents and are incorporated into the new Practice Accreditation program developed by ASTRO. To be effective, however, these techniques must be practical in a resource-limited environment. This session will therefore focus on practical tools such as the newly-released radiation oncology incident learning system, RO-ILS, supported by AAPM and ASTRO. With these general constructs in mind, a case study will be presented of quality management in an SBRT service. An example FMEA risk assessment will be presented along with incident learning examples including root cause analysis. As the physicist's role as “quality officer” continues to evolve it will be essential to understand and employ the most effective techniques for quality improvement. This session will provide a concrete overview of the fundamentals in quality and safety. Learning Objectives: Recognize the essential elements of a good quality management system in radiotherapy. Understand the value of incident learning and the AAPM/ASTRO ROILS incident learning system. Appreciate failure mode and effects analysis as a risk assessment tool and its use in resource-limited environments. Understand the fundamental principles of good error proofing that extends beyond traditional prescriptive QA measures.« less

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

PubMed

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Automatic welding detection by an intelligent tool pipe inspection

NASA Astrophysics Data System (ADS)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

NASA Astrophysics Data System (ADS)

Shahiri, Amirah Mohamed; Husain, Wahidah; Rashid, Nur'Aini Abd

2017-10-01

Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students' dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.

A dissociation between engagement and learning: Enthusiastic instructions fail to reliably improve performance on a memory task.

PubMed

Motz, Benjamin A; de Leeuw, Joshua R; Carvalho, Paulo F; Liang, Kaley L; Goldstone, Robert L

2017-01-01

Despite widespread assertions that enthusiasm is an important quality of effective teaching, empirical research on the effect of enthusiasm on learning and memory is mixed and largely inconclusive. To help resolve these inconsistencies, we conducted a carefully-controlled laboratory experiment, investigating whether enthusiastic instructions for a memory task would improve recall accuracy. Scripted videos, either enthusiastic or neutral, were used to manipulate the delivery of task instructions. We also manipulated the sequence of learning items, replicating the spacing effect, a known cognitive technique for memory improvement. Although spaced study reliably improved test performance, we found no reliable effect of enthusiasm on memory performance across two experiments. We did, however, find that enthusiastic instructions caused participants to respond to more item prompts, leaving fewer test questions blank, an outcome typically associated with increased task motivation. We find no support for the popular claim that enthusiastic instruction will improve learning, although it may still improve engagement. This dissociation between motivation and learning is discussed, as well as its implications for education and future research on student learning.

A dissociation between engagement and learning: Enthusiastic instructions fail to reliably improve performance on a memory task

PubMed Central

de Leeuw, Joshua R.; Carvalho, Paulo F.; Liang, Kaley L.; Goldstone, Robert L.

2017-01-01

Despite widespread assertions that enthusiasm is an important quality of effective teaching, empirical research on the effect of enthusiasm on learning and memory is mixed and largely inconclusive. To help resolve these inconsistencies, we conducted a carefully-controlled laboratory experiment, investigating whether enthusiastic instructions for a memory task would improve recall accuracy. Scripted videos, either enthusiastic or neutral, were used to manipulate the delivery of task instructions. We also manipulated the sequence of learning items, replicating the spacing effect, a known cognitive technique for memory improvement. Although spaced study reliably improved test performance, we found no reliable effect of enthusiasm on memory performance across two experiments. We did, however, find that enthusiastic instructions caused participants to respond to more item prompts, leaving fewer test questions blank, an outcome typically associated with increased task motivation. We find no support for the popular claim that enthusiastic instruction will improve learning, although it may still improve engagement. This dissociation between motivation and learning is discussed, as well as its implications for education and future research on student learning. PMID:28732087

Maintenance of Voluntary Self-regulation Learned through Real-Time fMRI Neurofeedback

PubMed Central

Robineau, Fabien; Meskaldji, Djalel E.; Koush, Yury; Rieger, Sebastian W.; Mermoud, Christophe; Morgenthaler, Stephan; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

2017-01-01

Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is an emerging technique that allows for learning voluntary control over brain activity. Such brain training has been shown to cause specific behavioral or cognitive enhancements, and even therapeutic effects in neurological and psychiatric patient populations. However, for clinical applications it is important to know if learned self-regulation can be maintained over longer periods of time and whether it transfers to situations without neurofeedback. Here, we present preliminary results from five healthy participants who successfully learned to control their visual cortex activity and who we re-scanned 6 and 14 months after the initial neurofeedback training to perform learned self-regulation. We found that participants achieved levels of self-regulation that were similar to those achieved at the end of the successful initial training, and this without further neurofeedback information. Our results demonstrate that learned self-regulation can be maintained over longer periods of time and causes lasting transfer effects. They thus support the notion that neurofeedback is a promising therapeutic approach whose effects can last far beyond the actual training period. PMID:28386224

Collaborative Learning Works! Resources for Faculty

NASA Astrophysics Data System (ADS)

Mathieu, R. D.; Brissenden, G.; NISE College Level-1 Team

1998-12-01

Recent calls for instructional innovation in undergraduate science, mathematics, engineering, and technology (SMET) courses highlight the need for a solid foundation of education research at the undergraduate level on which to base policy and practice. We report the results of a meta-analysis that integrates research on undergraduate SMET education since 1980. The meta-analysis demonstrates that various forms of small-group learning are effective in promoting greater academic achievement, more favorable attitudes toward learning, and increased persistence through SMET courses and programs. The magnitude of the effects reported in this study exceeds most findings in comparable reviews of research on educational innovations and supports more widespread implementation of small-group learning in undergraduate SMET courses. We have created a web-site to assist instructors who wish to incorporate collaborative learning in their lectures, classrooms, and laboratories. The site provides straightforward, easy-to-use ideas for those just getting started, extensive additional resources for those already using small-group techniques, and the educational research foundation for the use of collaborative learning (including the meta-analysis). You can visit the site at www.wcer.wisc.edu/nise/cl1.

Course transformation: Content, structure and effectiveness analysis

NASA Astrophysics Data System (ADS)

DuHadway, Linda P.

The organization of learning materials is often limited by the systems available for delivery of such material. Currently, the learning management system (LMS) is widely used to distribute course materials. These systems deliver the material in a text-based, linear way. As online education continues to expand and educators seek to increase their effectiveness by adding more effective active learning strategies, these delivery methods become a limitation. This work demonstrates the possibility of presenting course materials in a graphical way that expresses important relations and provides support for manipulating the order of those materials. The ENABLE system gathers data from an existing course, uses text analysis techniques, graph theory, graph transformation, and a user interface to create and present graphical course maps. These course maps are able to express information not currently available in the LMS. Student agents have been developed to traverse these course maps to identify the variety of possible paths through the material. The temporal relations imposed by the current course delivery methods have been replaced by prerequisite relations that express ordering that provides educational value. Reducing the connections to these more meaningful relations allows more possibilities for change. Technical methods are used to explore and calibrate linear and nonlinear models of learning. These methods are used to track mastery of learning material and identify relative difficulty values. Several probability models are developed and used to demonstrate that data from existing, temporally based courses can be used to make predictions about student success in courses using the same material but organized without the temporal limitations. Combined, these demonstrate the possibility of tools and techniques that can support the implementation of a graphical course map that allows varied paths and provides an enriched, more informative interface between the educator, the student, and the learning material. This fundamental change in how course materials are presented and interfaced with has the potential to make educational opportunities available to a broader spectrum of people with diverse abilities and circumstances. The graphical course map can be pivotal in attaining this transition.

Immigration and culture as factors mediating the teaching and learning of urban science

NASA Astrophysics Data System (ADS)

Shady, Ashraf

In this dissertation I explore how cultural and sociohistorical dimensions of stakeholder groups (teachers, students, administrators, and researchers) mediate the interests of urban students in science. This study was conducted during the school year of 2006--2007 in a low-academically performing middle school in New York City. As an Egyptian immigrant science teacher I experienced resistance from my students in an eighth grade inclusion science class that warranted the use of cogenerative dialogue as a tool to improve teaching and learning. In the cogenerative dialogue sessions, participants (e.g., students, teachers, university researchers, and sometimes administrators) make every effort to convene as equals with goals of improving teaching and learning. By seeking the students' perspectives in cogenerative dialogue participants will be able to identify contradictions that can be addressed in an effort to improve the quality of the learning environments. Examples of such contradictions include shut down techniques that teachers use intentionally and unintentionally in order to have control over students. This authentic ethnography focused on two Black students from low-income homes, and me, a middle-aged male of Egypt's middle class. Throughout this study, the students acted in the capacity of student-researchers, assisting me to construct culturally adaptive curriculum materials, and to analyze data sources. This study utilized a sociocultural framework together with microanalysis of videotaped vignettes to obtain evidence that supports patterns of coherence and associated contradictions that emerged during the research. As the teacher-researcher, I learned along with my students how to communicate successfully in the context of structures that often act against success, including social class, ethnicity, gender, and age. The results of this study indicate that as a result of participating in cogenerative dialogues, I as well as the students learned the importance of group membership, and shared responsibilities for learning and acquiring new identities that support teaching and learning, and value diversity. Students reproduced, and transformed cultural practices from other social fields, such as cogenerative dialogues and home, to support their learning. Participating in cogenerative dialogues has produced a higher quality of teacher-student discourse as evidenced in data sources.

Status of teaching elementary science for English learners in science, mathematics and technology centered magnet schools

NASA Astrophysics Data System (ADS)

Han, Alyson Kim

According to the California Commission on Teacher Credentialing (2001), one in three students speaks a language other than English. Additionally, the Commission stated that a student is considered to be an English learner if the second language acquisition is English. In California more than 1.4 million English learners enter school speaking a variety of languages, and this number continues to rise. There is an imminent need to promote instructional strategies that support this group of diverse learners. Although this was not a California study, the results derived from the nationwide participants' responses provided a congruent assessment of the basic need to provide effective science teaching strategies to all English learners. The purpose of this study was to examine the status of elementary science teaching practices used with English learners in kindergarten through fifth grade in public mathematics, science, and technology-centered elementary magnet schools throughout the country. This descriptive research was designed to provide current information and to identify trends in the areas of curriculum and instruction for English learners in science themed magnet schools. This report described the status of elementary (grades K-5) school science instruction for English learners based on the responses of 116 elementary school teachers: 59 grade K-2, and 57 grade 3-5 teachers. Current research-based approaches support incorporating self-directed learning strategy, expository teaching strategy, active listening strategies, questioning strategies, wait time strategy, small group strategy, peer tutoring strategy, large group learning strategy, demonstrations strategy, formal debates strategy, review sessions strategy, mediated conversation strategy, cooperative learning strategy, and theme-based instruction into the curriculum to assist English learners in science education. Science Technology Society (STS) strategy, problem-based learning strategy, discovery learning strategy, constructivist learning strategy, learning cycle strategy, SCALE technique strategy, conceptual change strategy, inquiry-based strategy, cognitive academic language learning approach (CALLA) strategy, and learning from text strategy provide effective science teaching instruction to English learners. These science instructional strategies assist elementary science teachers by providing additional support to make science instruction more comprehensible for English learners.

Compartiendo la Ciencia con los Ninos: Una Guia para los Padres (Sharing Science with Children: A Guide for Parents).

ERIC Educational Resources Information Center

North Carolina Museum of Life and Science, Durham.

This guide offers Spanish-speaking parents ways in which they can help their children learn about science at home and in the community. Science is a way of looking at the world. It uses everyday techniques such as observation and classification to give us information about things and how they work. Advice to parents that want to support their…

Composition design for (PrNd–La–Ce)2Fe14B melt-spun magnets by machine learning technique

NASA Astrophysics Data System (ADS)

Li, Rui; Liu, Yao; Zuo, Shu-Lan; Zhao, Tong-Yun; Hu, Feng-Xia; Sun, Ji-Rong; Shen, Bao-Gen

2018-04-01

Not Available Project supported by the National Basic Research Program of China (Grant No. 2014CB643702), the National Natural Science Foundation of China (Grant No. 51590880), the Knowledge Innovation Project of the Chinese Academy of Sciences (Grant No. KJZD-EW-M05), and the National Key Research and Development Program of China (Grant No. 2016YFB0700903).

Figure Analysis: A Teaching Technique to Promote Visual Literacy and Active Learning

ERIC Educational Resources Information Center

Wiles, Amy M.

2016-01-01

Learning often improves when active learning techniques are used in place of traditional lectures. For many of these techniques, however, students are expected to apply concepts that they have already grasped. A challenge, therefore, is how to incorporate active learning into the classroom of courses with heavy content, such as molecular-based…

The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

NASA Astrophysics Data System (ADS)

Makahinda, T.

2018-02-01

The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

An Approach to V&V of Embedded Adaptive Systems

NASA Technical Reports Server (NTRS)

Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth

2004-01-01

Rigorous Verification and Validation (V&V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V&V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online stability monitoring tools. The novelty detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology. ,

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

Machine Learning and Data Mining for Comprehensive Test Ban Treaty Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Russell, S; Vaidya, S

2009-07-30

The Comprehensive Test Ban Treaty (CTBT) is gaining renewed attention in light of growing worldwide interest in mitigating risks of nuclear weapons proliferation and testing. Since the International Monitoring System (IMS) installed the first suite of sensors in the late 1990's, the IMS network has steadily progressed, providing valuable support for event diagnostics. This progress was highlighted at the recent International Scientific Studies (ISS) Conference in Vienna in June 2009, where scientists and domain experts met with policy makers to assess the current status of the CTBT Verification System. A strategic theme within the ISS Conference centered on exploring opportunitiesmore » for further enhancing the detection and localization accuracy of low magnitude events by drawing upon modern tools and techniques for machine learning and large-scale data analysis. Several promising approaches for data exploitation were presented at the Conference. These are summarized in a companion report. In this paper, we introduce essential concepts in machine learning and assess techniques which could provide both incremental and comprehensive value for event discrimination by increasing the accuracy of the final data product, refining On-Site-Inspection (OSI) conclusions, and potentially reducing the cost of future network operations.« less

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

PubMed

Morabito, Francesco Carlo; Campolo, Maurizio; Mammone, Nadia; Versaci, Mario; Franceschetti, Silvana; Tagliavini, Fabrizio; Sofia, Vito; Fatuzzo, Daniela; Gambardella, Antonio; Labate, Angelo; Mumoli, Laura; Tripodi, Giovanbattista Gaspare; Gasparini, Sara; Cianci, Vittoria; Sueri, Chiara; Ferlazzo, Edoardo; Aguglia, Umberto

2017-03-01

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

UDENTE (Universal Dental E-Learning) a golden opportunity for dental education.

PubMed

Reynolds, Patricia

2012-01-10

The incorporation of technological advancements in higher education has started to bridge the gap in local, national and global delivery of dental courses. This gap, including the global decrease in senior clinical academics, has influenced the development of new teaching and learning techniques. Institutional virtual learning environments (VLE) and other e-learning resources are now in higher demand. This paper describes how one such innovative solutions has been IVIDENT (International Virtual Dental School), has enabled secure and seamless access to high quality e-content and tools through an innovative, universal flexible learning platform. IVIDENT, now UDENTE (Universal Dental E-learning) has been shown to offer new learning experiences for students of dentistry, but its approach can apply across all educational domains. UDENTE also benefits staff as it allows them to contribute and access resources through peer reviewed publishing processes, which ensure the highest quality in education. UDENTE was developed thanks to a £2.3 million grant from the Higher Education Funding Council for England (HEFCE) and the Department of Health. http://www.udente.org. This academically led educational research project involved dental schools in seven countries. An initially scoping of requirements was followed by elaboration of the tools needed. Pilot testing of the tools, systems and learning resources in particular and the impact of the UDENTE in general were carried out. The pilots revealed evidence of positive impact of a space for learning, teaching, development and communication, with tools for planning of electives and administrative support. The results of these initial pilots have been positive and encouraging, describing UDENTE as an accessible, user friendly platform providing tools that otherwise would be difficult to access in a single space. However, attention to supporting faculty to embrace these new learning domains is essential if such technology enhanced learning (TEL) is to be viewed as a golden opportunity in Higher Education.

Mindfulness-Based Stress Reduction and Group Support Decrease Stress in Adolescents with Cardiac Diagnoses: A Randomized Two-Group Study.

PubMed

Freedenberg, Vicki A; Hinds, Pamela S; Friedmann, Erika

2017-10-01

Adolescents with cardiac diagnoses face unique challenges that can cause psychosocial distress. This study compares a Mindfulness-Based Stress Reduction (MBSR) program to a video online support group for adolescents with cardiac diagnoses. MBSR is a structured psycho-educational program which includes yoga, meditation, cognitive restructuring, and group support. A published feasibility study by our group showed significant reduction in anxiety following this intervention. Participants were randomized to MBSR or video online support group, and completed measures of anxiety, depression, illness-related stress, and coping pre- and post-6-session interventions. Qualitative data were obtained from post-intervention interviews. A total of 46 teens participated (mean 14.8 years; 63% female). Participants had congenital heart disease and/or cardiac device (52%), or postural orthostatic tachycardia syndrome (48%). Illness-related stress significantly decreased in both groups. Greater use of coping skills predicted lower levels of depression in both groups post-study completion. Higher baseline anxiety/depression scores predicted improved anxiety/depression scores in both groups. Each group reported the benefits of social support. The MBSR group further expressed benefits of learning specific techniques, strategies, and skills that they applied in real-life situations to relieve distress. Both the MBSR intervention and video support group were effective in reducing distress in this sample. Qualitative data elucidated the added benefits of using MBSR techniques to manage stress and symptoms. The video group format is useful for teens that cannot meet in person but can benefit from group support. Psychosocial interventions with stress management techniques and/or group support can reduce distress in adolescents with cardiac diagnoses.

Utilizing geogebra in financial mathematics problems: didactic experiment in vocational college

NASA Astrophysics Data System (ADS)

Ghozi, Saiful; Yuniarti, Suci

2017-12-01

GeoGebra application offers users to solve real problems in geometry, statistics, and algebra fields. This studydeterminesthe effect of utilizing Geogebra on students understanding skill in the field of financial mathematics. This didactic experiment study used pre-test-post-test control group design. Population of this study were vocational college students in Banking and Finance Program of Balikpapan State Polytechnic. Two classes in the first semester were chosen using cluster random sampling technique, one class as experiment group and one class as control group. Data were analysed used independent sample t-test. The result of data analysis showed that students understanding skill with learning by utilizing GeoGeobra is better than students understanding skill with conventional learning. This result supported that utilizing GeoGebra in learning can assist the students to enhance their ability and depth understanding on mathematics subject.

Pigeons acquire multiple categories in parallel via associative learning: A parallel to human word learning?

PubMed Central

Wasserman, Edward A.; Brooks, Daniel I.; McMurray, Bob

2014-01-01

Might there be parallels between category learning in animals and word learning in children? To examine this possibility, we devised a new associative learning technique for teaching pigeons to sort 128 photographs of objects into 16 human language categories. We found that pigeons learned all 16 categories in parallel, they perceived the perceptual coherence of the different object categories, and they generalized their categorization behavior to novel photographs from the training categories. More detailed analyses of the factors that predict trial-by-trial learning implicated a number of factors that may shape learning. First, we found considerable trial-by-trial dependency of pigeons’ categorization responses, consistent with several recent studies that invoke this dependency to claim that humans acquire words via symbolic or inferential mechanisms; this finding suggests that such dependencies may also arise in associative systems. Second, our trial-by-trial analyses divulged seemingly irrelevant aspects of the categorization task, like the spatial location of the report responses, which influenced learning. Third, those trial-by-trial analyses also supported the possibility that learning may be determined both by strengthening correct stimulus-response associations and by weakening incorrect stimulus-response associations. The parallel between all these findings and important aspects of human word learning suggests that associative learning mechanisms may play a much stronger part in complex human behavior than is commonly believed. PMID:25497520

Learning Probabilistic Logic Models from Probabilistic Examples

PubMed Central

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2009-01-01

Abstract We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. PMID:19888348

Learning Probabilistic Logic Models from Probabilistic Examples.

PubMed

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Effects of Intrinsic Motivation on Feedback Processing During Learning

PubMed Central

DePasque, Samantha; Tricomi, Elizabeth

2015-01-01

Learning commonly requires feedback about the consequences of one’s actions, which can drive learners to modify their behavior. Motivation may determine how sensitive an individual might be to such feedback, particularly in educational contexts where some students value academic achievement more than others. Thus, motivation for a task might influence the value placed on performance feedback and how effectively it is used to improve learning. To investigate the interplay between intrinsic motivation and feedback processing, we used functional magnetic resonance imaging (fMRI) during feedback-based learning before and after a novel manipulation based on motivational interviewing, a technique for enhancing treatment motivation in mental health settings. Because of its role in the reinforcement learning system, the striatum is situated to play a significant role in the modulation of learning based on motivation. Consistent with this idea, motivation levels during the task were associated with sensitivity to positive versus negative feedback in the striatum. Additionally, heightened motivation following a brief motivational interview was associated with increases in feedback sensitivity in the left medial temporal lobe. Our results suggest that motivation modulates neural responses to performance-related feedback, and furthermore that changes in motivation facilitates processing in areas that support learning and memory. PMID:26112370

Promoting children's learning and development in conflict-affected countries: Testing change process in the Democratic Republic of the Congo.

PubMed

Aber, J Lawrence; Tubbs, Carly; Torrente, Catalina; Halpin, Peter F; Johnston, Brian; Starkey, Leighann; Shivshanker, Anjuli; Annan, Jeannie; Seidman, Edward; Wolf, Sharon

2017-02-01

Improving children's learning and development in conflict-affected countries is critically important for breaking the intergenerational transmission of violence and poverty. Yet there is currently a stunning lack of rigorous evidence as to whether and how programs to improve learning and development in conflict-affected countries actually work to bolster children's academic learning and socioemotional development. This study tests a theory of change derived from the fields of developmental psychopathology and social ecology about how a school-based universal socioemotional learning program, the International Rescue Committee's Learning to Read in a Healing Classroom (LRHC), impacts children's learning and development. The study was implemented in three conflict-affected provinces of the Democratic Republic of the Congo and employed a cluster-randomized waitlist control design to estimate impact. Using multilevel structural equation modeling techniques, we found support for the central pathways in the LRHC theory of change. Specifically, we found that LRHC differentially impacted dimensions of the quality of the school and classroom environment at the end of the first year of the intervention, and that in turn these dimensions of quality were differentially associated with child academic and socioemotional outcomes. Future implications and directions are discussed.

The Effect of Student Learning Styles, Race and Gender on Learning Outcomes: The Case of Public Goods

ERIC Educational Resources Information Center

Devaraj, Nirupama; Raman, Jaishankar

2014-01-01

We investigate the impact of active learning techniques, specifically experiment based learning, in a Principles of Economics class. Our case study demonstrates that when using pedagogical techniques intended to facilitate active learning, teachers should be intentional about incorporating components of learning that appeal to students with…

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.

PubMed

Kireeva, Natalia V; Ovchinnikova, Svetlana I; Kuznetsov, Sergey L; Kazennov, Andrey M; Tsivadze, Aslan Yu

2014-02-01

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes

NASA Astrophysics Data System (ADS)

Kireeva, Natalia V.; Ovchinnikova, Svetlana I.; Kuznetsov, Sergey L.; Kazennov, Andrey M.; Tsivadze, Aslan Yu.

2014-02-01

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology.

PubMed

Dunlosky, John; Rawson, Katherine A; Marsh, Elizabeth J; Nathan, Mitchell J; Willingham, Daniel T

2013-01-01

Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique?  2a. Learning conditions  2b. Student characteristics  2c. Materials  2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students' performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students' performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research. © The Author(s) 2013.

How to become an expert educator: a qualitative study on the view of health professionals with experience in patient education.

PubMed

Svavarsdóttir, Margrét Hrönn; Sigurðardóttir, Árún K; Steinsbekk, Aslak

2015-05-13

Health professionals with the level of competency necessary to provide high-quality patient education are central to meeting patients' needs. However, research on how competencies in patient education should be developed and health professionals trained in them, is lacking. The aim of this study was to investigate the characteristics of an expert educator according to health professionals experienced in patient education for patients with coronary heart disease, and their views on how to become an expert educator. This descriptive qualitative study was conducted through individual interviews with health professionals experienced in patient education in cardiac care. Participants were recruited from cardiac care units and by using a snowball sampling technique. The interviews were audiotaped and transcribed verbatim. The data were analyzed with thematic approaches, using systematic text condensation. Nineteen Icelandic and Norwegian registered nurses, physiotherapists, and cardiologists, who had worked in cardiac care for 12 years on average, participated in the study. Being sensitive to the patient's interests and learning needs, and possessing the ability to tailor the education to each patient's needs and context of the situation was described as the hallmarks of an expert educator. To become an expert educator, motivation and active participation of the novice educator and a supportive learning environment were considered prerequisites. Supportive educational resources, observation and experiential training, and guidance from experienced educators were given as examples of resources that enhance competence development. Experienced educators expressed the need for peer support, inter-professional cooperation, and mentoring to further develop their competency. Expert patient educators were described as those demonstrating sensitivity toward the patient's learning needs and an ability to individualize the patient's education. A supportive learning environment, inner motivation, and an awareness of the value of patient education were considered the main factors required to become an expert educator. The experienced educators expressed a need for continuing education and peer support.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Supporting the Growing Needs of the GIS Industry

NASA Technical Reports Server (NTRS)

2003-01-01

Visual Learning Systems, Inc. (VLS), of Missoula, Montana, has developed a commercial software application called Feature Analyst. Feature Analyst was conceived under a Small Business Innovation Research (SBIR) contract with NASA's Stennis Space Center, and through the Montana State University TechLink Center, an organization funded by NASA and the U.S. Department of Defense to link regional companies with Federal laboratories for joint research and technology transfer. The software provides a paradigm shift to automated feature extraction, as it utilizes spectral, spatial, temporal, and ancillary information to model the feature extraction process; presents the ability to remove clutter; incorporates advanced machine learning techniques to supply unparalleled levels of accuracy; and includes an exceedingly simple interface for feature extraction.

Welcome to the techno highway: development of a health assessment CD-ROM and website.

PubMed

Bosco, Anna Maria; Ward, Catherine

2005-09-01

Traditionally teaching nursing students psychomotor skills took place in a laboratory setting; however, recent developments in computer technology have revolutionised how educators can transfer knowledge. To meet the need for an efficient and interactive learning experience a software product was required to educate nursing students about health assessment techniques. This paper presents how existing 'old technology' of a video was given new life by embracing new technology, resulting in development of an interactive CD-ROM with supporting WebCT. This innovation reflects a more flexible approach to learning as it is dynamic, portable, self-paced and more convenient for adult learners especially those in remote areas.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Professional development in statistics, technology, and cognitively demanding tasks: classroom implementation and obstacles

NASA Astrophysics Data System (ADS)

Foley, Gregory D.; Bakr Khoshaim, Heba; Alsaeed, Maha; Nihan Er, S.

2012-03-01

Attending professional development programmes can support teachers in applying new strategies for teaching mathematics and statistics. This study investigated (a) the extent to which the participants in a professional development programme subsequently used the techniques they had learned when teaching mathematics and statistics and (b) the obstacles they encountered in enacting cognitively demanding instructional tasks in their classrooms. The programme created an intellectual learning community among the participants and helped them gain confidence as teachers of statistics, and the students of participating teachers became actively engaged in deep mathematical thinking. The participants indicated, however, that time, availability of resources and students' prior achievement critically affected the implementation of cognitively demanding instructional activities.

Assessing the use of multiple sources in student essays.

PubMed

Hastings, Peter; Hughes, Simon; Magliano, Joseph P; Goldman, Susan R; Lawless, Kimberly

2012-09-01

The present study explored different approaches for automatically scoring student essays that were written on the basis of multiple texts. Specifically, these approaches were developed to classify whether or not important elements of the texts were present in the essays. The first was a simple pattern-matching approach called "multi-word" that allowed for flexible matching of words and phrases in the sentences. The second technique was latent semantic analysis (LSA), which was used to compare student sentences to original source sentences using its high-dimensional vector-based representation. Finally, the third was a machine-learning technique, support vector machines, which learned a classification scheme from the corpus. The results of the study suggested that the LSA-based system was superior for detecting the presence of explicit content from the texts, but the multi-word pattern-matching approach was better for detecting inferences outside or across texts. These results suggest that the best approach for analyzing essays of this nature should draw upon multiple natural language processing approaches.

Enhancing science literacy through implementation of writing-to-learn strategies: Exploratory studies in high school biology

NASA Astrophysics Data System (ADS)

Hohenshell, Liesl Marie

Some evidence of benefits from writing-to-learn techniques exists; however, more research is needed describing the instructional context used to support learning through writing and the quality of learning that results from particular tasks. This dissertation includes three papers, building on past research linking inquiry, social negotiation, and writing strategies to enhance scientific literacy skills of high school biology students. The interactive constructivist position informed the pedagogical approach for two empirical, classroom-based studies utilizing mixed methods to identify quantitative differences in learning outcomes and students' perceptions of writing tasks. The first paper reports students with planned writing activities communicated biotechnology content better in textbook explanations to a younger audience, but did not score better on tests than students who had delayed planning experiences. Students with two writing experiences as opposed to one, completing a newspaper article, scored better on conceptual questions both after writing and on a test 8 weeks later. The difference in treatments initially impacted males compared to females, but this effect disappeared with subsequent writing. The second paper reports two parallel studies of students completing two different writing types, laboratory and summary reports. Three comparison groups were used, Control students wrote in a traditional format, while SWH group students used the Science Writing Heuristic (SWH) during guided inquiry laboratories. Control students wrote summary reports to the teacher, while SWH students wrote either to the teacher or to peers (Peer Review group). On conceptual questions, findings indicated that after laboratory writing SWH females performed better compared to SWH males and Control females; and as a group SWH students performed better than Control students on a test following summary reports (Study 1). These results were not replicated in Study 2. An open-ended survey revealed findings that persisted in both studies; compared to Control students, SWH students were more likely to describe learning as they were writing and to report distinct thinking was required in completing the two writing types. Students' comments across studies provide support for using non-traditional writing tasks as a means to assist learning. Various implications for writing to serve learning are reported, including identification of key support conditions.

Techniques for Engaging the Public in Planetary Science

NASA Astrophysics Data System (ADS)

Shupla, Christine; Shaner, Andrew; Smith Hackler, Amanda

2017-10-01

Public audiences are often curious about planetary science. Scientists and education and public engagement specialists can leverage this interest to build scientific literacy. This poster will highlight research-based techniques the authors have tested with a variety of audiences, and are disseminating to planetary scientists through trainings.Techniques include:Make it personal. Audiences are interested in personal stories, which can capture the excitement, joy, and challenges that planetary scientists experience in their research. Audiences can learn more about the nature of science by meeting planetary scientists and hearing personal stories about their motivations, interests, and how they conduct research.Share relevant connections. Most audiences have very limited understanding of the solar system and the features and compositions of planetary bodies, but they enjoy learning about those objects they can see at night and factors that connect to their culture or local community.Demonstrate concepts. Some concepts can be clarified with analogies, but others can be demonstrated or modeled with materials. Demonstrations that are messy, loud, or that yield surprising results are particularly good at capturing an audience’s attention, but if they don’t directly relate to the key concept, they can serve as a distraction.Give them a role. Audience participation is an important engagement technique. In a presentation, scientists can invite the audience to respond to questions, pause to share their thoughts with a neighbor, or vote on an answer. Audiences can respond physically to prompts, raising hands, pointing, or clapping, or even moving to different locations in the room.Enable the audience to conduct an activity. People learn best by doing and by teaching others; simple hands-on activities in which the audience is discovering something themselves can be extremely effective at engaging audiences.This poster will cite examples of each technique, resources that can help planetary scientists develop presentations, demonstrations, and activities for public engagement events, and the research that supports the use of these techniques.

How to Avoid a Learning Curve in Stapedotomy: A Standardized Surgical Technique.

PubMed

Kwok, Pingling; Gleich, Otto; Dalles, Katharina; Mayr, Elisabeth; Jacob, Peter; Strutz, Jürgen

2017-08-01

To evaluate, whether a learning curve for beginners in stapedotomy can be avoided by using a prosthesis with thermal memory-shape attachment in combination with a standardized laser-assisted surgical technique. Retrospective case review. Tertiary referral center. Fifty-eight ears were operated by three experienced surgeons and compared with a group of 12 cases operated by a beginner in stapedotomy. Stapedotomy. Difference of pure-tone audiometry thresholds measured before and after surgery. The average postoperative gain for air conduction in the frequencies below 2 kHz was 20 to 25 dB and decreased for the higher frequencies. Using the Mann-Whitney-U test for comparing mean gain between experienced and inexperienced surgeons showed no significant difference (p = 0.281 at 4 kHz and p > 0.7 for the other frequencies). A Spearman rank correlation of the postoperative gain for air- and bone-conduction thresholds was obtained at each test frequency for the first 12 patients consecutively treated with a thermal memory-shape attachment prosthesis by two experienced and one inexperienced surgeon. This analysis does not support the hypothesis of a "learning effect" that should be associated with an improved outcome for successively treated patients. It is possible to avoid a learning curve in stapes surgery by applying a thermal memory-shape prosthesis in a standardized laser-assisted surgical procedure.

THE FLAG: A Web Resource of Innovative Assessment Tools for Faculty in College Science, Mathematics, Engineering, and Technology

NASA Astrophysics Data System (ADS)

Zeilik, M.; Mathieu, R. D.; National InstituteScience Education; College Level-One Team

2000-12-01

Even the most dedicated college faculty often discover that their students fail to learn what was taught in their courses and that much of what students do learn is quickly forgotten after the final exam. To help college faculty improve student learning in college Science, Mathematics, Engineering and Technology (SMET), the College Level - One Team of the National Institute for Science Education has created the "FLAG" a Field-tested Learning Assessment Guide for SMET faculty. Developed with funding from the National Science Foundation, the FLAG presents in guidebook format a diverse and robust collection of field-tested classroom assessment techniques (CATs), with supporting information on how to apply them in the classroom. Faculty can download the tools and techniques from the website, which also provides a goals clarifier, an assessment primer, a searchable database, and links to additional resources. The CATs and tools have been reviewed by an expert editorial board and the NISE team. These assessment strategies can help faculty improve the learning environments in their SMET courses especially the crucial introductory courses that most strongly shape students' college learning experiences. In addition, the FLAG includes the web-based Student Assessment of Learning Gains. The SALG offers a convenient way to evaluate the impact of your courses on students. It is based on findings that students' estimates of what they gained are more reliable and informative than their observations of what they liked about the course or teacher. It offers accurate feedback on how well the different aspects of teaching helped the students to learn. Students complete the SALG online after a generic template has been modified to fit the learning objectives and activities of your course. The results are presented to the teacher as summary statistics automatically. The FLAG can be found at the NISE "Innovations in SMET Education" website at www.wcer.wisc.edu/nise/cl1

A survey of automated methods for sensemaking support

NASA Astrophysics Data System (ADS)

Llinas, James

2014-05-01

Complex, dynamic problems in general present a challenge for the design of analysis support systems and tools largely because there is limited reliable a priori procedural knowledge descriptive of the dynamic processes in the environment. Problem domains that are non-cooperative or adversarial impute added difficulties involving suboptimal observational data and/or data containing the effects of deception or covertness. The fundamental nature of analysis in these environments is based on composite approaches involving mining or foraging over the evidence, discovery and learning processes, and the synthesis of fragmented hypotheses; together, these can be labeled as sensemaking procedures. This paper reviews and analyzes the features, benefits, and limitations of a variety of automated techniques that offer possible support to sensemaking processes in these problem domains.

Building online learning communities in a graduate dental hygiene program.

PubMed

Rogo, Ellen J; Portillo, Karen M

2014-08-01

The literature abounds with research related to building online communities in a single course; however, limited evidence is available on this phenomenon from a program perspective. The intent of this qualitative case study inquiry was to explore student experiences in a graduate dental hygiene program contributing or impeding the development and sustainability of online learning communities. Approval from the IRB was received. A purposive sampling technique was used to recruit participants from a stratification of students and graduates. A total of 17 participants completed semi-structured interviews. Data analysis was completed through 2 rounds - 1 for coding responses and 1 to construct categories of experiences. The participants' collective definition of an online learning community was a complex synergistic network of interconnected people who create positive energy. The findings indicated the development of this network began during the program orientation and was beneficial for building a foundation for the community. Students felt socially connected and supported by the network. Course design was another important category for participation in weekly discussions and group activities. Instructors were viewed as active participants in the community, offering helpful feedback and being a facilitator in discussions. Experiences impeding the development of online learning communities related to the poor performance of peers and instructors. Specific categories of experiences supported and impeded the development of online learning communities related to the program itself, course design, students and faculty. These factors are important to consider in order to maximize student learning potential in this environment. Copyright © 2014 The American Dental Hygienists’ Association.

Improving Word Learning in Children Using an Errorless Technique

ERIC Educational Resources Information Center

Warmington, Meesha; Hitch, Graham J.; Gathercole, Susan E.

2013-01-01

The current experiment examined the relative advantage of an errorless learning technique over an errorful one in the acquisition of novel names for unfamiliar objects in typically developing children aged between 7 and 9 years. Errorless learning led to significantly better learning than did errorful learning. Processing speed and vocabulary…

Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

The student learning process is important in online learning environments. If instructors can "observe" online learning behaviors, they can provide adaptive feedback, adjust instructional strategies, and assist students in establishing patterns of successful learning activities. This study used data mining techniques to examine and…

Using neural networks in software repositories

NASA Technical Reports Server (NTRS)

Eichmann, David (Editor); Srinivas, Kankanahalli; Boetticher, G.

1992-01-01

The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology.

GP Supervisors' Experience in Supporting Self-Regulated Learning: A Balancing Act

ERIC Educational Resources Information Center

Sagasser, Margaretha H.; Kramer, Anneke W. M.; van Weel, Chris; van der Vleuten, Cees P. M.

2015-01-01

Self-regulated learning is essential for professional development and lifelong learning. As self-regulated learning has many inaccuracies, the need to support self-regulated learning has been recommended. Supervisors can provide such support. In a prior study trainees reported on the variation in received supervisor support. This study aims at…

Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques.

PubMed

Monte-Moreno, Enric

2011-10-01

This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does not need calibration over time or subjects. The architecture of the system consists of a photoplethysmograph sensor, an activity detection module, a signal processing module that extracts features from the PPG waveform, and a machine learning algorithm that estimates the SBP, DBP and BGL values. The idea that underlies the system is that there is functional relationship between the shape of the PPG waveform and the blood pressure and glucose levels. As described in this paper we tested this method on 410 individuals without performing any personalized calibration. The results were computed after cross validation. The machine learning techniques tested were: ridge linear regression, a multilayer perceptron neural network, support vector machines and random forests. The best results were obtained with the random forest technique. In the case of blood pressure, the resulting coefficients of determination for reference vs. prediction were R(SBP)(2)=0.91, R(DBP)(2)=0.89, and R(BGL)(2)=0.90. For the glucose estimation, distribution of the points on a Clarke error grid placed 87.7% of points in zone A, 10.3% in zone B, and 1.9% in zone D. Blood pressure values complied with the grade B protocol of the British Hypertension society. An effective system for estimate of blood glucose and blood pressure from a photoplethysmograph is presented. The main advantage of the system is that for clinical use it complies with the grade B protocol of the British Hypertension society for the blood pressure and only in 1.9% of the cases did not detect hypoglycemia or hyperglycemia. Copyright © 2011 Elsevier B.V. All rights reserved.

Through the eyes of the student: Best practices in clinical facilitation.

PubMed

Muthathi, Immaculate S; Thurling, Catherine H; Armstrong, Susan J

2017-08-28

Clinical facilitation is an essential part of the undergraduate nursing curriculum. A number of studies address the issue of clinical facilitation in South Africa, but there remains a lack of knowledge and understanding regarding what students perceive as best practice in clinical facilitation of their learning. To determine what type of clinical facilitation undergraduate students believe should be offered by clinical facilitators (nurse educators, professional nurses and clinical preceptors) in the clinical area in order to best facilitate their learning. A qualitative, exploratory and descriptive study was conducted. Purposive sampling was performed to select nursing students from the second, third and fourth year of studies from a selected nursing education institution in Johannesburg. The sampling resulted in one focus group for each level of nursing, namely second, third and fourth year nursing students. Interviews were digitally recorded and transcribed verbatim, thematic data analysis was used and trustworthiness was ensured by applying credibility, dependability, confirmability and transferability. The data revealed that participants differentiated between best practices in clinical facilitation in the clinical skills laboratory and clinical learning environment. In the clinical skills laboratory, pre-contact preparation, demonstration technique and optimising group learning were identified as best practices. In the clinical learning environment, a need for standardisation of procedures in simulation and practice, the allocation and support for students also emerged. There is a need for all nurses involved in undergraduate nursing education to reflect on how they approach clinical facilitation, in both clinical skills laboratory and clinical learning environment. There is also a need to improve consistency in clinical practices between the nursing education institution and the clinical learning environment so as to support students' adaptation to clinical practice.

Enhancing the Impact of NASA Astrophysics Education and Public Outreach: Community Collaborations

NASA Astrophysics Data System (ADS)

Smith, Denise A.; Lawton, B. L.; Bartolone, L.; Schultz, G. R.; Blair, W. P.; Astrophysics E/PO Community, NASA; NASA Astrophysics Forum Team

2013-01-01

The NASA Astrophysics Science Education and Public Outreach Forum is one of four scientist-educator teams that support NASA's Science Mission Directorate and its nationwide education and public outreach community in increasing the coherence, efficiency, and effectiveness of their education and public outreach efforts. NASA Astrophysics education and outreach teams collaborate with each other through the Astrophysics Forum to place individual programs in context, connect with broader education and public outreach activities, learn and share successful strategies and techniques, and develop new partnerships. This poster highlights examples of collaborative efforts designed to engage youth and adults across the full spectrum of learning environments, from public outreach venues, to centers of informal learning, to K-12 and higher education classrooms. These include coordinated efforts to support major outreach events such as the USA Science and Engineering Festival; pilot "Astro4Girls" activities in public libraries to engage girls and their families in science during Women’s History Month; and a pilot "NASA's Multiwavelength Universe" online professional development course for middle and high school educators. Resources to assist scientists and Astro101 instructors in incorporating NASA Astrophysics discoveries into their education and public outreach efforts are also discussed.

Instructional Experiences of Graduate Assistants Implementing Explicit and Reflective Introductory Biology Laboratories

NASA Astrophysics Data System (ADS)

Bautista, Nazan Uludag; Schussler, Elisabeth E.; Rybczynski, Stephen M.

2014-05-01

Science education reform documents identify nature of science (NOS) as a critical component of scientific literacy and call for universities, colleges, and K-12 schools to explicitly integrate NOS learning into science curricula. In response to these calls, this study investigated the classroom practices of nine graduate assistants (GAs) who taught expository and inquiry laboratories that implemented an explicit and reflective (ER) pedagogy to teach NOS. The purpose of this qualitative study was to better understand the experiences that enabled or inhibited GA implementation of an ER strategy in a college setting. The findings revealed that achieving quality implementation in this setting was very difficult. Factors such as GAs' ability to foster meaningful classroom discussions, laboratory logistics (e.g. lack of time and supplies), and the value undergraduates and GAs saw in learning about NOS were identified by GAs and observed by the researchers as barriers to the technique maximizing its potential. Thus, for meaningful infusion of NOS into science curricula, pedagogical support for GAs to manage meaningful classroom discussions in support of NOS or other complex topics is recommended for an ER approach to NOS learning to be successful in college settings.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

History matching through dynamic decision-making

PubMed Central

Maschio, Célio; Santos, Antonio Alberto; Schiozer, Denis; Rocha, Anderson

2017-01-01

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark. PMID:28582413

The Physics Learning Center at the University of Wisconsin-Madison

NASA Astrophysics Data System (ADS)

Nossal, S. M.; Watson, L. E.; Hooper, E.; Huesmann, A.; Schenker, B.; Timbie, P.; Rzchowski, M.

2013-03-01

The Physics Learning Center at the University of Wisconsin-Madison provides academic support and small-group supplemental instruction to students studying introductory algebra-based and calculus-based physics. These classes are gateway courses for majors in the biological and physical sciences, pre-health fields, engineering, and secondary science education. The Physics Learning Center offers supplemental instruction groups twice weekly where students can discuss concepts and practice with problem-solving techniques. The Center also provides students with access on-line resources that stress conceptual understanding, and to exam review sessions. Participants in our program include returning adults, people from historically underrepresented racial/ethnic groups, students from families in lower-income circumstances, students in the first generation of their family to attend college, transfer students, veterans, and people with disabilities, all of whom might feel isolated in their large introductory course and thus have a more difficult time finding study partners. We also work with students potentially at-risk for having academic difficulty (due to factors academic probation, weak math background, low first exam score, or no high school physics). A second mission of the Physics Learning Center is to provide teacher training and leadership experience for undergraduate Peer Mentor Tutors. These Peer Tutors lead the majority of the weekly group sessions in close supervision by PLC staff members. We will describe our work to support students in the Physics Learning Center, including our teacher-training program for our undergraduate Peer Mentor Tutors

Reviewing the connection between speech and obstructive sleep apnea.

PubMed

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T; Alcázar-Ramírez, José D; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A

2016-02-20

Sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). The altered UA structure or function in OSA speakers has led to hypothesize the automatic analysis of speech for OSA assessment. In this paper we critically review several approaches using speech analysis and machine learning techniques for OSA detection, and discuss the limitations that can arise when using machine learning techniques for diagnostic applications. A large speech database including 426 male Spanish speakers suspected to suffer OSA and derived to a sleep disorders unit was used to study the clinical validity of several proposals using machine learning techniques to predict the apnea-hypopnea index (AHI) or classify individuals according to their OSA severity. AHI describes the severity of patients' condition. We first evaluate AHI prediction using state-of-the-art speaker recognition technologies: speech spectral information is modelled using supervectors or i-vectors techniques, and AHI is predicted through support vector regression (SVR). Using the same database we then critically review several OSA classification approaches previously proposed. The influence and possible interference of other clinical variables or characteristics available for our OSA population: age, height, weight, body mass index, and cervical perimeter, are also studied. The poor results obtained when estimating AHI using supervectors or i-vectors followed by SVR contrast with the positive results reported by previous research. This fact prompted us to a careful review of these approaches, also testing some reported results over our database. Several methodological limitations and deficiencies were detected that may have led to overoptimistic results. The methodological deficiencies observed after critically reviewing previous research can be relevant examples of potential pitfalls when using machine learning techniques for diagnostic applications. We have found two common limitations that can explain the likelihood of false discovery in previous research: (1) the use of prediction models derived from sources, such as speech, which are also correlated with other patient characteristics (age, height, sex,…) that act as confounding factors; and (2) overfitting of feature selection and validation methods when working with a high number of variables compared to the number of cases. We hope this study could not only be a useful example of relevant issues when using machine learning for medical diagnosis, but it will also help in guiding further research on the connection between speech and OSA.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.

PubMed

Hamanaka, Masatoshi; Taneishi, Kei; Iwata, Hiroaki; Ye, Jun; Pei, Jianguo; Hou, Jinlong; Okuno, Yasushi

2017-01-01

Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a support vector machine (SVM). However, the CGBVS has problems when training using more than a million datasets of CPIs since SVMs require an exponential increase in the calculation time and computer memory. To solve this problem, we propose the CGBVS-DNN, in which we use deep neural networks, a kind of deep learning technique, instead of the SVM. Deep learning does not require learning all input data at once because the network can be trained with small mini-batches. Experimental results show that the CGBVS-DNN outperformed the original CGBVS with a quarter million CPIs. Results of cross-validation show that the accuracy of the CGBVS-DNN reaches up to 98.2 % (σ<0.01) with 4 million CPIs. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Giving a Voice to the Person in My Dental Chair: Communication with People with Learning Disabilities.

PubMed

Hollingsworth, Lorna; Kalambouka, Afroditi

2015-05-01

This article aims to increase the dental teams' awareness of communicating with people with learning disabilities who have additional communication impairments. The paper presents a brief account of the factors behind why some people with learning disabilities may find it difficult to verbally communicate, and highlights the importance of ensuring high levels of care for all patients. It provides an overview of the principles of communication development and some of the most commonly used augmentative and alternative communication approaches. The paper concludes with suggestions of simple communication techniques as well as practical ideas, which can be easily incorporated into daily general dental practice in order to increase opportunities for successful interactions and minimise communication breakdown. By becoming more aware of the range of communication methods used to support those who have learning disabilities, the dental team will be more able to provide a better experience to their patients and ensure that their needs are met.

Markerless gating for lung cancer radiotherapy based on machine learning techniques

NASA Astrophysics Data System (ADS)

Lin, Tong; Li, Ruijiang; Tang, Xiaoli; Dy, Jennifer G.; Jiang, Steve B.

2009-03-01

In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks—ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

Variation in behavioral engagement during an active learning activity leads to differential knowledge gains in college students.

PubMed

LaDage, Lara D; Tornello, Samantha L; Vallejera, Jennilyn M; Baker, Emily E; Yan, Yue; Chowdhury, Anik

2018-03-01

There are many pedagogical techniques used by educators in higher education; however, some techniques and activities have been shown to be more beneficial to student learning than others. Research has demonstrated that active learning and learning in which students cognitively engage with the material in a multitude of ways result in better understanding and retention. The aim of the present study was to determine which of three pedagogical techniques led to improvement in learning and retention in undergraduate college students. Subjects partook in one of three different types of pedagogical engagement: hands-on learning with a model, observing someone else manipulate the model, and traditional lecture-based presentation. Students were then asked to take an online quiz that tested their knowledge of the new material, both immediately after learning the material and 2 wk later. Students who engaged in direct manipulation of the model scored higher on the assessment immediately after learning the material compared with the other two groups. However, there were no differences among the three groups when assessed after a 2-wk retention interval. Thus active learning techniques that involve direct interaction with the material can lead to learning benefits; however, how these techniques benefit long-term retention of the information is equivocal.

Fusing modeling techniques to support domain analysis for reuse opportunities identification

NASA Technical Reports Server (NTRS)

Hall, Susan Main; Mcguire, Eileen

1993-01-01

Functional modeling techniques or object-oriented graphical representations, which are more useful to someone trying to understand the general design or high level requirements of a system? For a recent domain analysis effort, the answer was a fusion of popular modeling techniques of both types. By using both functional and object-oriented techniques, the analysts involved were able to lean on their experience in function oriented software development, while taking advantage of the descriptive power available in object oriented models. In addition, a base of familiar modeling methods permitted the group of mostly new domain analysts to learn the details of the domain analysis process while producing a quality product. This paper describes the background of this project and then provides a high level definition of domain analysis. The majority of this paper focuses on the modeling method developed and utilized during this analysis effort.

Effect of Active Learning Techniques on Students' Choice of Approach to Learning in Dentistry: A South African Case Study

ERIC Educational Resources Information Center

Khan, S.

2011-01-01

The purpose of this article is to report on empirical work, related to a techniques module, undertaken with the dental students of the University of the Western Cape, South Africa. I will relate how a range of different active learning techniques (tutorials; question papers and mock tests) assisted students to adopt a deep approach to learning in…

An adult learner's learning style should inform but not limit educational choices

NASA Astrophysics Data System (ADS)

Barry, Margot; Egan, Arlene

2017-12-01

Adult learners are attracted to learning opportunities (e.g. course offers) which seem promising in terms of allowing them to match their choices to their own perceived predispositions. To find out more about their personal learning style, some adult learners may fill in a questionnaire designed by researchers who aim (and claim) to enable both course providers and learners to optimise learning outcomes. The evaluation of these questionnaires measures learning styles using indicators developed for this purpose, but the results are not conclusive and their utility is therefore questionable. This narrative review critically examines some of the research which explores the usefulness of considering students' learning styles in adult education. The authors present a discussion - which remains hypothetical - on why the use of learning styles measures continues to be popular despite the absence of rigorous research findings to support this practice. Factors discussed by the authors include confirmation bias (making choices which confirm our prejudices) and user qualification (limiting availability to trained users, e.g. psychologists) as well as limited resources and skills in evaluating research, paired with educators' quest to implement evidence-focused techniques. The authors conclude that while learning styles assessments can be useful for the purpose of reflection on strengths and weaknesses, they should play a limited role in educational choices.

An adult learner's learning style should inform but not limit educational choices

NASA Astrophysics Data System (ADS)

Barry, Margot; Egan, Arlene

2018-02-01

Adult learners are attracted to learning opportunities (e.g. course offers) which seem promising in terms of allowing them to match their choices to their own perceived predispositions. To find out more about their personal learning style, some adult learners may fill in a questionnaire designed by researchers who aim (and claim) to enable both course providers and learners to optimise learning outcomes. The evaluation of these questionnaires measures learning styles using indicators developed for this purpose, but the results are not conclusive and their utility is therefore questionable. This narrative review critically examines some of the research which explores the usefulness of considering students' learning styles in adult education. The authors present a discussion - which remains hypothetical - on why the use of learning styles measures continues to be popular despite the absence of rigorous research findings to support this practice. Factors discussed by the authors include confirmation bias (making choices which confirm our prejudices) and user qualification (limiting availability to trained users, e.g. psychologists) as well as limited resources and skills in evaluating research, paired with educators' quest to implement evidence-focused techniques. The authors conclude that while learning styles assessments can be useful for the purpose of reflection on strengths and weaknesses, they should play a limited role in educational choices.

Learning and memory functions of the Basal Ganglia.

PubMed

Packard, Mark G; Knowlton, Barbara J

2002-01-01

Although the mammalian basal ganglia have long been implicated in motor behavior, it is generally recognized that the behavioral functions of this subcortical group of structures are not exclusively motoric in nature. Extensive evidence now indicates a role for the basal ganglia, in particular the dorsal striatum, in learning and memory. One prominent hypothesis is that this brain region mediates a form of learning in which stimulus-response (S-R) associations or habits are incrementally acquired. Support for this hypothesis is provided by numerous neurobehavioral studies in different mammalian species, including rats, monkeys, and humans. In rats and monkeys, localized brain lesion and pharmacological approaches have been used to examine the role of the basal ganglia in S-R learning. In humans, study of patients with neurodegenerative diseases that compromise the basal ganglia, as well as research using brain neuroimaging techniques, also provide evidence of a role for the basal ganglia in habit learning. Several of these studies have dissociated the role of the basal ganglia in S-R learning from those of a cognitive or declarative medial temporal lobe memory system that includes the hippocampus as a primary component. Evidence suggests that during learning, basal ganglia and medial temporal lobe memory systems are activated simultaneously and that in some learning situations competitive interference exists between these two systems.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Educating the Educators: Developing Those Who Support Learning for Students with Additional Learning Needs

ERIC Educational Resources Information Center

McLachlan, Benita; Davis, Geraldine

2013-01-01

This article reports findings from a research project which developed and introduced the Enhanced Learning Support Assistant Programme (ELSAP) as a source of professional development for learning support assistants who were supporting students with additional learning needs in a college of further education in England. The purpose of this article…

Getting the Most Out of Dual-Listed Courses: Involving Undergraduate Students in Discussion Through Active Learning Techniques

NASA Astrophysics Data System (ADS)

Tasich, C. M.; Duncan, L. L.; Duncan, B. R.; Burkhardt, B. L.; Benneyworth, L. M.

2015-12-01

Dual-listed courses will persist in higher education because of resource limitations. The pedagogical differences between undergraduate and graduate STEM student groups and the underlying distinction in intellectual development levels between the two student groups complicate the inclusion of undergraduates in these courses. Active learning techniques are a possible remedy to the hardships undergraduate students experience in graduate-level courses. Through an analysis of both undergraduate and graduate student experiences while enrolled in a dual-listed course, we implemented a variety of learning techniques used to complement the learning of both student groups and enhance deep discussion. Here, we provide details concerning the implementation of four active learning techniques - role play, game, debate, and small group - that were used to help undergraduate students critically discuss primary literature. Student perceptions were gauged through an anonymous, end-of-course evaluation that contained basic questions comparing the course to other courses at the university and other salient aspects of the course. These were given as a Likert scale on which students rated a variety of statements (1 = strongly disagree, 3 = no opinion, and 5 = strongly agree). Undergraduates found active learning techniques to be preferable to traditional techniques with small-group discussions being rated the highest in both enjoyment and enhanced learning. The graduate student discussion leaders also found active learning techniques to improve discussion. In hindsight, students of all cultures may be better able to take advantage of such approaches and to critically read and discuss primary literature when written assignments are used to guide their reading. Applications of active learning techniques can not only address the gap between differing levels of students, but also serve as a complement to student engagement in any science course design.

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Identification and description of the momentum effect in studies of learning: An abstract science concept

NASA Astrophysics Data System (ADS)

Kwon, Jae-Sool; Mayer, Victor J.

Several studies of the validity of the intensive time series design have revealed a post-intervention increase in the level of achievement data. This so called momentum effect has not been demonstrated through the application of an appropriate analysis technique. The purpose of this study was to identify and apply a technique that would adequately represent and describe such an effect if indeed it does occur, and to use that technique to study the momentum effect as it is observed in several data sets on the learning of the concept of plate tectonics. Subsequent to trials of several different analyses, a segmented straight line regression analysis was chosen and used on three different data sets. Each set revealed similar patterns of inflection points between lines with similar time intervals between inflections for those data from students with formal cognitive tendencies. These results seem to indicate that this method will indeed be useful in representing and identifying the presence and duration of the momentum effect in time series data on achievement. Since the momentum effect could be described in each of the data sets and since its presence seems a function of similar circumstances, support is given for its presence in the learning of abstract scientific concepts for formal cognitive tendency students. The results indicate that the duration of the momentum effect is related to the level of student understanding tested and the cognitive level of the learners.

Learning Programming Technique through Visual Programming Application as Learning Media with Fuzzy Rating

ERIC Educational Resources Information Center

Buditjahjanto, I. G. P. Asto; Nurlaela, Luthfiyah; Ekohariadi; Riduwan, Mochamad

2017-01-01

Programming technique is one of the subjects at Vocational High School in Indonesia. This subject contains theory and application of programming utilizing Visual Programming. Students experience some difficulties to learn textual learning. Therefore, it is necessary to develop media as a tool to transfer learning materials. The objectives of this…

How Students Learn: Improving Teaching Techniques for Business Discipline Courses

ERIC Educational Resources Information Center

Cluskey, Bob; Elbeck, Matt; Hill, Kathy L.; Strupeck, Dave

2011-01-01

The focus of this paper is to familiarize business discipline faculty with cognitive psychology theories of how students learn together with teaching techniques to assist and improve student learning. Student learning can be defined as the outcome from the retrieval (free recall) of desired information. Student learning occurs in two processes.…

Navigating the Active Learning Swamp: Creating an Inviting Environment for Learning.

ERIC Educational Resources Information Center

Johnson, Marie C.; Malinowski, Jon C.

2001-01-01

Reports on a survey of faculty members (n=29) asking them to define active learning, to rate how effectively different teaching techniques contribute to active learning, and to list the three teaching techniques they use most frequently. Concludes that active learning requires establishing an environment rather than employing a specific teaching…

Indicators of Family Care for Development for Use in Multicountry Surveys

PubMed Central

Kariger, Patricia; Engle, Patrice; Britto, Pia M. Rebello; Sywulka, Sara M.; Menon, Purnima

2012-01-01

Indicators of family care for development are essential for ascertaining whether families are providing their children with an environment that leads to positive developmental outcomes. This project aimed to develop indicators from a set of items, measuring family care practices and resources important for caregiving, for use in epidemiologic surveys in developing countries. A mixed method (quantitative and qualitative) design was used for item selection and evaluation. Qualitative and quantitative analyses were conducted to examine the validity of candidate items in several country samples. Qualitative methods included the use of global expert panels to identify and evaluate the performance of each candidate item as well as in-country focus groups to test the content validity of the items. The quantitative methods included analyses of item-response distributions, using bivariate techniques. The selected items measured two family care practices (support for learning/stimulating environment and limit-setting techniques) and caregiving resources (adequacy of the alternate caregiver when the mother worked). Six play-activity items, indicative of support for learning/stimulating environment, were included in the core module of UNICEF's Multiple Cluster Indictor Survey 3. The other items were included in optional modules. This project provided, for the first time, a globally-relevant set of items for assessing family care practices and resources in epidemiological surveys. These items have multiple uses, including national monitoring and cross-country comparisons of the status of family care for development used globally. The obtained information will reinforce attention to efforts to improve the support for development of children. PMID:23304914

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

[Feasibility of implementing a learning based problem solving and peer evaluation approach among medical students in Mexico].

PubMed

Amato, Dante; de Jesús Novales-Castro, Xavier

2009-01-01

Assess the degree to which medical students accept and consider useful the techniques of problem based learning (PBL) and evaluation among peers. Analyze the association between the number of PBL clinical cases reviewed and the students' perception about their own learning in a basic course. A questionnaire was administered to 334 students enrolled in the third semester of medical school (Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México). Questions included acceptability of PBL, peer evaluation, and their perception about the usefulness of these techniques after having used them during the school year. We used a Likert scale to measure opinions on the degree of usefulness of the PBL, perception of their own learning, and the acceptance of the notion that evaluation activities evaluation among peers promote justice and favor the student's character formation. We measured the association of these variables with the number of clinical cases studied using Spearman's rank correlation coefficient. Most of the students considered that PBL method is useful (82%) and that evaluation activities among peers promote justice and character formation (70%). Students who reviewed more PBL cases considered the PBL activities more useful (rho = 0.489, p < 0.0001), and perceived that they achieved a better learning experience (rho = 0.200, p < 0.0001). Results show a fair acceptance by the students of the PBL method and activities of peer evaluation. The number of clinical cases reviewed during the course correlated with considering the PBL to be a useful method and perceiving a better learning experience. Our results support the inclusion of PBL and peer evaluation in the medical school curricula.

Learning in Educational Computer Games for Novices: The Impact of Support Provision Types on Virtual Presence, Cognitive Load, and Learning Outcomes

ERIC Educational Resources Information Center

Schrader, Claudia; Bastiaens, Theo

2012-01-01

Embedding support devices in educational computer games has been asserted to positively affect learning outcomes. However, there is only limited direct empirical evidence on which design variations of support provision influence learning. In order to better understand the impact of support design on novices' learning, the current study…

The Effectiveness of Active and Traditional Teaching Techniques in the Orthopedic Assessment Laboratory

ERIC Educational Resources Information Center

Nottingham, Sara; Verscheure, Susan

2010-01-01

Active learning is a teaching methodology with a focus on student-centered learning that engages students in the educational process. This study implemented active learning techniques in an orthopedic assessment laboratory, and the effects of these teaching techniques. Mean scores from written exams, practical exams, and final course evaluations…

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

PubMed

Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Sensibility in the relations and interactions of teaching and learning to be and do nursing.

PubMed

Terra, Marlene Gomes; Gonçalves, Lucia Hisako Takase; dos Santos, Evanguelia Kotzias Atherino; Erdmann, Alacoque Lorenzini

2010-01-01

This qualitative study focused on proxemic feelings and feelings of detachment and ambiguity among professors-nurses concerning their experiences. This study aimed to reveal the meanings of sensibility held by being-professor-nurse in teaching and learning to be and do nursing. The theoretical-philosophical support is based on Merleau-Ponty's existential phenomenological approach and the hermeneutics phenomenology of Paul Ricoeur was used. Nineteen professors-nurses from a Higher Education institution in the South of Brazil were interviewed between November and December 2006. Sensibility was revealed as the capacity to observe details in order to intervene in a situation the best way possible, and also as a way to break with exclusive models of the cognitive-instrumental rationality of science and technique, since sensibility is the basis for developing other ways of teaching and learning to be and do Nursing.

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy.

PubMed

Kawata, Yasuo; Arimura, Hidetaka; Ikushima, Koujirou; Jin, Ze; Morita, Kento; Tokunaga, Chiaki; Yabu-Uchi, Hidetake; Shioyama, Yoshiyuki; Sasaki, Tomonari; Honda, Hiroshi; Sasaki, Masayuki

2017-10-01

The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for GTV regions, which were determined based on the knowledge of radiation oncologists, were fed on a pixel-by-pixel basis into the respective FCM, ANN, and SVM ML techniques. Then, the ML techniques were incorporated into the automated delineation framework of GTVs followed by an optimum contour selection (OCS) method, which we proposed in a previous study. The three-ML-based frameworks were evaluated for 16 lung cancer cases (six solid, four ground glass opacity (GGO), six part-solid GGO) with the datasets of planning computed tomography (CT) and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT images using the three-dimensional Dice similarity coefficient (DSC). DSC denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those estimated using the automated framework. The FCM-based framework achieved the highest DSCs of 0.79±0.06, whereas DSCs of the ANN-based and SVM-based frameworks were 0.76±0.14 and 0.73±0.14, respectively. The FCM-based framework provided the highest segmentation accuracy and precision without a learning process (lowest calculation cost). Therefore, the FCM-based framework can be useful for delineation of tumor regions in practical treatment planning. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Healthcare Learning Community and Student Retention

ERIC Educational Resources Information Center

Johnson, Sherryl W.

2014-01-01

Teaching, learning, and retention processes have evolved historically to include multifaceted techniques beyond the traditional lecture. This article presents related results of a study using a healthcare learning community in a southwest Georgia university. The value of novel techniques and tools in promoting student learning and retention…

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

The implementation of portfolio assessment by the educators on the mathematics learning process in senior high school

NASA Astrophysics Data System (ADS)

Lestariani, Ida; Sujadi, Imam; Pramudya, Ikrar

2018-05-01

Portfolio assessment can shows the development of the ability of learners in a period through the work so that can be seen progress monitored learning of each learner. The purpose of research to describe and know the implementation of portfolio assessment on the mathematics learning process with the Senior High school math teacher class X as the subject because of the importance of applying the assessment for the progress of learning outcomes of learners. This research includes descriptive qualitative research type. Techniques of data collecting is done by observation method, interview and documentation. Data collection then validated using triangulation technique that is observation technique, interview and documentation. Data analysis technique is done by data reduction, data presentation and conclusion. The results showed that the steps taken by teachers in applying portfolio assessment obtained focused on learning outcomes. Student learning outcomes include homework and daily tests. Based on the results of research can be concluded that the implementation of portfolio assessment is the form of learning results are scored. Teachers have not yet implemented other portfolio assessment techniques such as student work.

Supporting nurse mentor development: An exploration of developmental constellations in nursing mentorship practice.

PubMed

MacLaren, Julie-Ann

2018-01-01

Supervised practice as a mentor is currently an integral component of nurse mentor education. However, workplace education literature tends to focus on dyadic mentor-student relationships rather than developmental relationships between colleagues. This paper explores the supportive relationships of nurses undertaking a mentorship qualification, using the novel technique of constellation development to determine the nature of workplace support for this group. Semi-structured interviews were conducted with three recently qualified nurse mentors. All participants developed a mentorship constellation identifying colleagues significant to their own learning in practice. These significant others were also interviewed alongside practice education, and nurse education leads. Constellations were analysed in relation to network size, breadth, strength of relationships, and attributes of individuals. Findings suggest that dyadic forms of supervisory mentorship may not offer the range of skills and attributes that developing mentors require. Redundancy of mentorship attributes within the constellation (overlapping attributes between members) may counteract problems caused when one mentor attempts to fulfil all mentorship roles. Wider nursing teams are well placed to provide the support and supervision required by mentors in training. Where wider and stronger networks were not available to mentorship students, mentorship learning was at risk. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

The Effect of Higher Education Faculty Training in Improvisational Theatre Techniques on Student Learning and Perceptions of Engagement and Faculty Perceptions of Teaching and Learning

ERIC Educational Resources Information Center

Massie, DeAnna

2017-01-01

College instructors are content experts but ineffective at creating engaging and productive learning environments. This mixed methods study explored how improvisational theatre techniques affect college instructors' ability to increase student engagement and learning. Theoretical foundations included engagement, active learning, collaboration and…

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Problem based learning with scaffolding technique on geometry

NASA Astrophysics Data System (ADS)

Bayuningsih, A. S.; Usodo, B.; Subanti, S.

2018-05-01

Geometry as one of the branches of mathematics has an important role in the study of mathematics. This research aims to explore the effectiveness of Problem Based Learning (PBL) with scaffolding technique viewed from self-regulation learning toward students’ achievement learning in mathematics. The research data obtained through mathematics learning achievement test and self-regulated learning (SRL) questionnaire. This research employed quasi-experimental research. The subjects of this research are students of the junior high school in Banyumas Central Java. The result of the research showed that problem-based learning model with scaffolding technique is more effective to generate students’ mathematics learning achievement than direct learning (DL). This is because in PBL model students are more able to think actively and creatively. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

Monitoring Collaborative Activities in Computer Supported Collaborative Learning

ERIC Educational Resources Information Center

Persico, Donatella; Pozzi, Francesca; Sarti, Luigi

2010-01-01

Monitoring the learning process in computer supported collaborative learning (CSCL) environments is a key element for supporting the efficacy of tutor actions. This article proposes an approach for analysing learning processes in a CSCL environment to support tutors in their monitoring tasks. The approach entails tracking the interactions within…

Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

ERIC Educational Resources Information Center

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

Navigation Assistance: A Trade-Off between Wayfinding Support and Configural Learning Support

ERIC Educational Resources Information Center

Munzer, Stefan; Zimmer, Hubert D.; Baus, Jorg

2012-01-01

Current GPS-based mobile navigation assistance systems support wayfinding, but they do not support learning about the spatial configuration of an environment. The present study examined effects of visual presentation modes for navigation assistance on wayfinding accuracy, route learning, and configural learning. Participants (high-school students)…

Transforaminal Lumbar Interbody Fusion with Rigid Interspinous Process Fixation: A Learning Curve Analysis of a Surgeon Team's First 74 Cases.

PubMed

Doherty, Patrick; Welch, Arthur; Tharpe, Jason; Moore, Camille; Ferry, Chris

2017-05-30

Studies have shown that a significant learning curve may be associated with adopting minimally invasive transforaminal lumbar interbody fusion (MIS TLIF) with bilateral pedicle screw fixation (BPSF). Accordingly, several hybrid TLIF techniques have been proposed as surrogates to the accepted BPSF technique, asserting that less/fewer fixation(s) or less disruptive fixation may decrease the learning curve while still maintaining the minimally disruptive benefits. TLIF with interspinous process fixation (ISPF) is one such surrogate procedure. However, despite perceived ease of adaptability given the favorable proximity of the spinous processes, no evidence exists demonstrating whether or not the technique may possess its own inherent learning curve. The purpose of this study was to determine whether an intraoperative learning curve for one- and two-level TLIF + ISPF may exist for a single lead surgeon. Seventy-four consecutive patients who received one- or two-Level TLIF with rigid ISPF by a single lead surgeon were retrospectively reviewed. It was the first TLIF + ISPF case series for the lead surgeon. Intraoperative blood loss (EBL), hospitalization length-of-stay (LOS), fluoroscopy time, and postoperative complications were collected. EBL, LOS, and fluoroscopy time were modeled as a function of case number using multiple linear regression methods. A change point was included in each model to allow the trajectory of the outcomes to change during the duration of the case series. These change points were determined using profile likelihood methods. Models were fit using the maximum likelihood estimates for the change points. Age, sex, body mass index (BMI), and the number of treated levels were included as covariates. EBL, LOS, and fluoroscopy time did not significantly differ by age, sex, or BMI (p ≥ 0.12). Only EBL differed significantly by the number of levels (p = 0.026). The case number was not a significant predictor of EBL, LOS, or fluoroscopy time (p ≥ 0.21). At the time of data collection (mean time from surgery: 13.3 months), six patients had undergone revision due to interbody migration. No ISPF device complications were observed. Study outcomes support the ideal that TLIF + ISPF can be a readily adopted procedure without a significant intraoperative learning curve. However, the authors emphasize that further assessment of long-term healing outcomes is essential in fully characterizing both the efficacy and the indication learning curve for the TLIF + ISPF technique.

Pralidoxime and pesticide poisoning: A question of severity?

PubMed

Walton, Emma Louise

2016-12-01

In this issue of the Biomedical Journal, we highlight new data supporting the use of pralidoxime in the treatment of cases of organophosphate poisoning, which also suggest that WHO treatment guidelines should be updated. We also learn about a modified surgical technique to repair severe spinal injuries, as well as new insight into the structure of human adenovirus that could inform vaccine development. Copyright © 2016 Chang Gung University. Published by Elsevier B.V. All rights reserved.

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia.

PubMed

Tamboer, P; Vorst, H C M; Ghebreab, S; Scholte, H S

2016-01-01

Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique--support vector machine--to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18-21 years), a classification performance of 80% (p < 0.001; d-prime = 1.67) was achieved on the basis of differences in gray matter (sensitivity 82%, specificity 78%). The voxels that were most reliable for classification were found in the left occipital fusiform gyrus (LOFG), in the right occipital fusiform gyrus (ROFG), and in the left inferior parietal lobule (LIPL). Additionally, we found that classification certainty (e.g. the percentage of times a subject was correctly classified) correlated with severity of dyslexia (r = 0.47). Furthermore, various significant correlations were found between the three anatomical regions and behavioural measures of spelling, phonology and whole-word-reading. No correlations were found with behavioural measures of short-term memory and visual/attentional confusion. These data indicate that the LOFG, ROFG and the LIPL are neuro-endophenotype and potentially biomarkers for types of dyslexia related to reading, spelling and phonology. In a second and independent sample of 876 young adults of a general population, the trained classifier of the first sample was tested, resulting in a classification performance of 59% (p = 0.07; d-prime = 0.65). This decline in classification performance resulted from a large percentage of false alarms. This study provided support for the use of machine learning in anatomical brain imaging.

Data-driven mapping of the potential mountain permafrost distribution.

PubMed

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2017-07-15

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km 2 area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping. Copyright © 2017 Elsevier B.V. All rights reserved.

Support vector machines for nuclear reactor state estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformedmore » into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.« less

Movement Sonification: Effects on Motor Learning beyond Rhythmic Adjustments.

PubMed

Effenberg, Alfred O; Fehse, Ursula; Schmitz, Gerd; Krueger, Bjoern; Mechling, Heinz

2016-01-01

Motor learning is based on motor perception and emergent perceptual-motor representations. A lot of behavioral research is related to single perceptual modalities but during last two decades the contribution of multimodal perception on motor behavior was discovered more and more. A growing number of studies indicates an enhanced impact of multimodal stimuli on motor perception, motor control and motor learning in terms of better precision and higher reliability of the related actions. Behavioral research is supported by neurophysiological data, revealing that multisensory integration supports motor control and learning. But the overwhelming part of both research lines is dedicated to basic research. Besides research in the domains of music, dance and motor rehabilitation, there is almost no evidence for enhanced effectiveness of multisensory information on learning of gross motor skills. To reduce this gap, movement sonification is used here in applied research on motor learning in sports. Based on the current knowledge on the multimodal organization of the perceptual system, we generate additional real-time movement information being suitable for integration with perceptual feedback streams of visual and proprioceptive modality. With ongoing training, synchronously processed auditory information should be initially integrated into the emerging internal models, enhancing the efficacy of motor learning. This is achieved by a direct mapping of kinematic and dynamic motion parameters to electronic sounds, resulting in continuous auditory and convergent audiovisual or audio-proprioceptive stimulus arrays. In sharp contrast to other approaches using acoustic information as error-feedback in motor learning settings, we try to generate additional movement information suitable for acceleration and enhancement of adequate sensorimotor representations and processible below the level of consciousness. In the experimental setting, participants were asked to learn a closed motor skill (technique acquisition of indoor rowing). One group was treated with visual information and two groups with audiovisual information (sonification vs. natural sounds). For all three groups learning became evident and remained stable. Participants treated with additional movement sonification showed better performance compared to both other groups. Results indicate that movement sonification enhances motor learning of a complex gross motor skill-even exceeding usually expected acoustic rhythmic effects on motor learning.

Movement Sonification: Effects on Motor Learning beyond Rhythmic Adjustments

PubMed Central

Effenberg, Alfred O.; Fehse, Ursula; Schmitz, Gerd; Krueger, Bjoern; Mechling, Heinz

2016-01-01

Motor learning is based on motor perception and emergent perceptual-motor representations. A lot of behavioral research is related to single perceptual modalities but during last two decades the contribution of multimodal perception on motor behavior was discovered more and more. A growing number of studies indicates an enhanced impact of multimodal stimuli on motor perception, motor control and motor learning in terms of better precision and higher reliability of the related actions. Behavioral research is supported by neurophysiological data, revealing that multisensory integration supports motor control and learning. But the overwhelming part of both research lines is dedicated to basic research. Besides research in the domains of music, dance and motor rehabilitation, there is almost no evidence for enhanced effectiveness of multisensory information on learning of gross motor skills. To reduce this gap, movement sonification is used here in applied research on motor learning in sports. Based on the current knowledge on the multimodal organization of the perceptual system, we generate additional real-time movement information being suitable for integration with perceptual feedback streams of visual and proprioceptive modality. With ongoing training, synchronously processed auditory information should be initially integrated into the emerging internal models, enhancing the efficacy of motor learning. This is achieved by a direct mapping of kinematic and dynamic motion parameters to electronic sounds, resulting in continuous auditory and convergent audiovisual or audio-proprioceptive stimulus arrays. In sharp contrast to other approaches using acoustic information as error-feedback in motor learning settings, we try to generate additional movement information suitable for acceleration and enhancement of adequate sensorimotor representations and processible below the level of consciousness. In the experimental setting, participants were asked to learn a closed motor skill (technique acquisition of indoor rowing). One group was treated with visual information and two groups with audiovisual information (sonification vs. natural sounds). For all three groups learning became evident and remained stable. Participants treated with additional movement sonification showed better performance compared to both other groups. Results indicate that movement sonification enhances motor learning of a complex gross motor skill—even exceeding usually expected acoustic rhythmic effects on motor learning. PMID:27303255

A Survey on Ambient Intelligence in Health Care

PubMed Central

Acampora, Giovanni; Cook, Diane J.; Rashidi, Parisa; Vasilakos, Athanasios V.

2013-01-01

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people’s capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users’ goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths. PMID:24431472

A Survey on Ambient Intelligence in Health Care.

PubMed

Acampora, Giovanni; Cook, Diane J; Rashidi, Parisa; Vasilakos, Athanasios V

2013-12-01

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

Real time flaw detection and characterization in tube through partial least squares and SVR: Application to eddy current testing

NASA Astrophysics Data System (ADS)

Ahmed, Shamim; Miorelli, Roberto; Calmon, Pierre; Anselmi, Nicola; Salucci, Marco

2018-04-01

This paper describes Learning-By-Examples (LBE) technique for performing quasi real time flaw localization and characterization within a conductive tube based on Eddy Current Testing (ECT) signals. Within the framework of LBE, the combination of full-factorial (i.e., GRID) sampling and Partial Least Squares (PLS) feature extraction (i.e., GRID-PLS) techniques are applied for generating a suitable training set in offine phase. Support Vector Regression (SVR) is utilized for model development and inversion during offine and online phases, respectively. The performance and robustness of the proposed GIRD-PLS/SVR strategy on noisy test set is evaluated and compared with standard GRID/SVR approach.

Comparative Study on the Different Testing Techniques in Tree Classification for Detecting the Learning Motivation

NASA Astrophysics Data System (ADS)

Juliane, C.; Arman, A. A.; Sastramihardja, H. S.; Supriana, I.

2017-03-01

Having motivation to learn is a successful requirement in a learning process, and needs to be maintained properly. This study aims to measure learning motivation, especially in the process of electronic learning (e-learning). Here, data mining approach was chosen as a research method. For the testing process, the accuracy comparative study on the different testing techniques was conducted, involving Cross Validation and Percentage Split. The best accuracy was generated by J48 algorithm with a percentage split technique reaching at 92.19 %. This study provided an overview on how to detect the presence of learning motivation in the context of e-learning. It is expected to be good contribution for education, and to warn the teachers for whom they have to provide motivation.

Effects of Enhancement Techniques on L2 Incidental Vocabulary Learning

ERIC Educational Resources Information Center

Duan, Shiping

2018-01-01

Enhancement Techniques are conducive to incidental vocabulary learning. This study investigated the effects of two types of enhancement techniques-multiple-choice glosses (MC) and L1 single-gloss (SG) on L2 incidental learning of new words and retention of them. A total of 89 university learners of English as a Foreign Language (EFL) were asked to…

A Framework to Support Mobile Learning in Multilingual Environments

ERIC Educational Resources Information Center

Jantjies, Mmaki E.; Joy, Mike

2014-01-01

This paper presents a multilingual mobile learning framework that can be used to support the pedagogical development of mobile learning systems which can support learning in under-resourced multilingual schools. The framework has been developed following two empirical mobile learning studies. Both studies were conducted in multilingual South…

Modified UTAUT2 Model for M-Learning among Students in India

ERIC Educational Resources Information Center

Bharati, V. Jayendra; Srikanth, R.

2018-01-01

Ubiquitous technologies have a great potential to enrich students' academic experience. Students are more interested in using interactive learning techniques apart from the traditional learning techniques. Several research studies for m-learning has been done in the USA, UK concentrating on students undergoing a graduation degree, especially…

Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

NASA Astrophysics Data System (ADS)

Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

2018-04-01

The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

PubMed Central

Wu, Jianning; Wu, Bin

2015-01-01

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis. PMID:25705672

The novel quantitative technique for assessment of gait symmetry using advanced statistical learning algorithm.

PubMed

Wu, Jianning; Wu, Bin

2015-01-01

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

An application of machine learning to the organization of institutional software repositories

NASA Technical Reports Server (NTRS)

Bailin, Sidney; Henderson, Scott; Truszkowski, Walt

1993-01-01

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application.

Valid and Reliable Science Content Assessments for Science Teachers

NASA Astrophysics Data System (ADS)

Tretter, Thomas R.; Brown, Sherri L.; Bush, William S.; Saderholm, Jon C.; Holmes, Vicki-Lynn

2013-03-01

Science teachers' content knowledge is an important influence on student learning, highlighting an ongoing need for programs, and assessments of those programs, designed to support teacher learning of science. Valid and reliable assessments of teacher science knowledge are needed for direct measurement of this crucial variable. This paper describes multiple sources of validity and reliability (Cronbach's alpha greater than 0.8) evidence for physical, life, and earth/space science assessments—part of the Diagnostic Teacher Assessments of Mathematics and Science (DTAMS) project. Validity was strengthened by systematic synthesis of relevant documents, extensive use of external reviewers, and field tests with 900 teachers during assessment development process. Subsequent results from 4,400 teachers, analyzed with Rasch IRT modeling techniques, offer construct and concurrent validity evidence.

Effects of intrinsic motivation on feedback processing during learning.

PubMed

DePasque, Samantha; Tricomi, Elizabeth

2015-10-01

Learning commonly requires feedback about the consequences of one's actions, which can drive learners to modify their behavior. Motivation may determine how sensitive an individual might be to such feedback, particularly in educational contexts where some students value academic achievement more than others. Thus, motivation for a task might influence the value placed on performance feedback and how effectively it is used to improve learning. To investigate the interplay between intrinsic motivation and feedback processing, we used functional magnetic resonance imaging (fMRI) during feedback-based learning before and after a novel manipulation based on motivational interviewing, a technique for enhancing treatment motivation in mental health settings. Because of its role in the reinforcement learning system, the striatum is situated to play a significant role in the modulation of learning based on motivation. Consistent with this idea, motivation levels during the task were associated with sensitivity to positive versus negative feedback in the striatum. Additionally, heightened motivation following a brief motivational interview was associated with increases in feedback sensitivity in the left medial temporal lobe. Our results suggest that motivation modulates neural responses to performance-related feedback, and furthermore that changes in motivation facilitate processing in areas that support learning and memory. Copyright © 2015. Published by Elsevier Inc.

Using Computational Modeling to Assess the Impact of Clinical Decision Support on Cancer Screening within Community Health Centers

PubMed Central

Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.

2014-01-01

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman’s Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability. PMID:24953241

A holistic approach to supporting staff in a pediatric hospital setting.

PubMed

Schwerman, Nichole; Stellmacher, Judy

2012-09-01

Health care professionals experience significant stress in the workplace. Building opportunities for health care professionals to manage stress is essential. Children's Hospital of Wisconsin designed a holistic set of programs called the R&R Series to support the emotional, cognitive, and spiritual health of staff and assist staff in using self-care strategies to build resiliency. Six hundred seventy program evaluations were collected during a 1-year pilot series. Program participants were from a wide variety of departments throughout the health care system. Staff reported feeling more supported, being better able to manage work and life stress, and practicing the self-care techniques they learned. Programs such as the R&R Series are one way to promote the overall health and resiliency of health care professionals. Copyright 2012, SLACK Incorporated.

The Mediating Effect of Intrinsic Motivation to Learn on the Relationship between Student´s Autonomy Support and Vitality and Deep Learning.

PubMed

Núñez, Juan L; León, Jaime

2016-07-18

Self-determination theory has shown that autonomy support in the classroom is associated with an increase of students' intrinsic motivation. Moreover, intrinsic motivation is related with positive outcomes. This study examines the relationships between autonomy support, intrinsic motivation to learn and two motivational consequences, deep learning and vitality. Specifically, the hypotheses were that autonomy support predicts the two types of consequences, and that autonomy support directly and indirectly predicts the vitality and the deep learning through intrinsic motivation to learn. Participants were 276 undergraduate students. The mean age was 21.80 years (SD = 2.94). Structural equation modeling was used to test the relationships between variables and delta method was used to analyze the mediating effect of intrinsic motivation to learn. Results indicated that student perception of autonomy support had a positive effect on deep learning and vitality (p < .001). In addition, these associations were mediated by intrinsic motivation to learn. These findings suggest that teachers are key elements in generating of autonomy support environment to promote intrinsic motivation, deep learning, and vitality in classroom. Educational implications are discussed.

Facilitation of learning: part 2.

PubMed

Warburton, Tyler; Houghton, Trish; Barry, Debbie

2016-04-27

The previous article in this series of 11, Facilitation of learning: part 1, reviewed learning theories and how they relate to clinical practice. Developing an understanding of these theories is essential for mentors and practice teachers to enable them to deliver evidence-based learning support. This is important given that effective learning support is dependent on an educator who possesses knowledge of their specialist area as well as the relevent tools and methods to support learning. The second domain of the Nursing and Midwifery Council's Standards to Support Learning and Assessment in Practice relates to the facilitation of learning. To fulfil this domain, mentors and practice teachers are required to demonstrate their ability to recognise the needs of learners and provide appropriate support to meet those needs. This article expands on some of the discussions from part 1 of this article and considers these from a practical perspective, in addition to introducing some of the tools that can be used to support learning.

Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint.

PubMed

Saito, Priscila T M; Nakamura, Rodrigo Y M; Amorim, Willian P; Papa, João P; de Rezende, Pedro J; Falcão, Alexandre X

2015-01-01

Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.

A work-based learning approach for clinical support workers on mental health inpatient wards.

PubMed

Kemp, Philip; Gilding, Moorene; Seewooruttun, Khooseal; Walsh, Hannah

2016-09-14

Background With a rise in the number of unqualified staff providing health and social care, and reports raising concerns about the quality of care provided, there is a need to address the learning needs of clinical support workers. This article describes a qualitative evaluation of a service improvement project that involved a work-based learning approach for clinical support workers on mental health inpatient wards. Aim To investigate and identify insights in relation to the content and process of learning using a work-based learning approach for clinical support workers. Method This was a qualitative evaluation of a service improvement project involving 25 clinical support workers at the seven mental health inpatient units in South London and Maudsley NHS Foundation Trust. Three clinical skills tutors were appointed to develop, implement and evaluate the work-based learning approach. Four sources of data were used to evaluate this approach, including reflective journals, qualitative responses to questionnaires, three focus groups involving the clinical support workers and a group interview involving the clinical skills tutors. Data were analysed using thematic analysis. Findings The work-based learning approach was highly valued by the clinical support workers and enhanced learning in practice. Face-to-face learning in practice helped the clinical support workers to develop practice skills and reflective learning skills. Insights relating to the role of clinical support workers were also identified, including the benefits of face-to-face supervision in practice, particularly in relation to the interpersonal aspects of care. Conclusion A work-based learning approach has the potential to enhance care delivery by meeting the learning needs of clinical support workers and enabling them to apply learning to practice. Care providers should consider how the work-based learning approach can be used on a systematic, organisation-wide basis in the context of budgetary restrictions.

The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine

NASA Astrophysics Data System (ADS)

Taha, Z.; Razman, M. A. M.; Adnan, F. A.; Ghani, A. S. Abdul; Majeed, A. P. P. Abdul; Musa, R. M.; Sallehudin, M. F.; Mukai, Y.

2018-03-01

Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by fish will be dissolved in the water and thus, reduce the water quality (oxygen quantity in the water will be reduced). The reduction of oxygen (water quality) leads the fish to die and in some cases, may lead to fish diseases. This study correlates Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique. The behaviour is clustered with respect to the position of the centre of gravity of the school of fish prior feeding, during feeding and after feeding. The clustered fish behaviour is then classified by means of a machine learning technique namely Support vector machine (SVM). It has been shown from the study that the Fine Gaussian variation of SVM is able to provide a reasonably accurate classification of fish feeding behaviour with a classification accuracy of 79.7%. The proposed integration technique may increase the usefulness of the captured data and thus better differentiates the various behaviour of farmed fishes.

Music to knowledge: A visual programming environment for the development and evaluation of music information retrieval techniques

NASA Astrophysics Data System (ADS)

Ehmann, Andreas F.; Downie, J. Stephen

2005-09-01

The objective of the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) project is the creation of a large, secure corpus of audio and symbolic music data accessible to the music information retrieval (MIR) community for the testing and evaluation of various MIR techniques. As part of the IMIRSEL project, a cross-platform JAVA based visual programming environment called Music to Knowledge (M2K) is being developed for a variety of music information retrieval related tasks. The primary objective of M2K is to supply the MIR community with a toolset that provides the ability to rapidly prototype algorithms, as well as foster the sharing of techniques within the MIR community through the use of a standardized set of tools. Due to the relatively large size of audio data and the computational costs associated with some digital signal processing and machine learning techniques, M2K is also designed to support distributed computing across computing clusters. In addition, facilities to allow the integration of non-JAVA based (e.g., C/C++, MATLAB, etc.) algorithms and programs are provided within M2K. [Work supported by the Andrew W. Mellon Foundation and NSF Grants No. IIS-0340597 and No. IIS-0327371.

The Virtual Learning Commons: An Emerging Technology for Learning About Emerging Technologies

NASA Astrophysics Data System (ADS)

Pennington, D. D.; Del Rio, N.; Fierro, C.; Gandara, A.; Garcia, A.; Garza, J.; Giandoni, M.; Ochoa, O.; Padilla, E.; Salamah, S.

2013-12-01

The Virtual Learning Commons (VLC), funded by the National Science Foundation Office of Cyberinfrastructure CI-Team Program, is a combination of semantic, visualization, and social media tools that support knowledge sharing and innovation across research disciplines. The explosion of new scientific tools and techniques challenges the ability of researchers to be aware of emerging technologies that might benefit them. Even when aware, it can be difficult to understand enough about emerging technologies to become potential adopters or re-users. Often, emerging technologies have little documentation, especially about the context of their use. The VLC tackles this challenge by providing mechanisms for individuals and groups of researchers to collectively organize Web resources through social bookmarking, and engage each other around those collections in order to a) learn about potentially relevant technologies that are emerging; and b) get feedback from other researchers on innovative ideas and designs. Concurrently, developers of emerging technologies can learn about potential users and the issues they encounter, and they can analyze the impact of their tools on other projects. The VLC aims to support the 'fuzzy front end' of innovation, where novel ideas emerge and there is the greatest potential for impact on research design. It is during the fuzzy front end that conceptual collisions across disciplines and exposure to diverse perspectives provide opportunity for creative thinking that can lead to inventive outcomes. This presentation will discuss the innovation theories that have informed design of the VLC, and hypotheses about the flow of information in virtual settings that can enable the process of innovation. The presentation will include a brief demonstration of key capabilities within the VLC that enable learning about emerging technologies, including the technologies that are presented in this session.

Sustained Professional Development on Cooperative Learning: Impact on Six Teachers' Practices and Students' Learning.

PubMed

Goodyear, Victoria A

2017-03-01

It has been argued, extensively and internationally, that sustained school-based continuous professional development (CPD) has the potential to overcome some of the shortcomings of traditional one-off CPD programs. Yet, the evidence base on more effective or less effective forms of CPD is contradictory. The mechanisms by which sustained support should be offered are unclear, and the impacts on teachers' and students' learning are complex and difficult to track. The purpose of this study was to examine the impact of a sustained school-based, tailored, and supported CPD program on teachers' practices and students' learning. Data are reported from 6 case studies of individual teachers engaged in a yearlong CPD program focused on cooperative learning. The CPD program involved participatory action research and frequent interaction/support from a boundary spanner (researcher/facilitator). Data were gathered from 29 video-recorded lessons, 108 interviews, and 35 field journal entries. (a) Individualized (external) support, (b) departmental (internal) support, and (c) sustained support impacted teachers' practices of cooperative learning. The teachers adapted their practices of cooperative learning in response to their students' learning needs. Teachers began to develop a level of pedagogical fluency, and in doing so, teachers advanced students' learning. Because this study demonstrates impact, it contributes to international literature on effective CPD. The key contribution is the detailed evidence about how and why CPD supported 6 individual teachers to learn-differently-and the complexity of the learning support required to engage in ongoing curriculum development to positively impact student learning.

Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

ERIC Educational Resources Information Center

Choi, Woojae; Jacobs, Ronald L.

2011-01-01

While workplace learning includes formal and informal learning, the relationship between the two has been overlooked, because they have been viewed as separate entities. This study investigated the effects of formal learning, personal learning orientation, and supportive learning environment on informal learning among 203 middle managers in Korean…

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Minimizing Confusion and Disorientation: Cognitive Support Work in Informal Dementia Caregiving

PubMed Central

Berry, Brandon

2015-01-01

Drawing from ethnographic fieldwork and in-depth interviews, I explain how informal dementia caregivers attempt to reduce the affected individual’s moments of confusion and disorientation through cognitive support work. I identify three stages through which such support takes shape and then gradually declines in usage. In a first stage, family members collaborate with affected individuals to first identify and then to avoid “triggers” that elicit sudden bouts of confusion. In a second stage, caregivers lose the effective collaboration of the affected individual and begin unilateral attempts to minimize confused states through pre-emptive conversational techniques, third-party interactional support, and social-environment shifts. In a third stage, caregivers learn that the affected individual has reached a level of impairment that does not respond well to efforts at reduction and begin abandoning strategies. I identify the motivations driving cognitive support work and discuss the role of lay health knowledge in dementia caregiving. I conclude by considering the utility of cognitive support as a concept within dementia caregiving. PMID:24984915

Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help?

ERIC Educational Resources Information Center

Jeong, Heisawn; Hmelo-Silver, Cindy E.

2016-01-01

This article proposes 7 core affordances of technology for collaborative learning based on theories of collaborative learning and CSCL (Computer-Supported Collaborative Learning) practices. Technology affords learner opportunities to (1) engage in a joint task, (2) communicate, (3) share resources, (4) engage in productive collaborative learning…

Learning and Tuning of Fuzzy Rules

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

Limited transfer of long-term motion perceptual learning with double training.

PubMed

Liang, Ju; Zhou, Yifeng; Fahle, Manfred; Liu, Zili

2015-01-01

A significant recent development in visual perceptual learning research is the double training technique. With this technique, Xiao, Zhang, Wang, Klein, Levi, and Yu (2008) have found complete transfer in tasks that had previously been shown to be stimulus specific. The significance of this finding is that this technique has since been successful in all tasks tested, including motion direction discrimination. Here, we investigated whether or not this technique could generalize to longer-term learning, using the method of constant stimuli. Our task was learning to discriminate motion directions of random dots. The second leg of training was contrast discrimination along a new average direction of the same moving dots. We found that, although exposure of moving dots along a new direction facilitated motion direction discrimination, this partial transfer was far from complete. We conclude that, although perceptual learning is transferrable under certain conditions, stimulus specificity also remains an inherent characteristic of motion perceptual learning.

Recent developments in machine learning applications in landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Lun, Na Kai; Liew, Mohd Shahir; Matori, Abdul Nasir; Zawawi, Noor Amila Wan Abdullah

2017-11-01

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field.

Learning temporal rules to forecast instability in continuously monitored patients.

PubMed

Guillame-Bert, Mathieu; Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

2017-01-01

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries

NASA Astrophysics Data System (ADS)

Phan, Claude; Raheja, Amar; Bhandari, Subodh; Green, Robert L.; Do, Dat

2017-05-01

Millions of Americans come into contact with turfgrass on a daily basis. Often undervalued and seen as visual support stimulus for a larger entity, millions of acres of turfgrass can be found on residential lawns (which also provides an area for recreation), commercial landscape, parks, athletic fields, and golf courses. Besides these uses, turfgrass provides many functional benefits to the environment, such as reducing soil erosion, cooling its surrounding area, and soil carbon sequestration. However, rapidly expanding uses of turfgrass have also raised alarm for natural resources conservation and environmental quality, the largest impact being water consumption. This paper presents a machine learning approach that can assist growers and researchers in determining the overall quality and color rating of turfgrass, thereby assisting in turfgrass management including optimized irrigation water scheduling. Tools from Google and NVIDIA enable models to be trained using deep learning techniques on personal computers or on small form factor processors that can be used aboard small unmanned aerial vehicles (UAVs). The typical evaluation process is a long, laborious process, which is subjective by nature, and thus often exposed to criticism and concern. A computational approach to quality and color assessment will provide faster, accurate, and more consistent ratings, which in turn will help increase irrigation water use efficiency. The overall goal of the ongoing research is to use deep learning techniques and UAV imageries for the turfgrass quality and color assessment and help all the stakeholders to optimize water conservation.

A Digital Coach That Provides Affective and Social Learning Support to Low-Literate Learners

ERIC Educational Resources Information Center

Schouten, Dylan G. M.; Venneker, Fleur; Bosse, Tibor; Neerincx, Mark A.; Cremers, Anita H. M.

2018-01-01

In this study, we investigate if a digital coach for low-literate learners that provides cognitive learning support based on scaffolding can be improved by adding affective learning support based on motivational interviewing, and social learning support based on small talk. Several knowledge gaps are identified: motivational interviewing and small…

Clinical teaching and learning within a preceptorship model in an acute care hospital in Ireland; a qualitative study.

PubMed

McSharry, Edel; Lathlean, Judith

2017-04-01

A preceptorship model of clinical teaching was introduced to support the new all-graduate nurse education programme in Ireland in 2002. Little is known about how this model impacts upon the pedagogical practices of the preceptor or student learning in clinical practice leading to question what constitutes effective teaching and learning in clinical practice at undergraduate level. This study aimed to explore the clinical teaching and learning within a preceptorship model in an acute care hospital in Ireland and identify when best practice, based on current theoretical professional and educational principles occurred. A qualitative research study of a purposively selected sample of 13 students and 13 preceptors, working together in four clinical areas in one hospital in Ireland. Methods were semi-structured interviews, analysed thematically, complemented by documentary analysis relating to the teaching and assessment of the students. Ethical approval was gained from the hospital's Ethics Committee. Preceptor-student contact time within an empowering student-preceptor learning relationship was the foundation of effective teaching and learning and assessment. Dialoguing and talking through practice enhanced the students' knowledge and understanding, while the ability of the preceptor to ask higher order questions promoted the students' clinical reasoning and problem solving skills. Insufficient time to teach, and an over reliance on students' ability to participate in and contribute to practice with minimal guidance were found to negatively impact students' learning. Concepts such as cognitive apprenticeship, scaffolding and learning in communities of practice can be helpful in understanding the processes entailed in preceptorship. Preceptors need extensive educational preparation and support to ensure they have the pedagogical competencies necessary to provide the cognitive teaching techniques that foster professional performance and clinical reasoning. National competency based standards for preceptor preparation should be developed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Practising What We Teach: Vocational Teachers Learn to Research through Applying Action Learning Techniques

ERIC Educational Resources Information Center

Lasky, Barbara; Tempone, Irene

2004-01-01

Action learning techniques are well suited to the teaching of organisation behaviour students because of their flexibility, inclusiveness, openness, and respect for individuals. They are no less useful as a tool for change for vocational teachers, learning, of necessity, to become researchers. Whereas traditional universities have always had a…