Service-Learning's Ongoing Journey as a Method of Instruction: Implications for School-Based Agricultural Education

ERIC Educational Resources Information Center

Roberts, Richie; Edwards, M. Craig

2015-01-01

American education's journey has witnessed the rise and fall of various progressive education approaches, including service-learning. In many respects, however, service-learning is still undergoing formation and adoption as a teaching method, specifically in School-Based, Agricultural Education (SBAE). For this reason, the interest existed to…

From Continuous Improvement to Organisational Learning: Developmental Theory.

ERIC Educational Resources Information Center

Murray, Peter; Chapman, Ross

2003-01-01

Explores continuous improvement methods, which underlie total quality management, finding barriers to implementation in practice that are related to a one-dimensional approach. Suggests a multiple, unbounded learning cycle, a holistic approach that includes adaptive learning, learning styles, generative learning, and capability development.…

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.

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

37: COMPARISON OF TWO METHODS: TBL-BASED AND LECTURE-BASED LEARNING IN NURSING CARE OF PATIENTS WITH DIABETES IN NURSING STUDENTS

PubMed Central

Khodaveisi, Masoud; Qaderian, Khosro; Oshvandi, Khodayar; Soltanian, Ali Reza; Vardanjani, Mehdi molavi

2017-01-01

Background and aims learning plays an important role in developing nursing skills and right care-taking. The Present study aims to evaluate two learning methods based on team –based learning and lecture-based learning in learning care-taking of patients with diabetes in nursing students. Method In this quasi-experimental study, 64 students in term 4 in nursing college of Bukan and Miandoab were included in the study based on knowledge and performance questionnaire including 15 questions based on knowledge and 5 questions based on performance on care-taking in patients with diabetes were used as data collection tool whose reliability was confirmed by cronbach alpha (r=0.83) by the researcher. To compare the mean score of knowledge and performance in each group in pre-test step and post-test step, pair –t test and to compare mean of scores in two groups of control and intervention, the independent t- test was used. Results There was not significant statistical difference between two groups in pre terms of knowledge and performance score (p=0.784). There was significant difference between the mean of knowledge scores and diabetes performance in the post-test in the team-based learning group and lecture-based learning group (p=0.001). There was significant difference between the mean score of knowledge of diabetes care in pre-test and post-test in base learning groups (p=0.001). Conclusion In both methods team-based and lecture-based learning approaches resulted in improvement in learning in students, but the rate of learning in the team-based learning approach is greater compared to that of lecture-based learning and it is recommended that this method be used as a higher education method in the education of students.

The Effects of Mobile Natural-Science Learning Based on the 5E Learning Cycle: A Case Study

ERIC Educational Resources Information Center

Liu, Tzu-Chien; Peng, Hsinyi; Wu, Wen-Hsuan; Lin, Ming-Sheng

2009-01-01

This study has three major purposes, including designing mobile natural-science learning activities that rest on the 5E Learning Cycle, examining the effects of these learning activities on students' performances of learning aquatic plants, and exploring students' perceptions toward these learning activities. A case-study method is utilized and…

Moving Beyond the Training Room: Fostering Workplace Learning through Online Journaling

ERIC Educational Resources Information Center

Cyboran, Vincent L.

2005-01-01

A variety of instructional methods have been shown to be effective in fostering employee learning in workplace training. These include problem-based learning, cooperative learning, and situated learning. Despite their success, however, there are at least two important reasons to actively foster learning beyond the training room: The transfer of…

Active learning in capstone design courses.

PubMed

Goldberg, Jay R

2012-01-01

There is a growing trend to encourage students to take a more active role in their own education. Many schools are moving away from the sage on the stage to the guide on the side model where the instructor is a facilitator of learning. In this model, the emphasis is more on learning and less on teaching, and it requires instructors to incorporate more active and student-centered learning methods into their courses. These methods include collaborative, cooperative, problem-based, and project-based learning.

A Framework for Problem-Based Learning: Teaching Mathematics with a Relational Problem-Based Pedagogy

ERIC Educational Resources Information Center

Schettino, Carmel

2016-01-01

One recommendation for encouraging young women and other underrepresented students in their mathematical studies is to find instructional methods, such as problem-based learning (PBL), that allow them to feel included in the learning process. Using a more relationally centered pedagogy along with more inclusive instructional methods may be a way…

Accommodating Students' Sensory Learning Modalities in Online Formats

ERIC Educational Resources Information Center

Allison, Barbara N.; Rehm, Marsha L.

2016-01-01

Online classes have become a popular and viable method of educating students in both K-12 settings and higher education, including in family and consumer sciences (FCS) programs. Online learning dramatically affects the way students learn. This article addresses how online learning can accommodate the sensory learning modalities (sight, hearing,…

Applications of Machine Learning and Rule Induction,

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

A Comparison of Foreign Language Teaching Methods: Total Physical Response versus Song/Chants with Kindergartners.

ERIC Educational Resources Information Center

Omari, Deena Rae

Several teaching methods aid young children in learning foreign languages, all of which include continuous repetition and review of learned information. The two methods used in this study were Total Physical Response (TPR) and songs/chants. The TPR method used a gesture for each vocabulary card, and the songs/chants method incorporated Spanish…

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Enhancing Student Learning in Knowledge-Based Courses: Integrating Team-Based Learning in Mass Communication Theory Classes

ERIC Educational Resources Information Center

Han, Gang; Newell, Jay

2014-01-01

This study explores the adoption of the team-based learning (TBL) method in knowledge-based and theory-oriented journalism and mass communication (J&MC) courses. It first reviews the origin and concept of TBL, the relevant theories, and then introduces the TBL method and implementation, including procedures and assessments, employed in an…

What Will Users Expect from Virtual Learning Methods? A Conceptual Model to Analyze Future Leaning Method Enhancements

ERIC Educational Resources Information Center

Perera, Indika

2010-01-01

ICT (information and communication technologies) add enormous approaches to utilize computing into users' daily lives. Every aspect of social needs has been touched by ICT, including learning. VL (virtual learning), with the life span of slightly above a decade, still looks for possible approaches to enhance its functions with significant pressure…

How does tele-learning compare with other forms of education delivery? A systematic review of tele-learning educational outcomes for health professionals.

PubMed

Tomlinson, Jo; Shaw, Tim; Munro, Ana; Johnson, Ros; Madden, D Lynne; Phillips, Rosemary; McGregor, Deborah

2013-11-01

Telecommuniciation technologies, including audio and videoconferencing facilities, afford geographically dispersed health professionals the opportunity to connect and collaborate with others. Recognised for enabling tele-consultations and tele-collaborations between teams of health care professionals and their patients, these technologies are also well suited to the delivery of distance learning programs, known as tele-learning. To determine whether tele-learning delivery methods achieve equivalent learning outcomes when compared with traditional face-to-face education delivery methods. A systematic literature review was commissioned by the NSW Ministry of Health to identify results relevant to programs applying tele-learning delivery methods in the provision of education to health professionals. The review found few studies that rigorously compared tele-learning with traditional formats. There was some evidence, however, to support the premise that tele-learning models achieve comparable learning outcomes and that participants are generally satisfied with and accepting of this delivery method. The review illustrated that tele-learning technologies not only enable distance learning opportunities, but achieve comparable learning outcomes to traditional face-to-face models. More rigorous evidence is required to strengthen these findings and should be the focus of future tele-learning research.

Virtual Learning Ecosystems: A Proposed Framework for Integrating Educational Games, E-Learning Methods, and Virtual Community Platforms

ERIC Educational Resources Information Center

Washington, Christopher

2015-01-01

Digitally delivered learning shows the promise of enhancing learner motivation and engagement, advancing critical thinking skills, encouraging reflection and knowledge sharing, and improving professional self-efficacy. Digital learning objects take many forms including interactive media, apps and games, video and other e-learning activities and…

The Effects of Experiential Learning with an Emphasis on Reflective Writing on Deep-Level Processing of Leadership Students

ERIC Educational Resources Information Center

Moore, Copie; Boyd, Barry L.; Dooley, Kim E.

2010-01-01

Experiential learning and reflective writing are important components of college instructors' repertoires. Learning is not complete without proper reflection. The purpose of this study was to examine undergraduate students' perceptions of learning in a leadership course that emphasized experiential learning methods. The respondents included the…

The Impact of Changing Technology: The Case of E-Learning

ERIC Educational Resources Information Center

Yusuf, Nadia; Al-Banawi, Nisreen

2013-01-01

For centuries, education has relied on classroom methods, but technology-enhanced learning can potentially bring about a revolution in learning, making high-quality, cost-effective education available to a greater number of people. The basic advantages of e-learning include anytime-anywhere access to learning, cost reductions, ability to reach…

Study on process evaluation model of students' learning in practical course

NASA Astrophysics Data System (ADS)

Huang, Jie; Liang, Pei; Shen, Wei-min; Ye, Youxiang

2017-08-01

In practical course teaching based on project object method, the traditional evaluation methods include class attendance, assignments and exams fails to give incentives to undergraduate students to learn innovatively and autonomously. In this paper, the element such as creative innovation, teamwork, document and reporting were put into process evaluation methods, and a process evaluation model was set up. Educational practice shows that the evaluation model makes process evaluation of students' learning more comprehensive, accurate, and fairly.

[Introduction of active learning and student readership in teaching by the pharmaceutical faculty].

PubMed

Sekiguchi, Masaki; Yamato, Ippei; Kato, Tetsuta; Torigoe, Kojyun

2005-07-01

We have introduced improvements and new approaches into our teaching methods by exploiting 4 active learning methods for pharmacy students of first year. The 4 teaching methods for each lesson or take home assignment are follows: 1) problem-based learning (clinical case) including a student presentation of the clinical case, 2) schematic drawings of the human organs, one drawing done in 15-20 min during the week following a lecture and a second drawing done with reference to a professional textbook, 3) learning of professional themes in take home assignments, and 4) short test in order to confirm the understanding of technical terms by using paper or computer. These improvements and new methods provide active approaches for pharmacy students (as opposed to passive memorization of words and image study). In combination, they have proven to be useful as a learning method to acquire expert knowledge and to convert from passive learning approach to active learning approach of pharmacy students in the classroom.

Problem-Based Learning Method: Secondary Education 10th Grade Chemistry Course Mixtures Topic

ERIC Educational Resources Information Center

Üce, Musa; Ates, Ismail

2016-01-01

In this research; aim was determining student achievement by comparing problem-based learning method with teacher-centered traditional method of teaching 10th grade chemistry lesson mixtures topic. Pretest-posttest control group research design is implemented. Research sample includes; two classes of (total of 48 students) an Anatolian High School…

Cooperative Learning in Distance Learning: A Mixed Methods Study

ERIC Educational Resources Information Center

Kupczynski, Lori; Mundy, Marie Anne; Goswami, Jaya; Meling, Vanessa

2012-01-01

Distance learning has facilitated innovative means to include Cooperative Learning (CL) in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student…

Academic Performance in Introductory Accounting: Do Learning Styles Matter?

ERIC Educational Resources Information Center

Tan, Lin Mei; Laswad, Fawzi

2015-01-01

This study examines the impact of learning styles on academic performance using major assessment methods (examinations and assignments including multiple-choice and constructed response questions (CRQs)) in an introductory accounting course. Students' learning styles were assessed using Kolb's Learning Style Inventory Version 3.1. The results…

Case-Based Reasoning in Mixed Paradigm Settings and with Learning

DTIC Science & Technology

1994-04-30

Learning Prototypical Cases OFF-BROADWAY, MCI and RMHC -* are three CBR-ML systems that learn case prototypes. We feel that methods that enable the...at Irvine Machine Learning Repository, including heart disease and breast cancer databases. OFF-BROADWAY, MCI and RMHC -* made the following notable

Preparing Brigade Combat Team Soldiers for Mission Readiness Through Research on Intangible Psychological Constructs and their Applications. Phase 2: Measurement and Learning Methods

DTIC Science & Technology

2014-02-01

10 Cognitive Learning Strategies, Metacognitive Strategies, Scaffolding, and Cognitive Tutoring...culture, technology , and instructional practices. 11 7. Motivational and emotional influences on learning - What and how much is learned is...of learning and intangible skills. These resulting set of theories includes: 12 • Cognitive learning strategies, metacognitive strategies

LEARNING AND CREATIVITY WITH SPECIAL EMPHASIS ON SCIENCE.

ERIC Educational Resources Information Center

SULLIVAN, JOHN J.; TAYLOR, CALVIN W.

PAPERS CONCERNING (1) LEARNING AND METHODS OF INVESTIGATION, AND (2) CREATIVITY AND PRODUCTIVE THINKING ARE INCLUDED IN THIS NATIONAL SCIENCE TEACHERS ASSOCIATION PUBLICATION. IN THE PAPER THAT DEALS WITH LEARNING, A DEFINITION OF LEARNING AND A DESCRIPTION OF BEHAVIORAL PSYCHOLOGY ARE FOLLOWED BY A DISCUSSION OF DISCRIMINATIVE STIMULI AND…

Collaborative Learning in Advanced Supply Systems: The KLASS Pilot Project.

ERIC Educational Resources Information Center

Rhodes, Ed; Carter, Ruth

2003-01-01

The Knowledge and Learning in Advanced Supply Systems (KLASS) project developed collaborative learning networks of suppliers in the British automotive and aerospace industries. Methods included face-to-face and distance learning, work toward National Vocational Qualifications, and diagnostic workshops for senior managers on improving quality,…

Journal of College Reading and Learning, Volume XIX, 1986.

ERIC Educational Resources Information Center

O'Hear, Michael F., Ed.; And Others

1986-01-01

Addressing issues on developmental education, instructional and learning methods, learning assistance and academic support, and reading and research, this issue of the Journal of College Reading and Learning includes the following articles: "Moving the Mountain to Mohammed: Study Skills Tutoring in the Residence Halls" (J. L. Rogers); "Memory…

Supporting School Leaders in Blended Learning with Blended Learning

ERIC Educational Resources Information Center

Acree, Lauren; Gibson, Theresa; Mangum, Nancy; Wolf, Mary Ann; Kellogg, Shaun; Branon, Suzanne

2017-01-01

This study provides a mixed-methods case-study design evaluation of the Leadership in Blended Learning (LBL) program. The LBL program uses blended approaches, including face-to-face and online, to prepare school leaders to implement blended learning initiatives in their schools. This evaluation found that the program designers effectively…

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…

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

PubMed

Xingquan Zhu

2011-12-01

Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

NASA Astrophysics Data System (ADS)

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-02-01

For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

PubMed Central

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-01-01

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. PMID:29515993

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Online eLearning for undergraduates in health professions: A systematic review of the impact on knowledge, skills, attitudes and satisfaction

PubMed Central

George, Pradeep Paul; Papachristou, Nikos; Belisario, José Marcano; Wang, Wei; Wark, Petra A; Cotic, Ziva; Rasmussen, Kristine; Sluiter, René; Riboli–Sasco, Eva; Car, Lorainne Tudor; Musulanov, Eve Marie; Molina, Joseph Antonio; Heng, Bee Hoon; Zhang, Yanfeng; Wheeler, Erica Lynette; Al Shorbaji, Najeeb; Majeed, Azeem; Car, Josip

2014-01-01

Background Health systems worldwide are facing shortages in health professional workforce. Several studies have demonstrated the direct correlation between the availability of health workers, coverage of health services, and population health outcomes. To address this shortage, online eLearning is increasingly being adopted in health professionals’ education. To inform policy–making, in online eLearning, we need to determine its effectiveness. Methods We performed a systematic review of the effectiveness of online eLearning through a comprehensive search of the major databases for randomised controlled trials that compared online eLearning to traditional learning or alternative learning methods. The search period was from January 2000 to August 2013. We included articles which primarily focused on students' knowledge, skills, satisfaction and attitudes toward eLearning and cost-effectiveness and adverse effects as secondary outcomes. Two reviewers independently extracted data from the included studies. Due to significant heterogeneity among the included studies, we presented our results as a narrative synthesis. Findings Fifty–nine studies, including 6750 students enrolled in medicine, dentistry, nursing, physical therapy and pharmacy studies, met the inclusion criteria. Twelve of the 50 studies testing knowledge gains found significantly higher gains in the online eLearning intervention groups compared to traditional learning, whereas 27 did not detect significant differences or found mixed results. Eleven studies did not test for differences. Six studies detected significantly higher skill gains in the online eLearning intervention groups, whilst 3 other studies testing skill gains did not detect differences between groups and 1 study showed mixed results. Twelve studies tested students' attitudes, of which 8 studies showed no differences in attitudes or preferences for online eLearning. Students' satisfaction was measured in 29 studies, 4 studies showed higher satisfaction for online eLearning and 20 studies showed no difference in satisfaction between online eLearning and traditional learning. Risk of bias was high for several of the included studies. Conclusion The current evidence base suggests that online eLearning is equivalent, possibly superior to traditional learning. These findings present a potential incentive for policy makers to cautiously encourage its adoption, while respecting the heterogeneity among the studies. PMID:24976965

Electronic Learning in Yugoslavia.

ERIC Educational Resources Information Center

Barker, Philip G.

1990-01-01

Describes a course taught at the University of Zagreb (Yugoslavia) on electronic learning methods based upon computer-assisted learning techniques. The course content is outlined, including lectures, workshops, videotapes, demonstration software, and courseware authoring; a multimedia teaching laboratory is described; and an evaluation of course…

Multi-model blending

DOEpatents

Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

2016-10-18

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Preferred Methods of Learning for Nursing Students in an On-Line Degree Program.

PubMed

Hampton, Debra; Pearce, Patricia F; Moser, Debra K

Investigators have demonstrated that on-line courses result in effective learning outcomes, but limited information has been published related to preferred teaching strategies. Delivery of on-line courses requires various teaching methods to facilitate interaction between students, content, and technology. The purposes of this study were to understand student teaching/learning preferences in on-line courses to include (a) differences in preferred teaching/learning methods for on-line nursing students across generations and (b) which teaching strategies students found to be most engaging and effective. Participants were recruited from 2 accredited, private school nursing programs (N=944) that admit students from across the United States and deliver courses on-line. Participants provided implied consent, and 217 (23%) students completed the on-line survey. Thirty-two percent of the students were from the Baby Boomer generation (1946-1964), 48% from Generation X (1965-1980), and 20% from the Millennial Generation (born after 1980). The preferred teaching/learning methods for students were videos or narrated PowerPoint presentations, followed by synchronous Adobe Connect educations sessions, assigned journal article reading, and e-mail dialog with the instructor. The top 2 methods identified by participants as the most energizing/engaging and most effective for learning were videos or narrated PowerPoint presentations and case studies. The teaching/learning method least preferred by participants and that was the least energizing/engaging was group collaborative projects with other students; the method that was the least effective for learning was wikis. Baby Boomers and Generation X participants had a significantly greater preference for discussion board (P<.0167) than millennial students. Millennial students also had a greater preference for simulation than did Baby Boomer and Generation X students and rated on-line games as significantly more energizing/engaging and more effective for learning (P<.0167) than did Baby Boomer and Generation X students. In conclusion, the results of this study demonstrate that there are distinct student preferences and generational differences in preferred teaching/learning methods for on-line students. Faculty need to incorporate various teaching methodologies within on-line courses to include both synchronous and asynchronous activities and interactive and passive methodologies. Copyright © 2016 Elsevier Inc. All rights reserved.

E-learning: controlling costs and increasing value.

PubMed

Walsh, Kieran

2015-04-01

E-learning now accounts for a substantial proportion of medical education provision. This progress has required significant investment and this investment has in turn come under increasing scrutiny so that the costs of e-learning may be controlled and its returns maximised. There are multiple methods by which the costs of e-learning can be controlled and its returns maximised. This short paper reviews some of those methods that are likely to be most effective and that are likely to save costs without compromising quality. Methods might include accessing free or low-cost resources from elsewhere; create short learning resources that will work on multiple devices; using open source platforms to host content; using in-house faculty to create content; sharing resources between institutions; and promoting resources to ensure high usage. Whatever methods are used to control costs or increase value, it is most important to evaluate the impact of these methods.

[Problem-based learning, a comparison in the acquisition of transversal competencies].

PubMed

González Pascual, Juan Luis; López Martin, Inmaculada; Toledo Gómez, David

2009-01-01

In the European Higher Education Area (EEES in Spanish reference), a change in the pedagogical model has occurred: from teaching centered on the figure of the professor to learning centered on students, from an integral perspective. This learning must bring together the full set of competencies included in the program requirements necessary to obtain a degree. The specific competencies characterize a profession and distinguish one from others. The transversal competencies surpass the limits of one particular discipline to be potentially developed in all; these are subdivided in three types: instrumental, interpersonal and systemic. The authors describe and compare the acquisition of transversal competencies connected to students' portfolios and Problem-based Learning as pedagogical methods from the perspective of second year nursing students at the European University in Madrid during the 2007-8 academic year To do so, the authors carried out a transversal descriptive study; data was collected by a purpose-made questionnaire the authors developed which they based on the transversal competencies of the Tuning Nursing Project. Variables included age, sex, pedagogical method, perception on acquisition of those 24 competencies by means of a Likert Scale. U de Mann-Whitney descriptive and analytical statistics. The authors conclude that the portfolio and Problem-based Learning are useful pedagogical methods for acquiring transversal competencies; these results coincide with those of other studies. Comparing both methods, the authors share the opinion that the Problem-based Learning method could stimulate the search for information better than the portfolio method.

The Journal of the Society for Accelerative Learning and Teaching, Volume 7.

ERIC Educational Resources Information Center

Journal of the Society for Accelerative Learning and Teaching, 1982

1982-01-01

The four 1982 numbers of the Journal of the Society for Accelerative Learning and Teaching (SALT) include articles on: a comparison of the Tomatis Method and Suggestopedia; the CLC system of accelerated learning; Suggestopedia in the English-as-a-second-language classroom; experiments with SALT techniques; accelerative learning techniques for…

Implementation of Project Based Learning in Mechatronic Lab Course at Bandung State Polytechnic

ERIC Educational Resources Information Center

Basjaruddin, Noor Cholis; Rakhman, Edi

2016-01-01

Mechatronics is a multidisciplinary that includes a combination of mechanics, electronics, control systems, and computer science. The main objective of mechatronics learning is to establish a comprehensive mindset in the development of mechatronic systems. Project Based Learning (PBL) is an appropriate method for use in the learning process of…

Generational diversity in associate degree nursing students: Teaching styles and preferences in Pennsylvania

NASA Astrophysics Data System (ADS)

Kitko, Jennifer V.

2011-12-01

Nursing educators face the challenge of meeting the needs of a multi-generational classroom. The reality of having members from the Veteran and Baby Boomer generations in a classroom with Generation X and Y students provides an immediate need for faculty to examine students' teaching method preferences as well as their own use of teaching methods. Most importantly, faculty must facilitate an effective multi-generational learning environment. Research has shown that the generation to which a person belongs is likely to affect the ways in which he/she learns (Hammill, 2005). Characterized by its own attitudes, behaviors, beliefs, and motivational needs, each generation also has distinct educational expectations. It is imperative, therefore, that nurse educators be aware of these differences and develop skills through which to communicate with the different generations, thereby reducing teaching/learning problems in the classroom. This is a quantitative, descriptive study that compared the teaching methods preferred by different generations of associate degree nursing students with the teaching methods that the instructors actually use. The research study included 289 participants; 244 nursing student participants and 45 nursing faculty participants from four nursing departments in colleges in Pennsylvania. Overall, the results of the study found many statistically significant findings. The results of the ANOVA test revealed eight statistically significant findings among Generation Y, Generation X and Baby boomers. The preferred teaching methods included: lecture, self-directed learning, web-based course with no class meetings, important for faculty to know my name, classroom structure, know why I am learning what I am learning, learning for the sake of learning and grade is all that matters. Lecture was found to be the most frequently used teaching method by faculty as well as the most preferred teaching methods by students. Overall, the support for a variety of teaching methods was also found in the analysis of the data.

Identification of Learning Mechanisms in a Wild Meerkat Population

PubMed Central

Hoppitt, Will; Samson, Jamie; Laland, Kevin N.; Thornton, Alex

2012-01-01

Vigorous debates as to the evolutionary origins of culture remain unresolved due to an absence of methods for identifying learning mechanisms in natural populations. While laboratory experiments on captive animals have revealed evidence for a number of mechanisms, these may not necessarily reflect the processes typically operating in nature. We developed a novel method that allows social and asocial learning mechanisms to be determined in animal groups from the patterns of interaction with, and solving of, a task. We deployed it to analyse learning in groups of wild meerkats (Suricata suricatta) presented with a novel foraging apparatus. We identify nine separate learning processes underlying the meerkats’ foraging behaviour, in each case precisely quantifying their strength and duration, including local enhancement, emulation, and a hitherto unrecognized form of social learning, which we term ‘observational perseverance’. Our analysis suggests a key factor underlying the stability of behavioural traditions is a high ratio of specific to generalized social learning effects. The approach has widespread potential as an ecologically valid tool to investigate learning mechanisms in natural groups of animals, including humans. PMID:22905113

Comparison of teaching about breast cancer via mobile or traditional learning methods in gynecology residents.

PubMed

Alipour, Sadaf; Moini, Ashraf; Jafari-Adli, Shahrzad; Gharaie, Nooshin; Mansouri, Khorshid

2012-01-01

Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. The mobile learning method had a significantly better effect on learning and created more interest in the subject. Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.

Correlation of the summary method with learning styles.

PubMed

Sarikcioglu, Levent; Senol, Yesim; Yildirim, Fatos B; Hizay, Arzu

2011-09-01

The summary is the last part of the lesson but one of the most important. We aimed to study the relationship between the preference of the summary method (video demonstration, question-answer, or brief review of slides) and learning styles. A total of 131 students were included in the present study. An inventory was prepared to understand the students' learning styles, and a satisfaction questionnaire was provided to determine the summary method selection. The questionnaire and inventory were collected and analyzed. A comparison of the data revealed that the summary method with video demonstration received the highest score among all the methods tested. Additionally, there were no significant differences between learning styles and summary method with video demonstration. We suggest that such a summary method should be incorporated into neuroanatomy lessons. Since anatomy has a large amount of visual material, we think that it is ideally suited for this summary method.

Learn Not To Burn.

ERIC Educational Resources Information Center

English, Nancy; Hendricks, Charlotte M.

1997-01-01

Describes the "Learn Not to Burn Preschool Program," a low-cost fire safety awareness and burn prevention curriculum for young children. The program promotes eight burn prevention methods--including practicing an escape plan--using developmentally appropriate learning objectives to increase children's fire safety knowledge, skill, and…

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

The Journal of Suggestive-Accelerative Learning and Teaching, Volume 5, Number 2.

ERIC Educational Resources Information Center

Journal of Suggestive-Accelerative Learning and Teaching, 1980

1980-01-01

A collection of articles concerning suggestive-accelerative learning and teaching (SALT) methods includes: "Suggestive Teaching Methods in the Soviet Union" (Eva Szalontai); "SALT Applied to Remedial Reading: A Critical Review" (Allyn Prichard and Jean Taylor); "The Waldorf Schools: An Artistic Approach to Education"…

Distance Education and Open Learning--Implications for Professional Development and Retraining.

ERIC Educational Resources Information Center

Scriven, Bruce

1991-01-01

Discusses the increasing need for professional development and retraining in Australia, especially for inservice teacher education, and describes new methods that may be more effective than traditional methods. Highlights include open learning; the modularization of courses and programs; the adaptation of instructional materials; and distance…

Suggestology as an Effective Language Learning Method.

ERIC Educational Resources Information Center

MaCoy, Katherine W.

The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Assessment and Learning of Mathematics.

ERIC Educational Resources Information Center

Leder, Gilah C., Ed.

This book addresses the link between student learning of mathematics, the teaching method adopted in the mathematics classroom, and the assessment procedures used to determine and measure student knowledge. Fifteen chapters address issues that include a review of different models of mathematics learning and assessment practices, three contrasting…

Cooperative Learning as a Tool To Teach Vertebrate Anatomy.

ERIC Educational Resources Information Center

Koprowski, John L.; Perigo, Nan

2000-01-01

Describes a method for teaching biology that includes more investigative exercises that foster an environment for cooperative learning in introductory laboratories that focus on vertebrates. Fosters collaborative learning by facilitating interaction between students as they become experts on their representative vertebrate structures. (SAH)

Learning Achievement Improvement Efforts Course Learn and Learning Using the Jigsaw Method and Card Media in STKIP PGRI Ngawi 2014/2015 Academic Year

ERIC Educational Resources Information Center

Haryono

2015-01-01

Subject Teaching and Learning is a basic educational courses that must be taken by all student teachers. Class Action Research aims to improve student achievement Teaching and Learning course by applying Jigsaw and media cards. Research procedures using Classroom Action Research (CAR) with multiple cycles. Each cycle includes four phases:…

How Children Learn Mathematics, Teaching Implications of Piaget's Research.

ERIC Educational Resources Information Center

Copeland, Richard W.

Included are the standard topics presented in the undergraduate and/or graduate course on methods of teaching mathematics in elementary education. Chapter 1 describes the historical development of learning theories, including Piaget's. Chapter 2 contains a biographical sketch of Piaget and an explanation of his theory of cognitive development.…

Can Learning Style Predict Student Satisfaction with Different Instruction Methods and Academic Achievement in Medical Education?

ERIC Educational Resources Information Center

Gurpinar, Erol; Alimoglu, Mustafa Kemal; Mamakli, Sumer; Aktekin, Mehmet

2010-01-01

The curriculum of our medical school has a hybrid structure including both traditional training (lectures) and problem-based learning (PBL) applications. The purpose of this study was to determine the learning styles of our medical students and investigate the relation of learning styles with each of satisfaction with different instruction methods…

www.teld.net: Online Courseware Engine for Teaching by Examples and Learning by Doing.

ERIC Educational Resources Information Center

Huang, G. Q.; Shen, B.; Mak, K. L.

2001-01-01

Describes TELD (Teaching by Examples and Learning by Doing), a Web-based online courseware engine for higher education. Topics include problem-based learning; project-based learning; case methods; TELD as a Web server; course materials; TELD as a search engine; and TELD as an online virtual classroom for electronic delivery of electronic…

Newly qualified teachers' visions of science learning and teaching

NASA Astrophysics Data System (ADS)

Roberts, Deborah L.

2011-12-01

This study investigated newly qualified teachers' visions of science learning and teaching. The study also documented their preparation in an elementary science methods course. The research questions were: What educational and professional experiences influenced the instructor's visions of science learning and teaching? What visions of science learning and teaching were promoted in the participants' science methods course? What visions of science learning and teaching did these newly qualified teachers bring with them as they graduated from their teacher preparation program? How did these visions compare with those advocated by reform documents? Data sources included participants' assignments, weekly reflections, and multi-media portfolio finals. Semi-structured interviews provided the emic voice of participants, after graduation but before they had begun to teach. These data were interpreted via a combination of qualitative methodologies. Vignettes described class activities. Assertions supported by excerpts from participants' writings emerged from repeated review of their assignments. A case study of a typical participant characterized weekly reflections and final multi-media portfolio. Four strands of science proficiency articulated in a national reform document provided a framework for interpreting activities, assignments, and interview responses. Prior experiences that influenced design of the methods course included an inquiry-based undergraduate physics course, participation in a reform-based teacher preparation program, undergraduate and graduate inquiry-based science teaching methods courses, participation in a teacher research group, continued connection to the university as a beginning teacher, teaching in diverse Title 1 schools, service as the county and state elementary science specialist, participation in the Carnegie Academy for the Scholarship of Teaching and Learning, service on a National Research Council committee, and experience teaching a science methods course. The methods course studied here emphasized reform-based practices, science as inquiry, culturally responsive teaching, scientific discourse, and integration of science with technology and other disciplines. Participants' writings and interview responses articulated visions of science learning and teaching that included aspects of reform-based practices. Some participants intentionally incorporated and implemented reform-based strategies in field placements during the methods course and student teaching. The strands of scientific proficiency were evident in activities, assignments and participants' interviews in varying degrees.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

PubMed Central

Burlina, Philippe; Billings, Seth; Joshi, Neil

2017-01-01

Objective To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Methods Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and “engineered” features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. Results The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). Conclusions This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification. PMID:28854220

Learning Situations in Nursing Education: A Concept Analysis.

PubMed

Shahsavari, Hooman; Zare, Zahra; Parsa-Yekta, Zohreh; Griffiths, Pauline; Vaismoradi, Mojtaba

2018-02-01

The nursing student requires opportunities to learn within authentic contexts so as to enable safe and competent practice. One strategy to facilitate such learning is the creation of learning situations. A lack of studies on the learning situation in nursing and other health care fields has resulted in insufficient knowledge of the characteristics of the learning situation, its antecedents, and consequences. Nurse educators need to have comprehensive and practical knowledge of the definition and characteristics of the learning situation so as to enable their students to achieve enhanced learning outcomes. The aim of this study was to clarify the concept of the learning situation as it relates to the education of nurses and improve understanding of its characteristics, antecedents, and consequences. The Bonis method of concept analysis, as derived from the Rodgers' evolutionary method, provided the framework for analysis. Data collection and analysis were undertaken in two phases: "interdisciplinary" and "intra-disciplinary." The data source was a search of the literature, encompassing nursing and allied health care professions, published from 1975 to 2016. No agreement on the conceptual phenomenon was discovered in the international literature. The concept of a learning situation was used generally in two ways and thus classified into the themes of: "formal/informal learning situation" and "biologic/nonbiologic learning situation." Antecedents to the creation of a learning situation included personal and environmental factors. The characteristics of a learning situation were described in terms of being complex, dynamic, and offering potential and effective learning opportunities. Consequences of the learning situation included enhancement of the students' learning, professionalization, and socialization into the professional role. The nurse educator, when considering the application of the concept of a learning situation in their educational planning, must acknowledge that the application of this concept will include the student's clinical learning experiences. More studies are required to determine factors influencing the creation of a successful learning situation from the perspectives of nurse educators and nursing students, clinical nurses and patients.

Understanding Resident Learning Preferences Within an Internal Medicine Noon Conference Lecture Series: A Qualitative Study

PubMed Central

Sawatsky, Adam P.; Zickmund, Susan L.; Berlacher, Kathryn; Lesky, Dan; Granieri, Rosanne

2014-01-01

Background The lecture remains the most common approach for didactic offerings in residency programs despite conflicting evidence about the effectiveness of this format. Objective The purpose of this study was to explore the perspectives of internal medicine residents toward conferences held in the lecture format. Methods The investigators invited internal medicine residents (N  =  144) to participate in focus groups discussing their perspectives about noon conference lectures. The investigators used a semistructured guide to ask about motivations for attendance and effectiveness of noon conferences, transcribed the recordings, coded the discussions, and analyzed the results. Results Seven focus groups with a total of 41 residents were held. This identified 4 major domains: (1) motivations for attendance; (2) appropriate content; (3) effective teaching methods; and (4) perspectives on active participation. Residents' motivations were categorized into external factors, including desire for a break and balance to their workload, and intrinsic attributes, including the learning opportunity, topic, and speaker. Appropriate content was described as clinically relevant, practical, and presenting a balance of evidence. Identified effective teaching methods included shorter teaching sessions focused on high-yield learning points structured around cases and questions. While active participation increases residents' perceived level of stress, the benefits of this format include increased attention and learning. Conclusions This study furthers our knowledge of the learning preferences of internal medicine residents within the changing environment of residency education and can be used in conjunction with principles of adult learning to reform how we deliver core medical knowledge. PMID:24701307

Students' Perceptions of Blended Learning and its Effectiveness As a Part of Second Year Dental Curriculum

NASA Astrophysics Data System (ADS)

Varthis, Spyridon

The field of dental medical education is one of the most rapidly evolving fields in education. Newer teaching methods are being evaluated and incorporated in dental institutions. One of the promising new methods is the blended learning approach that may involve a "flipped" instructional sequencing, where online instruction precedes the group meeting, allowing for more sophisticated learning through discussion and critical thinking. The author conducted a mixed method, experimental study that focused on second year dental students' perceptions of blended learning and its effectiveness. A sample size of 40 dental students in their second year from a Northeastern Regional Dental School were invited to participate in this study to evaluate a blended learning approach in comparison to a more traditional lecture format. Students who participated in the study, participated in group problem-solving, responded to Likert-type surveys, completed content exams, and were interviewed individually. Based on Likert survey data and interview responses, the participants in the blended learning treatment reported very positive opinions including positive perceptions of the organization, support of meaningful learning and potential merits for use in dental education. There also was evidence that the blended learning group achieved at least as well as the traditional lecture group, and excelled on certain content test items. The results of this study support the conclusion that blended instruction promotes active, in-depth and self-regulated learning. During blended learning, students set standards or goals regarding their learning, evaluate their progress toward these goals, and then adapt and regulate their cognition, motivation, and behavior in order to accomplish their goals. Overall, the results of this research on blended learning, including the use of problem-based learning in group discussions, supports the merits of incorporating blended earning in dental education curricula.

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.

The Effects of Mobile-Computer-Supported Collaborative Learning: Meta-Analysis and Critical Synthesis.

PubMed

Sung, Yao-Ting; Yang, Je-Ming; Lee, Han-Yueh

2017-08-01

One of the trends in collaborative learning is using mobile devices for supporting the process and products of collaboration, which has been forming the field of mobile-computer-supported collaborative learning (mCSCL). Although mobile devices have become valuable collaborative learning tools, evaluative evidence for their substantial contributions to collaborative learning is still scarce. The present meta-analysis, which included 48 peer-reviewed journal articles and doctoral dissertations written over a 16-year period (2000-2015) involving 5,294 participants, revealed that mCSCL has produced meaningful improvements for collaborative learning, with an overall mean effect size of 0.516. Moderator variables, such as domain subject, group size, teaching method, intervention duration, and reward method were related to different effect sizes. The results provided implications for future research and practice, such as suggestions on how to appropriately use the functionalities of mobile devices, how to best leverage mCSCL through effective group learning mechanisms, and what outcome variables should be included in future studies to fully elucidate the process and products of mCSCL.

The Effects of Mobile-Computer-Supported Collaborative Learning: Meta-Analysis and Critical Synthesis

PubMed Central

Sung, Yao-Ting; Yang, Je-Ming; Lee, Han-Yueh

2017-01-01

One of the trends in collaborative learning is using mobile devices for supporting the process and products of collaboration, which has been forming the field of mobile-computer-supported collaborative learning (mCSCL). Although mobile devices have become valuable collaborative learning tools, evaluative evidence for their substantial contributions to collaborative learning is still scarce. The present meta-analysis, which included 48 peer-reviewed journal articles and doctoral dissertations written over a 16-year period (2000–2015) involving 5,294 participants, revealed that mCSCL has produced meaningful improvements for collaborative learning, with an overall mean effect size of 0.516. Moderator variables, such as domain subject, group size, teaching method, intervention duration, and reward method were related to different effect sizes. The results provided implications for future research and practice, such as suggestions on how to appropriately use the functionalities of mobile devices, how to best leverage mCSCL through effective group learning mechanisms, and what outcome variables should be included in future studies to fully elucidate the process and products of mCSCL. PMID:28989193

Education and learning: what's on the horizon?

PubMed

Pilcher, Jobeth

2014-01-01

Numerous organizations have called for significant changes in education for health care professionals. The call has included the need to incorporate evidence-based as well as innovative strategies. Previous articles in this column have focused primarily on evidence-based teaching strategies, including concept mapping, brain-based learning strategies, methods of competency assessment, and so forth. This article shifts the focus to new ways of thinking about knowledge and education. The article will also introduce evolving, innovative, less commonly used learning strategies and provide a peek into the future of learning.

Problem-Based Learning in Accounting

ERIC Educational Resources Information Center

Dockter, DuWayne L.

2012-01-01

Seasoned educators use an assortment of student-centered methods and tools to enhance their student's learning environment. In respects to methodologies used in accounting, educators have utilized and created new forms of problem-based learning exercises, including case studies, simulations, and other projects, to help students become more active…

Cautions: Implementing Interpersonal Interaction in Workplace E-Learning

ERIC Educational Resources Information Center

Githens, Rod P.

2006-01-01

E-learning programs in workplaces have been slow to incorporate social and collaborative methods. Although these programs provide flexibility and cost savings, poor learning outcomes and low completion rates have caused some organizations to transition to approaches that include interpersonal interaction. In reviewing studies of e-learning…

Improving Clinical Practices for Children with Language and Learning Disorders

ERIC Educational Resources Information Center

Kamhi, Alan G.

2014-01-01

Purpose: This lead article of the Clinical Forum addresses some of the gaps that exist between clinical practice and current knowledge about instructional factors that influence learning and language development. Method: Topics reviewed and discussed include principles of learning, generalization, treatment intensity, processing interventions,…

Promoting Technology-Assisted Active Learning in Computer Science Education

ERIC Educational Resources Information Center

Gao, Jinzhu; Hargis, Jace

2010-01-01

This paper describes specific active learning strategies for teaching computer science, integrating both instructional technologies and non-technology-based strategies shown to be effective in the literature. The theoretical learning components addressed include an intentional method to help students build metacognitive abilities, as well as…

Narratives of Experiential Learning: Students' Engagement in a Physical Activity-Based Service-Learning Course

ERIC Educational Resources Information Center

Whitley, Meredith A.; Walsh, David; Hayden, Laura; Gould, Daniel

2017-01-01

Purpose: Three undergraduate students' experiences in a physical activity-based service learning course are chronicled using narrative inquiry. Method: Data collection included demographics questionnaires, pre- and postservice interviews, reflection journals, postservice written reflections, and participant observations. The data were analyzed…

Coupled dictionary learning for joint MR image restoration and segmentation

NASA Astrophysics Data System (ADS)

Yang, Xuesong; Fan, Yong

2018-03-01

To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

A Research Context for Diagnostic and Prescriptive Mathematics.

ERIC Educational Resources Information Center

Engelhardt, Jon; Uprichard, A. Edward

1998-01-01

Argues that a position should be taken on which future research initiatives on learning and instruction will be most worthy if grounded in general systems theory and multiple research methods are employed. Presents an application of general systems theory to research on learning and instruction, including a system of research methods and…

Experimental Methodology in English Teaching and Learning: Method Features, Validity Issues, and Embedded Experimental Design

ERIC Educational Resources Information Center

Lee, Jang Ho

2012-01-01

Experimental methods have played a significant role in the growth of English teaching and learning studies. The paper presented here outlines basic features of experimental design, including the manipulation of independent variables, the role and practicality of randomised controlled trials (RCTs) in educational research, and alternative methods…

Manifold learning of brain MRIs by deep learning.

PubMed

Brosch, Tom; Tam, Roger

2013-01-01

Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with pplications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are (1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 x 128 x 128 practical, and (2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.

Didactical Design Material Units of Distance and Speed to Developed Mathematical Connection in Elementary School

NASA Astrophysics Data System (ADS)

Jannah, R. R.; Apriliya, S.; Karlimah

2017-03-01

This study aims to develop alternative instructional design based of barriers learning which identified by developing mathematical connection capabilities to the material unit of distance and speed. The research was conducted in the fifth grade elementary school Instructional design is complemented with a hypothetical learning trajectory in the form of a pedagogical didactic anticipation. The method used is descriptive method with qualitative approach. Techniques data collection used were observation, interviews, and documentation. The instrument used the researchers themselves are equipped with an instrument written test. The data were analyzed qualitatively to determine the student learning obstacles, then arrange hypothetical learning trajectory and pedagogical didactic anticipation. Learning obstacle are identified, it is learning obstacle related the connections between mathematical topics, learning obstacle related with other disciplines, and learning obstacle related with everyday life. The results of this research are improvement and development of didactic design in mathematics which has activities mathematical connection to the material unit of distance and speed in elementary school. The learning activities are carried out is using varied methods include method lectures, demonstrations, practice and exercise, as well as using the modified instructional media.

Students' perspectives of undergraduate research methods education at three public medical schools in Uganda.

PubMed

Munabi, Ian Guyton; Buwembo, William; Joseph, Ruberwa; Peter, Kawungezi; Bajunirwe, Francis; Mwaka, Erisa Sabakaki

2016-01-01

In this study we used a model of adult learning to explore undergraduate students' views on how to improve the teaching of research methods and biostatistics. This was a secondary analysis of survey data of 600 undergraduate students from three medical schools in Uganda. The analysis looked at student's responses to an open ended section of a questionnaire on their views on undergraduate teaching of research methods and biostatistics. Qualitative phenomenological data analysis was done with a bias towards principles of adult learning. Students appreciated the importance of learning research methods and biostatistics as a way of understanding research problems; appropriately interpreting statistical concepts during their training and post-qualification practice; and translating the knowledge acquired. Stressful teaching environment and inadequate educational resource materials were identified as impediments to effective learning. Suggestions for improved learning included: early and continuous exposure to the course; more active and practical approach to teaching; and a need for mentorship. The current methods of teaching research methods and biostatistics leave most of the students in the dissonance phase of learning resulting in none or poor student engagement that results in a failure to comprehend and/or appreciate the principles governing the use of different research methods.

Fostering the development of effective person-centered healthcare communication skills: an interprofessional shared learning model.

PubMed

Cavanaugh, James T; Konrad, Shelley Cohen

2012-01-01

To describe the implementation of an interprofessional shared learning model designed to promote the development of person-centered healthcare communication skills. Master of social work (MSW) and doctor of physical therapy (DPT) degree students. The model used evidence-based principles of effective healthcare communication and shared learning methods; it was aligned with student learning outcomes contained in MSW and DPT curricula. Students engaged in 3 learning sessions over 2 days. Sessions involved interactive reflective learning, simulated role-modeling with peer assessment, and context-specific practice of communication skills. The perspective of patients/clients was included in each learning activity. Activities were evaluated through narrative feedback. Students valued opportunities to learn directly from each other and from healthcare consumers. Important insights and directions for future interprofessional learning experiences were gleaned from model implementation. The interprofessional shared learning model shows promise as an effective method for developing person-centered communication skills.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

NASA Astrophysics Data System (ADS)

Hindriks, Koen V.; Tykhonov, Dmytro

In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.

CD-ROM Integration Peaks Student Interest in Inquiry.

ERIC Educational Resources Information Center

O'Bannon, Blanche

1997-01-01

Discussion of learning processes examines past educational practices and considers how CD-ROM technology can impact teaching and learning. A lesson plan for elementary school science that uses a CD-ROM encyclopedia is presented that includes instructional goals, performance objectives, teaching and learning activities, and assessment methods.…

Factors That Influence Organization Learning Sustainability in Non-Profit Organizations

ERIC Educational Resources Information Center

Prugsamatz, Raphaella

2010-01-01

Purpose: The purpose of this paper is to broaden previous work on organizational learning and the factors that influence learning in organizational settings. Design/methodology/approach: Qualitative and quantitative research methods that included in-depth interviews and questionnaire distribution were used. Data gathered were analyzed using…

Strategies for Teaching Students with Learning and Behavior Problems. Fifth Edition.

ERIC Educational Resources Information Center

Bos, Candace S.; Vaughn, Sharon

This book provides information about general approaches to learning and teaching, offering descriptions of methods and procedures and focusing on classroom and behavior management, consultation, and working with parents and professionals. The 12 chapters include: (1) "The Teaching-Learning Process" (e.g., characteristics of students with…

The Journal of the Society for Accelerative Learning and Teaching, Volume 10, 1985.

ERIC Educational Resources Information Center

Schuster, Don H., Ed.

1985-01-01

Four numbers of the journal contain a variety of articles on methods and programs of accelerative learning and teaching, including: "Music Therapy and Education"; "The Effects of Background Music on Vocabulary Learning"; "Terminating the Tyranny of Time from 21st Century Education"; "An Example of Limbic…

Using ICT-Supported Narratives in Teaching Science and Their Effects on Middle School Students

ERIC Educational Resources Information Center

Ekici, Fatma Taskin; Pekmezci, Sultan

2015-01-01

Effective and sustainable science education is enriched by the use of visuals, auditory, and tactile experiences. In order to provide effective learning, instruction needs to include multimodal approaches. Integrating ICT supported narrations into learning environments may provide effective and sustainable learning methods. Investigated in this…

Face-to-Face or Distance Training? Two Different Approaches To Motivate SMEs To Learn--An Update.

ERIC Educational Resources Information Center

Allan, John; O'Dwyer, Michele; Ryan, Eamon; Lawless, Naomi

2001-01-01

Two projects attempted to assess and meet small and medium-sized enterprises' training needs. Britain's Learning support for Small Businesses delivery methods included paper, CD-ROM, and the Internet. The University of Limerick, Ireland, offered face-to-face learning for microenterprises. (SK)

Teaching to Strengths: Engaging Young Boys in Learning

ERIC Educational Resources Information Center

Johnson, Cynthia; Gooliaff, Shauna

2013-01-01

Traditional teaching methods often fail to engage male students in learning. The purpose of this research was to increase student engagement in the story writing process and increase self-confidence in boys at risk. A qualitative approach included student surveys as well as teacher journaling and portfolios (including e-portfolios). The student…

Implementing Curriculum-Based Learning Portfolio: A Case Study in Taiwan

ERIC Educational Resources Information Center

Chen, Shu-Chin Susan; Cheng, Yu-Pay

2011-01-01

The main purpose of this descriptive research is to examine and document the development of a curriculum-based learning portfolio model for children in a preschool for three-six-year-olds in Taiwan. Data collection methods adopted include classroom observation, in-depth interviews, questionnaires and documentation. Participants include a preschool…

Using Formal Game Design Methods to Embed Learning Outcomes into Game Mechanics and Avoid Emergent Behaviour

ERIC Educational Resources Information Center

Grey, Simon; Grey, David; Gordon, Neil; Purdy, Jon

2017-01-01

This paper offers an approach to designing game-based learning experiences inspired by the Mechanics-Dynamics-Aesthetics (MDA) model (Hunicke et al., 2004) and the elemental tetrad model (Schell, 2008) for game design. A case for game based learning as an active and social learning experience is presented including arguments from both teachers and…

Effects of Cooperative Learning on Learning Achievement and Group Working Behavior of Junior Students in Modern French Literature Course

ERIC Educational Resources Information Center

Orprayoon, Soudaya

2014-01-01

This study reported on the results of a quasi-experimental research to explore the effectiveness of using a cooperative learning method on students' academic achievement, their group working behavior and their perception and opinions towards cooperative learning in a Modern French Literature course. The sample included twelve junior students…

The Effectiveness of the Chemistry Problem Based Learning (PBL) via FB among Pre-University Students

ERIC Educational Resources Information Center

Sunar, Mohd Shahir Mohamed; Shaari, Ahmad Jelani

2017-01-01

The impact of social media, such as Facebook in various fields including education is undeniable. The main objective of this study is to examine the effect of the interaction between students' learning styles and learning approaches on their achievements in the chemistry subject using the Problem-Based Learning (PBL) method through Facebook. The…

Life Span as the Measure of Performance and Learning in a Business Gaming Simulation

ERIC Educational Resources Information Center

Thavikulwat, Precha

2012-01-01

This study applies the learning curve method of measuring learning to participants of a computer-assisted business gaming simulation that includes a multiple-life-cycle feature. The study involved 249 participants. It verified the workability of the feature and estimated the participants' rate of learning at 17.4% for every doubling of experience.…

The Effectiveness of Project-Based Learning on Pupils with Learning Difficulties Regarding Academic Performance, Group Work and Motivation

ERIC Educational Resources Information Center

Filippatou, Diamanto; Kaldi, Stavroula

2010-01-01

This study focuses upon the effectiveness of project-based learning on primary school pupils with learning difficulties regarding their academic performance and attitudes towards self efficacy, task value, group work and teaching methods applied. The present study is a part of a larger one that included six Greek fourth-grade primary school…

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

PubMed

Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

2017-01-01

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

TECHNIQUES FOR TEACHING CONSERVATION EDUCATION.

ERIC Educational Resources Information Center

BROWN, ROBERT E.; MOUSER, G.W.

CONSERVATION PRINCIPLES, FIELD METHODS AND TECHNIQUES, AND SPECIFIC FIELD LEARNING ACTIVITIES ARE INCLUDED IN THIS REFERENCE VOLUME FOR TEACHERS. CONSERVATION PRINCIPLES INCLUDE STATEMENTS PERTAINING TO (1) SOIL, (2) WATER, (3) FOREST, AND (4) WILDLIFE. FIELD METHODS AND TECHNIQUES INCLUDE (1) PREPARING FOR A FIELD TRIP, (2) GETTING STUDENT…

Sherlock Holmes, Master Problem Solver.

ERIC Educational Resources Information Center

Ballew, Hunter

1994-01-01

Shows the connections between Sherlock Holmes's investigative methods and mathematical problem solving, including observations, characteristics of the problem solver, importance of data, questioning the obvious, learning from experience, learning from errors, and indirect proof. (MKR)

Model-based reinforcement learning with dimension reduction.

PubMed

Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi

2016-12-01

The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of Electronic Learning Versus Lecture-based Learning in Improving Emergency Medicine Residents' Knowledge About Mild Induced Hypothermia After Cardiac Arrest.

PubMed

Soleimanpour, Maryam; Rahmani, Farzad; Naghizadeh Golzari, Mehrad; Ala, Alireza; Morteza Bagi, Hamid Reza; Mehdizadeh Esfanjani, Robab; Soleimanpour, Hassan

2017-08-01

The process of medical education depends on several issues such as training materials, students, professors, educational fields, and the applied technologies. The current study aimed at comparing the impacts of e-learning and lecture-based learning of mild induced hypothermia (MIH) after cardiac arrest on the increase of knowledge among emergency medicine residents. In a pre- and post-intervention study, MIH after cardiac arrest was taught to 44 emergency medicine residents. Residents were randomly divided into 2 groups. The first group included 21 participants (lecture-based learning) and the second had 23 participants (e-learning). A 19-item questionnaire with approved validity and reliability was employed as the pretest and posttest. Then, data were analyzed with SPSS software version 17.0. There was no statistically significant difference in terms of the learning method between the test scores of the 2 groups (P = 0.977). E-learning and lecture-based learning methods was effective in augmentation of residents of emergency medicine knowledge about MIH after cardiac arrest; nevertheless, there was no significant difference between these mentioned methods.

Integrated Method of Teaching in Web Quest Activity and Its Impact on Undergraduate Students’ Cognition and Learning Behaviors: A Future Trend in Medical Education

PubMed Central

Jahromi, Zohreh Badiyepeymaie; Mosalanejad, Leili

2015-01-01

Introduction: Web Quest is one of the new ways of teaching and learning that is based on research, and includes the principles of learning and cognitive activities, such as collaborative learning, social and cognitive learning, and active learning, and increases motivation. The aim of this study is to evaluate the Web Quest influence on students’ learning behaviors. Materials and Methods: In this quasi-experimental study, which was performed on undergraduates taking a psychiatric course at Jahrom University of Medical Sciences, simple sampling was used to select the cases to be studied; the students entered the study through census and were trained according toWeb Quest methodology. The procedure was to present the course as a case study and team work. Each topic included discussing concepts and then patient’s treatment and the communicative principles for two weeks. Active participation of the students in response to the scenario and introduced problem was equal to preparing scientific videos about the disease and collecting the latest medical treatment for the disease from the Internet. Three questionnaires, including the self-directed learning Questionnaire, teamwork evaluation Questionnaire (value of team), and Buffard self-regulated Questionnaire, were the data gathering tools. Results: The results showed that the average of self-regulated learning and self-directed learning (SDL) increased after the educational intervention. However, the increase was not significant. On the other hand, problem solving (P=0.001) and the value of teamwork (P=0.002), apart from increasing the average, had significant statistical values. Conclusions: In view of Web Quest’s positive impacts on students’ learning behaviors, problem solving and teamwork, the effective use of active learning and teaching practices and use of technology in medical education are recommended. PMID:25946931

Use of a problem-based learning teaching model for undergraduate medical and nursing education: a systematic review and meta-analysis

PubMed Central

Sayyah, Mehdi; Shirbandi, Kiarash; Saki-Malehi, Amal; Rahim, Fakher

2017-01-01

Objectives The aim of this systematic review and meta-analysis was to evaluate the problem-based learning (PBL) method as an alternative to conventional educational methods in Iranian undergraduate medical courses. Materials and methods We systematically searched international datasets banks, including PubMed, Scopus, and Embase, and internal resources of banks, including MagirIran, IranMedex, IranDoc, and Scientific Information Database (SID), using appropriate search terms, such as “PBL”, “problem-based learning”, “based on problems”, “active learning”, and“ learner centered”, to identify PBL studies, and these were combined with other key terms such as “medical”, “undergraduate”, “Iranian”, “Islamic Republic of Iran”, “I.R. of Iran”, and “Iran”. The search included the period from 1980 to 2016 with no language limits. Results Overall, a total of 1,057 relevant studies were initially found, of which 21 studies were included in the systematic review and meta-analysis. Of the 21 studies, 12 (57.14%) had a high methodological quality. Considering the pooled effect size data, there was a significant difference in the scores (standardized mean difference [SMD]=0.80, 95% CI [0.52, 1.08], P<0.000) in favor of PBL, compared with the lecture-based method. Subgroup analysis revealed that using PBL alone is more favorable compared to using a mixed model with other learning methods such as lecture-based learning (LBL). Conclusion The results of this systematic review showed that using PBL may have a positive effect on the academic achievement of undergraduate medical courses. The results suggest that teachers and medical education decision makers give more attention on using this method for effective and proper training. PMID:29042827

Correlation of the Summary Method with Learning Styles

ERIC Educational Resources Information Center

Sarikcioglu, Levent; Senol, Yesim; Yildirim, Fatos B.; Hizay, Arzu

2011-01-01

The summary is the last part of the lesson but one of the most important. We aimed to study the relationship between the preference of the summary method (video demonstration, question-answer, or brief review of slides) and learning styles. A total of 131 students were included in the present study. An inventory was prepared to understand the…

Senate Concurrent Resolution 83: Screening for Learning Disabilities. A Report to the 70th Legislature.

ERIC Educational Resources Information Center

Texas Education Agency, Austin.

In response to Senate Concurrent Resolution 83, the Texas Education Agency studied methods for screening all students upon entry to school for significant developmental lags that could lead to learning disabilities. The resulting report includes: (1) identification of screening techniques; (2) methods currently in use and validated for treatment…

"Total Physical Response": Un apprentissage non conventionnel ("Total Physical Response": An Unconventional Way to Learn).

ERIC Educational Resources Information Center

Lafayette, R. C.

1991-01-01

A discussion of the Total Physical Response method of second language instruction places the concept within the context of other unconventional language learning methods, reviews the rationale behind the approach, and outlines the classroom procedures used. A sampling of useful commands for classroom use is included. (19 references) (MSE)

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.

Unsupervised learning on scientific ocean drilling datasets from the South China Sea

NASA Astrophysics Data System (ADS)

Tse, Kevin C.; Chiu, Hon-Chim; Tsang, Man-Yin; Li, Yiliang; Lam, Edmund Y.

2018-06-01

Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar datasets, but using supervised learning methods which are designed to make predictions based on sample training data, unsupervised learning methods require no a priori information and focus only on the input data. In this study, popular unsupervised learning methods including K-means, self-organizing maps, hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters. The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales. K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales. This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

NASA Technical Reports Server (NTRS)

Jacklin, Stephen; Schumann, Johann; Gupta, Pramod; Richard, Michael; Guenther, Kurt; Soares, Fola

2005-01-01

Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance.

Development of an e-Learning Research Module Using Multimedia Instruction Approach.

PubMed

Kowitlawakul, Yanika; Chan, Moon Fai; Tan, Sharon Swee Lin; Soong, Alan Swee Kit; Chan, Sally Wai Chi

2017-03-01

Students nowadays feel more comfortable with new technologies, which increase their motivation and, as a result, improve their academic performance. In the last two decades, the use of information communication technology has been increasing in many disciplines in higher education. Online learning or e-learning has been used and integrated into the curriculum around the world. A team of nursing faculty and educational technology specialists have developed an e-learning research module and integrate it into the nursing curriculum. The aim was to assist master of nursing and postgraduate nursing students in developing their research knowledge before and throughout their enrollment in the research course. This e-learning module includes interactive multimedia such as audiovisual presentation, graphical theme, animation, case-based learning, and pretest and posttest for each topic area. The module focuses on three main topic areas: (1) basic research principles (for review), (2) quantitative method, and (3) qualitative method. The e-learning module is an innovative use of the information and communication technology to enhance student engagement and learning outcomes in a local context. This article discusses the development journey, piloting process, including the variety of evaluation perspectives, and the ways in which the results influenced the e-learning resource before its wider distribution.

Self-directed learning can outperform direct instruction in the course of a modern German medical curriculum - results of a mixed methods trial.

PubMed

Peine, Arne; Kabino, Klaus; Spreckelsen, Cord

2016-06-03

Modernised medical curricula in Germany (so called "reformed study programs") rely increasingly on alternative self-instructed learning forms such as e-learning and curriculum-guided self-study. However, there is a lack of evidence that these methods can outperform conventional teaching methods such as lectures and seminars. This study was conducted in order to compare extant traditional teaching methods with new instruction forms in terms of learning effect and student satisfaction. In a randomised trial, 244 students of medicine in their third academic year were assigned to one of four study branches representing self-instructed learning forms (e-learning and curriculum-based self-study) and instructed learning forms (lectures and seminars). All groups participated in their respective learning module with standardised materials and instructions. Learning effect was measured with pre-test and post-test multiple-choice questionnaires. Student satisfaction and learning style were examined via self-assessment. Of 244 initial participants, 223 completed the respective module and were included in the study. In the pre-test, the groups showed relatively homogenous scores. All students showed notable improvements compared with the pre-test results. Participants in the non-self-instructed learning groups reached scores of 14.71 (seminar) and 14.37 (lecture), while the groups of self-instructed learners reached higher scores with 17.23 (e-learning) and 15.81 (self-study). All groups improved significantly (p < .001) in the post-test regarding their self-assessment, led by the e-learning group, whose self-assessment improved by 2.36. The study shows that students in modern study curricula learn better through modern self-instructed methods than through conventional methods. These methods should be used more, as they also show good levels of student acceptance and higher scores in personal self-assessment of knowledge.

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Internet driver education study.

DOT National Transportation Integrated Search

2010-05-01

Incorporating technology through online courses, including drivers education (DE), is the wave of the future for : learning. While many states allow online DE as an accepted method of learning, Wisconsin currently only allows it on a : limited bas...

Recent developments in learning control and system identification for robots and structures

NASA Technical Reports Server (NTRS)

Phan, M.; Juang, J.-N.; Longman, R. W.

1990-01-01

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

28 CFR 36.309 - Examinations and courses.

Code of Federal Regulations, 2012 CFR

2012-07-01

... include taped examinations, interpreters or other effective methods of making orally delivered materials... qualified readers for individuals with visual impairments or learning disabilities, transcribers for... and services required by this section may include taped texts, interpreters or other effective methods...

28 CFR 36.309 - Examinations and courses.

Code of Federal Regulations, 2013 CFR

2013-07-01

... include taped examinations, interpreters or other effective methods of making orally delivered materials... qualified readers for individuals with visual impairments or learning disabilities, transcribers for... and services required by this section may include taped texts, interpreters or other effective methods...

28 CFR 36.309 - Examinations and courses.

Code of Federal Regulations, 2014 CFR

2014-07-01

... include taped examinations, interpreters or other effective methods of making orally delivered materials... qualified readers for individuals with visual impairments or learning disabilities, transcribers for... and services required by this section may include taped texts, interpreters or other effective methods...

Multitask visual learning using genetic programming.

PubMed

Jaśkowski, Wojciech; Krawiec, Krzysztof; Wieloch, Bartosz

2008-01-01

We propose a multitask learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (subfunctions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.

Improving student learning and views of physics in a large enrollment introductory physics class

NASA Astrophysics Data System (ADS)

Salehzadeh Einabad, Omid

Introductory physics courses often serve as gatekeepers for many scientific and engineering programs and, increasingly, colleges are relying on large, lecture formats for these courses. Many students, however, leave having learned very little physics and with poor views of the subject. In interactive engagement (IE), classroom activities encourage students to engage with each other and with physics concepts and to be actively involved in their own learning. These methods have been shown to be effective in introductory physics classes with small group recitations. This study examined student learning and views of physics in a large enrollment course that included IE methods with no separate, small-group recitations. In this study, a large, lecture-based course included activities that had students explaining their reasoning both verbally and in writing, revise their ideas about physics concepts, and apply their reasoning to various problems. The questions addressed were: (a) What do students learn about physics concepts and how does student learning in this course compare to that reported in the literature for students in a traditional course?, (b) Do students' views of physics change and how do students' views of physics compare to that reported in the literature for students in a traditional course?, and (c) Which of the instructional strategies contribute to student learning in this course? Data included: pre-post administration of the Force Concept Inventory (FCI), classroom exams during the term, pre-post administration of the Colorado Learning Attitudes About Science Survey (CLASS), and student work, interviews, and open-ended surveys. The average normalized gain (=0.32) on the FCI falls within the medium-gain range as reported in the physics education literature, even though the average pre-test score was very low (30%) and this was the instructor's first implementation of IE methods. Students' views of physics remained relatively unchanged by instruction. Findings also indicate that the interaction of the instructional strategies together contributed to student learning. Based on these results, IE methods should be adopted in introductory physics classes, particularly in classes where students have low pre-test scores. It is also important to provide support for instructors new to IE strategies.

Distance Learning 2000: Proceedings of the Annual Conference on Distance Teaching & Learning (16th, Madison, Wisconsin, August 2-4, 2000).

ERIC Educational Resources Information Center

Wisconsin Univ. System, Madison.

These proceedings contain 75 papers from information sessions that address important human factors in distance education from several perspectives, including implementation planning, management and policy, instructional design, teaching methods, faculty development, learning environments, learner supports, and evaluation. Among the papers are:…

Computer-Aided College Algebra: Learning Components that Students Find Beneficial

ERIC Educational Resources Information Center

Aichele, Douglas B.; Francisco, Cynthia; Utley, Juliana; Wescoatt, Benjamin

2011-01-01

A mixed-method study was conducted during the Fall 2008 semester to better understand the experiences of students participating in computer-aided instruction of College Algebra using the software MyMathLab. The learning environment included a computer learning system for the majority of the instruction, a support system via focus groups (weekly…

Active-Learning Methods To Improve Student Performance and Scientific Interest in a Large Introductory Oceanography Course.

ERIC Educational Resources Information Center

Yuretich, Richard F.; Khan, Samia A.; Leckie, R. Mark; Clement, John J.

2001-01-01

Transfers the environment of a large enrollment oceanography course by modifying lectures to include cooperative learning via interactive in-class exercises and directed discussion. Results of student surveys, course evaluations, and exam performance demonstrate that learning of the subject under these conditions has improved. (Author/SAH)

Learning from escaped prescribed fire reviews

Treesearch

Anne E. Black; Dave Thomas; James Saveland; Jennifer D. Ziegler

2011-01-01

The U.S. wildland fire community has developed a number of innovative methods for conducting a review following escape of a prescribed fire (expanding on the typical regional or local reviews, to include more of a learning focus - expanded After Action Reviews, reviews that incorporate High Reliability Organizing, Facilitated Learning Analyses, etc). The stated purpose...

Service Learning in Introductory Astronomy

ERIC Educational Resources Information Center

Orleski, Michael

2013-01-01

Service learning is a method of instruction where the students in a course use the course's content in a service project. The service is included as a portion of the students' course grades. During the fall semester 2010, service learning was incorporated into the Introduction to Astronomy course at Misericordia University. The class had eight…

Reasons and Methods to Learn the Management

ERIC Educational Resources Information Center

Li, Hongxin; Ding, Mengchun

2010-01-01

Reasons for learning the management include (1) perfecting the knowledge structure, (2) the management is the base of all organizations, (3) one person may be the manager or the managed person, (4) the management is absolutely not simple knowledge, and (5) the learning of the theoretical knowledge of the management can not be replaced by the…

Use of Web 2.0 Technologies to Enhance Learning Experiences in Alternative School Settings

ERIC Educational Resources Information Center

Karahan, Engin; Roehrig, Gillian

2016-01-01

As the learning paradigms are shifting to include various forms of digital technologies such as synchronous, asynchronous, and interactive methods, social networking technologies have been introduced to the educational settings in order to increase the quality of learning environments. The literature suggests that effective application of these…

The Impact of Different Teaching Approaches and Languages on Student Learning of Introductory Programming Concepts

ERIC Educational Resources Information Center

Kunkle, Wanda M.

2010-01-01

Many students experience difficulties learning to program. They find learning to program in the object-oriented paradigm particularly challenging. As a result, computing educators have tried a variety of instructional methods to assist beginning programmers. These include developing approaches geared specifically toward novices and experimenting…

Building Comprehensive High School Guidance Programs through the Smaller Learning Communities Model

ERIC Educational Resources Information Center

Harper, Geralyn

2013-01-01

Despite many reform initiatives, including the federally funded initiative titled the Smaller Learning Communities' (SLC) Model, many students are still underexposed to comprehensive guidance programs. The purpose of this mixed method project study was to examine which components in a comprehensive guidance program for the learning academies at a…

ESL Instruction and Adults with Learning Disabilities. ERIC Digest.

ERIC Educational Resources Information Center

Schwarz, Robin; Terrill, Lynda

This digest reviews what is known about adult English-as-a-Second-Language (ESL) learners and learning disabilities, suggests ways to identify and assess ESL adults who may have learning disabilities, and offers practical methods for both instruction and teacher training. Topics covered in some detail include identifying and diagnosing learning…

Unpacking "Active Learning": A Combination of Flipped Classroom and Collaboration Support Is More Effective but Collaboration Support Alone Is Not

ERIC Educational Resources Information Center

Rau, Martina A.; Kennedy, Kristopher; Oxtoby, Lucas; Bollom, Mark; Moore, John W.

2017-01-01

Much evidence shows that instruction that actively engages students with learning materials is more effective than traditional, lecture-centric instruction. These "active learning" models comprise an extremely heterogeneous set of instructional methods: they often include collaborative activities, flipped classrooms, or a combination of…

Active Learning Strategies and Assessment in World Geography Classes

ERIC Educational Resources Information Center

Klein, Phil

2003-01-01

Active learning strategies include a variety of methods, such as inquiry and discovery, in which students are actively engaged in the learning process. This article describes several strategies that can be used in secondary-or college-level world geography courses. The goal of these activities is to foster development of a spatial perspective in…

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

PubMed

Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

2017-01-01

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

A review of microbiology service learning.

PubMed

Webb, Ginny

2017-02-01

Service learning is a teaching method that incorporates community engagement into the curriculum of a course. Service learning is becoming increasingly popular on college campuses and across disciplines. Studies have shown many benefits to service learning for the students and the community they serve. Service learning has been incorporated into science courses, including microbiology. This review will address the benefits to service learning and provide an overview of the various types of service-learning projects that have been completed in microbiology courses. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods

PubMed Central

Vera, L.; Pérez-Beteta, J.; Molina, D.; Borrás, J. M.; Benavides, M.; Barcia, J. A.; Velásquez, C.; Albillo, D.; Lara, P.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Machine learning methods are integrated in clinical research studies due to their strong capability to discover parameters having a high information content and their predictive combined potential. Several studies have been developed using glioblastoma patient’s imaging data. Many of them have focused on including large numbers of variables, mostly two-dimensional textural features and/or genomic data, regardless of their meaning or potential clinical relevance. Materials and methods: 193 glioblastoma patients were included in the study. Preoperative 3D magnetic resonance images were collected and semi-automatically segmented using an in-house software. After segmentation, a database of 90 parameters including geometrical and textural image-based measures together with patients’ clinical data (including age, survival, type of treatment, etc.) was constructed. The criterion for including variables in the study was that they had either shown individual impact on survival in single or multivariate analyses or have a precise clinical or geometrical meaning. These variables were used to perform several machine learning experiments. In a first set of computational cross-validation experiments based on regression trees, those attributes showing the highest information measures were extracted. In the second phase, more sophisticated learning methods were employed in order to validate the potential of the previous variables predicting survival. Concretely support vector machines, neural networks and sparse grid methods were used. Results: Variables showing high information measure in the first phase provided the best prediction results in the second phase. Specifically, patient age, Stupp regimen and a geometrical measure related with the irregularity of contrast-enhancing areas were the variables showing the highest information measure in the first stage. For the second phase, the combinations of patient age and Stupp regimen together with one tumor geometrical measure and one tumor heterogeneity feature reached the best quality prediction. Conclusions: Advanced machine learning methods identified the parameters with the highest information measure and survival predictive potential. The uninformed machine learning methods identified a novel feature measure with direct impact on survival. Used in combination with other previously known variables multi-indexes can be defined that can help in tumor characterization and prognosis prediction. Recent advances on the definition of those multi-indexes will be reported in the conference. Funding: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

Influence of a veterinary curriculum on the approaches and study skills of veterinary medical students.

PubMed

Chigerwe, Munashe; Ilkiw, Jan E; Boudreaux, Karen A

2011-01-01

The objectives of the present study were to evaluate first-, second-, third-, and fourth-year veterinary medical students' approaches to studying and learning as well as the factors within the curriculum that may influence these approaches. A questionnaire consisting of the short version of the Approaches and Study Skills Inventory for Students (ASSIST) was completed by 405 students, and it included questions relating to conceptions about learning, approaches to studying, and preferences for different types of courses and teaching. Descriptive statistics, factor analysis, Cronbach's alpha analysis, and log-linear analysis were performed on the data. Deep, strategic, and surface learning approaches emerged. There were a few differences between our findings and those presented in previous studies in terms of the correlation of the subscale monitoring effectiveness, which showed loading with both the deep and strategic learning approaches. In addition, the subscale alertness to assessment demands showed correlation with the surface learning approach. The perception of high workloads, the use of previous test files as a method for studying, and examinations that are based only on material provided in lecture notes were positively associated with the surface learning approach. Focusing on improving specific teaching and assessment methods that enhance deep learning is anticipated to enhance students' positive learning experience. These teaching methods include instructors who encourage students to be critical thinkers, the integration of course material in other disciplines, courses that encourage thinking and reading about the learning material, and books and articles that challenge students while providing explanations beyond lecture material.

Methods of integrating Islamic values in teaching biology for shaping attitude and character

NASA Astrophysics Data System (ADS)

Listyono; Supardi, K. I.; Hindarto, N.; Ridlo, S.

2018-03-01

Learning is expected to develop the potential of learners to have the spiritual attitude: moral strength, self-control, personality, intelligence, noble character, as well as the skills needed by themselves, society, and nation. Implementation of role and morale in learning is an alternative way which is expected to answer the challenge. The solution offered is to inject student with religious material Islamic in learning biology. The content value of materials teaching biology includes terms of practical value, religious values, daily life value, socio-political value, and the value of art. In Islamic religious values (Qur'an and Hadith) various methods can touch human feelings, souls, and generate motivation. Integrating learning with Islamic value can be done by the deductive or inductive approach. The appropriate method of integration is the amtsal (analog) method, hiwar (dialog) method, targhib & tarhib (encouragement & warning) method, and example method (giving a noble role model / good example). The right strategy in integrating Islamic values is outlined in the design of lesson plan. The integration of Islamic values in lesson plan will facilitate teachers to build students' character because Islamic values can be implemented in every learning steps so students will be accustomed to receiving the character value in this integrated learning.

Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

DTIC Science & Technology

2013-11-01

development of a class of “gradient free” optimization techniques; these include local approaches, such as a Nelder- Mead simplex search (c.f. [73]), and global...1Note that this simple method differs from the Nelder Mead constrained nonlinear optimization method [73]. 39 the Non-dominated Sorting Genetic Algorithm...Kober, and Jan Peters. Model-free inverse reinforcement learning. In International Conference on Artificial Intelligence and Statistics, 2011. [12] George

Rapidly Measuring the Speed of Unconscious Learning: Amnesics Learn Quickly and Happy People Slowly

PubMed Central

Dienes, Zoltan; Baddeley, Roland J.; Jansari, Ashok

2012-01-01

Background We introduce a method for quickly determining the rate of implicit learning. Methodology/Principal Findings The task involves making a binary prediction for a probabilistic sequence over 10 minutes; from this it is possible to determine the influence of events of a different number of trials in the past on the current decision. This profile directly reflects the learning rate parameter of a large class of learning algorithms including the delta and Rescorla-Wagner rules. To illustrate the use of the method, we compare a person with amnesia with normal controls and we compare people with induced happy and sad moods. Conclusions/Significance Learning on the task is likely both associative and implicit. We argue theoretically and demonstrate empirically that both amnesia and also transient negative moods can be associated with an especially large learning rate: People with amnesia can learn quickly and happy people slowly. PMID:22457759

Anatomical Society core regional anatomy syllabus for undergraduate medicine: the Delphi process.

PubMed

Smith, C F; Finn, G M; Stewart, J; McHanwell, S

2016-01-01

A modified Delphi method was employed to seek consensus when revising the UK and Ireland's core syllabus for regional anatomy in undergraduate medicine. A Delphi panel was constructed involving 'expert' (individuals with at least 5 years' experience in teaching medical students anatomy at the level required for graduation). The panel (n = 39) was selected and nominated by members of Council and/or the Education Committee of the Anatomical Society and included a range of specialists including surgeons, radiologists and anatomists. The experts were asked in two stages to 'accept', 'reject' or 'modify' (first stage only) each learning outcome. A third stage, which was not part of the Delphi method, then allowed the original authors of the syllabus to make changes either to correct any anatomical errors or to make minor syntax changes. From the original syllabus of 182 learning outcomes, removing the neuroanatomy component (163), 23 learning outcomes (15%) remained unchanged, seven learning outcomes were removed and two new learning outcomes added. The remaining 133 learning outcomes were modified. All learning outcomes on the new core syllabus achieved over 90% acceptance by the panel. © 2015 Anatomical Society.

The learner’s perspective in GP teaching practices with multi-level learners: a qualitative study

PubMed Central

2014-01-01

Background Medical students, junior hospital doctors on rotation and general practice (GP) registrars are undertaking their training in clinical general practices in increasing numbers in Australia. Some practices have four levels of learner. This study aimed to explore how multi-level teaching (also called vertical integration of GP education and training) is occurring in clinical general practice and the impact of such teaching on the learner. Methods A qualitative research methodology was used with face-to-face, semi-structured interviews of medical students, junior hospital doctors, GP registrars and GP teachers in eight training practices in the region that taught all levels of learners. Interviews were audio-recorded and transcribed. Qualitative analysis was conducted using thematic analysis techniques aided by the use of the software package N-Vivo 9. Primary themes were identified and categorised by the co-investigators. Results 52 interviews were completed and analysed. Themes were identified relating to both the practice learning environment and teaching methods used. A practice environment where there is a strong teaching culture, enjoyment of learning, and flexible learning methods, as well as learning spaces and organised teaching arrangements, all contribute to positive learning from a learners’ perspective. Learners identified a number of innovative teaching methods and viewed them as positive. These included multi-level learner group tutorials in the practice, being taught by a team of teachers, including GP registrars and other health professionals, and access to a supernumerary GP supervisor (also termed “GP consultant teacher”). Other teaching methods that were viewed positively were parallel consulting, informal learning and rural hospital context integrated learning. Conclusions Vertical integration of GP education and training generally impacted positively on all levels of learner. This research has provided further evidence about the learning culture, structures and teaching processes that have a positive impact on learners in the clinical general practice setting where there are multiple levels of learners. It has also identified some innovative teaching methods that will need further examination. The findings reinforce the importance of the environment for learning and learner centred approaches and will be important for training organisations developing vertically integrated practices and in their training of GP teachers. PMID:24645670

Promoting clinical competence: using scaffolded instruction for practice-based learning.

PubMed

Tilley, Donna Scott; Allen, Patricia; Collins, Cathie; Bridges, Ruth Ann; Francis, Patricia; Green, Alexia

2007-01-01

Competency-based education is essential for bridging the gap between education and practice. The attributes of competency-based education include an outcomes focus, allowance for increasing levels of competency, learner accountability, practice-based learning, self-assessment, and individualized learning experiences. One solution to this challenge is scaffolded instruction, where collaboration and knowledge facilitate learning. Collaboration refers to the role of clinical faculty who model desired clinical skills then gradually shift responsibility for nursing activity to the student. This article describes scaffolded instruction as applied in a Web-based second-degree bachelor of science in nursing (BSN) program. This second-degree BSN program uses innovative approaches to education, including a clinical component that relies on clinical coaches. Students in the program remain in their home community and complete their clinical hours with an assigned coach. The method will be described first, followed by a description of how the method was applied.

The New South Wales Allied Health Workplace Learning Study: barriers and enablers to learning in the workplace

PubMed Central

2014-01-01

Background Workplace learning refers to continuing professional development that is stimulated by and occurs through participation in workplace activities. Workplace learning is essential for staff development and high quality clinical care. The purpose of this study was to explore the barriers to and enablers of workplace learning for allied health professionals within NSW Health. Methods A qualitative study was conducted with a purposively selected maximum variation sample (n = 46) including 19 managers, 19 clinicians and eight educators from 10 allied health professions. Seven semi-structured interviews and nine focus groups were audio-recorded and transcribed. The ‘framework approach’ was used to guide the interviews and analysis. Textual data were coded and charted using an evolving thematic framework. Results Key enablers of workplace learning included having access to peers, expertise and ‘learning networks’, protected learning time, supportive management and positive staff attitudes. The absence of these key enablers including heavy workload and insufficient staffing were important barriers to workplace learning. Conclusion Attention to these barriers and enablers may help organisations to more effectively optimise allied health workplace learning. Ultimately better workplace learning may lead to improved patient, staff and organisational outcomes. PMID:24661614

Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of traditional Chinese medicine.

PubMed

Chen, Zhao; Cao, Yanfeng; He, Shuaibing; Qiao, Yanjiang

2018-01-01

Action (" gongxiao " in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties (" yaoxing " in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory. One hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction. As results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered. The performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Improving Junior High School Students' Mathematical Analogical Ability Using Discovery Learning Method

ERIC Educational Resources Information Center

Maarif, Samsul

2016-01-01

The aim of this study was to identify the influence of discovery learning method towards the mathematical analogical ability of junior high school's students. This is a research using factorial design 2x2 with ANOVA-Two ways. The population of this research included the entire students of SMPN 13 Jakarta (State Junior High School 13 of Jakarta)…

A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

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

Hemphill, Geralyn M.

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to bemore » an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.« less

Effects of e-learning, lectures, and role playing on nursing students’ knowledge acquisition, retention and satisfaction

PubMed Central

Pourghaznein, Tayebeh; Sabeghi, Hakimeh; Shariatinejad, Keyvan

2015-01-01

Background: Nursing education can maintain its dynamic quality when it moves toward innovation and modern methods of teaching and learning. Therefore, teachers are required to employ up to date methods in their teaching plans. This study evaluated the effects of e-learning, lectures, and role playing on nursing students’ learning, retention, and satisfaction. Methods: Sixty nursing students were selected as an experiment and control groups during two consecutive semesters. The educational content was presented as e-learning and role playing during one semester (experiment group) and as lectures in the next semester (control group). A questionnaire containing three parts was used to assess demographics, learning and satisfaction statuses. The questionnaire also included a final openended question to evaluate the students’ ideas about the whole course. Results: The mean scores of posttest were 16.13 ± 1.37 using role playing, 15.50 ± 1.44 using e-learning and 16.45 ± 1.23 using lectures. The differences between the mean scores of posttest and pretest were 12.84 ± 1.43, 12.56 ± 1.57, and 13.73 ± 1.53 in the mentioned methods, respectively. Lectures resulted in significantly better learning compared to role playing and e-learning. In contrast, retention rates were significantly lower using lectures than using role playing and e-learning. Students’ satisfaction from e-learning was significantly lower than lecturing and role playing. Conclusion: Due to the lower rates of retention following lectures, the teachers are recommended to use student- centered approaches in their lectures. Since students’ satisfaction with e-learning was lower than the other methods, further studies are suggested to explore the problems of e-learning in Iran. PMID:26000257

eLearning resources to supplement postgraduate neurosurgery training.

PubMed

Stienen, Martin N; Schaller, Karl; Cock, Hannah; Lisnic, Vitalie; Regli, Luca; Thomson, Simon

2017-02-01

In an increasingly complex and competitive professional environment, improving methods to educate neurosurgical residents is key to ensure high-quality patient care. Electronic (e)Learning resources promise interactive knowledge acquisition. We set out to give a comprehensive overview on available eLearning resources that aim to improve postgraduate neurosurgical training and review the available literature. A MEDLINE query was performed, using the search term "electronic AND learning AND neurosurgery". Only peer-reviewed English-language articles on the use of any means of eLearning to improve theoretical knowledge in postgraduate neurosurgical training were included. Reference lists were crosschecked for further relevant articles. Captured parameters were the year, country of origin, method of eLearning reported, and type of article, as well as its conclusion. eLearning resources were additionally searched for using Google. Of n = 301 identified articles by the MEDLINE search, n = 43 articles were analysed in detail. Applying defined criteria, n = 28 articles were excluded and n = 15 included. Most articles were generated within this decade, with groups from the USA, the UK and India having a leadership role. The majority of articles reviewed existing eLearning resources, others reported on the concept, development and use of generated eLearning resources. There was no article that scientifically assessed the effectiveness of eLearning resources (against traditional learning methods) in terms of efficacy or costs. Only one article reported on satisfaction rates with an eLearning tool. All authors of articles dealing with eLearning and the use of new media in neurosurgery uniformly agreed on its great potential and increasing future use, but most also highlighted some weaknesses and possible dangers. This review found only a few articles dealing with the modern aspects of eLearning as an adjunct to postgraduate neurosurgery training. Comprehensive eLearning platforms offering didactic modules with clear learning objectives are rare. Two decades after the rise of eLearning in neurosurgery, some promising solutions are readily available, but the potential of eLearning has not yet been sufficiently exploited.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

PubMed

Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

PubMed Central

Zhang, Hongjun; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score. PMID:28894463

Correct machine learning on protein sequences: a peer-reviewing perspective.

PubMed

Walsh, Ian; Pollastri, Gianluca; Tosatto, Silvio C E

2016-09-01

Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Science + Writing = Super Learning. Writing Workshop.

ERIC Educational Resources Information Center

Bower, Paula Rogovin

1993-01-01

Article presents suggestions for motivating elementary students to learn by combining science and writing. The strategies include planning the right environment; teaching the scientific method; establishing a link to literature; and making time for students to observe, experiment, and write. (SM)

Evaluating groups in learning disabilities.

PubMed

Chia, S H

Groupwork can be effective in meeting a range of needs presented by students with profound learning disabilities. This article describes the process involved in setting up groups for these students, and includes examples of a group session and methods for evaluating groupwork.

Developing Language Learning Textbooks Enriched with Sense of Literacy: The Case of Junior High School in Indonesia

ERIC Educational Resources Information Center

Sodiq, Syamsul

2015-01-01

This research is aimed at developing an Indonesian course-books integrated with the materials for life skill education (LSE). It can support effective learning through literacy models and results qualified book on Indonesian language learning. By applying Fenrich's method on development model (1997) include five phases of analysis, planning,…

Eye Tracking and Early Detection of Confusion in Digital Learning Environments: Proof of Concept

ERIC Educational Resources Information Center

Pachman, Mariya; Arguel, Amaël; Lockyer, Lori; Kennedy, Gregor; Lodge, Jason M.

2016-01-01

Research on incidence of and changes in confusion during complex learning and problem-solving calls for advanced methods of confusion detection in digital learning environments (DLEs). In this study we attempt to address this issue by investigating the use of multiple measures, including psychophysiological indicators and self-ratings, to detect…

Translating Knowledge through Blended Learning: A Comparative Analysis of Face-to-Face and Blended Learning Methods

ERIC Educational Resources Information Center

Golden, Thomas P.; Karpur, Arun

2012-01-01

This study is a comparative analysis of the impact of traditional face-to-face training contrasted with a blended learning approach, as it relates to improving skills, knowledge and attitudes for enhancing practices for achieving improved employment outcomes for individuals with disabilities. The study included two intervention groups: one…

Shaping the College Curriculum: Academic Plans in Action.

ERIC Educational Resources Information Center

Stark, Joan S.; Lattuca, Lisa R.

This book proposes a broad view of the college curriculum, suggesting that it be defined as an "academic plan." The plan included decisions about what, why, and how students learn; ways to determine whether students have learned what they are supposed to learn; and methods of using this information to improve the plan. Taking both a macro and a…

Researching into Learning Resources in Colleges and Universities. The Practical Research Series.

ERIC Educational Resources Information Center

Higgins, Chris; Reading, Judy; Taylor, Paul

This book examines issues and methods for conducting research into the educational resource environment in colleges and universities. That environment is defined as whatever is used to facilitate the learning process, including learning space, support staff, and teaching staff. Chapter 1 is an introduction to the series and lays out the process of…

Tracking Skill Acquisition with Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model with Covariates

ERIC Educational Resources Information Center

Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A.

2018-01-01

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…

Bridging the Gap: Cognitive and Social Approaches to Research in Second Language Learning and Teaching

ERIC Educational Resources Information Center

Hulstijn, Jan H.; Young, Richard F.; Ortega, Lourdes; Bigelow, Martha; DeKeyser, Robert; Ellis, Nick C.; Lantolf, James P.; Mackey, Alison; Talmy, Steven

2014-01-01

For some, research in learning and teaching of a second language (L2) runs the risk of disintegrating into irreconcilable approaches to L2 learning and use. On the one side, we find researchers investigating linguistic-cognitive issues, often using quantitative research methods including inferential statistics; on the other side, we find…

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

NASA Astrophysics Data System (ADS)

Xu, Jun; Zhang, Lei; Zhang, David

2018-06-01

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real noisy images.

Random learning units using WIRIS quizzes in Moodle

NASA Astrophysics Data System (ADS)

Mora, Ángel; Mérida, Enrique; Eixarch, Ramon

2011-09-01

Moodle is an extended learning management system for developing learning units, including mathematically-based subjects. A wide variety of material can be developed in Moodle which contains facilities for forums, questionnaires, lessons, tasks, wikis, glossaries and chats. Therefore, the Moodle platform provides a meeting point for those working in a mathematics course. Mathematics requires special materials and activities: The material must include mathematical objects and the activities included in the virtual course must be able to do mathematical computations. WIRIS is a powerful software for educational environments. It has libraries for calculus, algebra, geometry and much more. In this article, examples showing the use of WIRIS in numerical methods and examples of using a new tool, WIRIS quizzes, are illustrated. By enhancing Moodle with WIRIS, we can add random learning questions to modules. Moodle has a simpler version of this capability, but WIRIS extends the method in which the random material is presented to the students. Random objects can appear in a question, in a variable of a question, in a plot or in the definition of a mathematical object. This article illustrates material prepared for numerical methods using a WIRIS library integrated in WIRIS quizzes. As a result, WIRIS in Moodle can be considered as a global solution for mathematics education.

Machine learning methods in chemoinformatics

PubMed Central

Mitchell, John B O

2014-01-01

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

E-learning and nursing assessment skills and knowledge - An integrative review.

PubMed

McDonald, Ewan W; Boulton, Jessica L; Davis, Jacqueline L

2018-07-01

This review examines the current evidence on the effectiveness of digital technologies or e-based learning for enhancing the skills and knowledge of nursing students in nursing assessment. This integrative review identifies themes emerging from e-learning and 'nursing assessment' literature. Literature reviews have been undertaken in relation to digital learning and nursing education, including clinical skills, clinical case studies and the nurse-educator role. Whilst perceptions of digital learning are well covered, a gap in knowledge persists for understanding the effectiveness of e-learning on nursing assessment skills and knowledge. This is important as comprehensive assessment skills and knowledge are a key competency for newly qualified nurses. The MEDLINE, CINAHL, Cochrane Library and ProQuest Nursing and Allied Health Source electronic databases were searched for the period 2006 to 2016. Hand searching in bibliographies was also undertaken. Selection criteria for this review included: FINDINGS: Twenty articles met the selection criteria for this review, and five major themes for e-based learning were identified (a) students become self-evaluators; (b) blend and scaffold learning; (c) measurement of clinical reasoning; (d) mobile technology and Facebook are effective; and (e) training and preparation is vital. Although e-based learning programs provide a flexible teaching method, evidence suggests e-based learning alone does not exceed face-to-face patient simulation. This is particularly the case where nursing assessment learning is not scaffolded. This review demonstrates that e-based learning and traditional teaching methods used in conjunction with each other create a superior learning style. Copyright © 2018 Elsevier Ltd. All rights reserved.

Algorithms for Learning Preferences for Sets of Objects

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; desJardins, Marie; Eaton, Eric

2010-01-01

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features.

Flipped Learning With Simulation in Undergraduate Nursing Education.

PubMed

Kim, HeaRan; Jang, YounKyoung

2017-06-01

Flipped learning has proliferated in various educational environments. This study aimed to verify the effects of flipped learning on the academic achievement, teamwork skills, and satisfaction levels of undergraduate nursing students. For the flipped learning group, simulation-based education via the flipped learning method was provided, whereas traditional, simulation-based education was provided for the control group. After completion of the program, academic achievement, teamwork skills, and satisfaction levels were assessed and analyzed. The flipped learning group received higher scores on academic achievement, teamwork skills, and satisfaction levels than the control group, including the areas of content knowledge and clinical nursing practice competency. In addition, this difference gradually increased between the two groups throughout the trial. The results of this study demonstrated the positive, statistically significant effects of the flipped learning method on simulation-based nursing education. [J Nurs Educ. 2017;56(6):329-336.]. Copyright 2017, SLACK Incorporated.

Case study of a problem-based learning course of physics in a telecommunications engineering degree

NASA Astrophysics Data System (ADS)

Macho-Stadler, Erica; Jesús Elejalde-García, Maria

2013-08-01

Active learning methods can be appropriate in engineering, as their methodology promotes meta-cognition, independent learning and problem-solving skills. Problem-based learning is the educational process by which problem-solving activities and instructor's guidance facilitate learning. Its key characteristic involves posing a 'concrete problem' to initiate the learning process, generally implemented by small groups of students. Many universities have developed and used active methodologies successfully in the teaching-learning process. During the past few years, the University of the Basque Country has promoted the use of active methodologies through several teacher training programmes. In this paper, we describe and analyse the results of the educational experience using the problem-based learning (PBL) method in a physics course for undergraduates enrolled in the technical telecommunications engineering degree programme. From an instructors' perspective, PBL strengths include better student attitude in class and increased instructor-student and student-student interactions. The students emphasised developing teamwork and communication skills in a good learning atmosphere as positive aspects.

A Survey of tooth morphology teaching methods employed in the United Kingdom and Ireland.

PubMed

Lone, M; McKenna, J P; Cryan, J F; Downer, E J; Toulouse, A

2018-01-15

Tooth morphology is a central component of the dental curriculum and is applicable to all dental specialities. Traditional teaching methods are being supplemented with innovative strategies to tailor teaching and accommodate the learning styles of the recent generation of students. An online survey was compiled and distributed to the staff involved in teaching tooth morphology in the United Kingdom and Ireland to assess the importance of tooth morphology in the dentistry curriculum and the methodologies employed in teaching. The results of the survey show that tooth morphology constitutes a small module in the dental curriculum. It is taught in the first 2 years of the dental curriculum but is applicable in the clinical years and throughout the dental career. Traditional teaching methods, lecture and practical, are being augmented with innovative teaching including e-learning via virtual learning environment, tooth atlas and e-books leading to blended learning. The majority of the schools teach both normal dental anatomy and morphologic variations of dental anatomy and utilise plastic teeth for practical and examination purposes. Learning the 3D aspects of tooth morphology was deemed important by most of the respondents who also agreed that tooth morphology is a difficult topic for the students. Despite being core to the dental curriculum, overall minimal time is dedicated to the delivery of tooth morphology, creating a reliance on the student to learn the material. New forms of delivery including computer-assisted learning tools should help sustain learning and previously acquired knowledge. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Learning curve for robotic-assisted surgery for rectal cancer: use of the cumulative sum method.

PubMed

Yamaguchi, Tomohiro; Kinugasa, Yusuke; Shiomi, Akio; Sato, Sumito; Yamakawa, Yushi; Kagawa, Hiroyasu; Tomioka, Hiroyuki; Mori, Keita

2015-07-01

Few data are available to assess the learning curve for robotic-assisted surgery for rectal cancer. The aim of the present study was to evaluate the learning curve for robotic-assisted surgery for rectal cancer by a surgeon at a single institute. From December 2011 to August 2013, a total of 80 consecutive patients who underwent robotic-assisted surgery for rectal cancer performed by the same surgeon were included in this study. The learning curve was analyzed using the cumulative sum method. This method was used for all 80 cases, taking into account operative time. Operative procedures included anterior resections in 6 patients, low anterior resections in 46 patients, intersphincteric resections in 22 patients, and abdominoperineal resections in 6 patients. Lateral lymph node dissection was performed in 28 patients. Median operative time was 280 min (range 135-683 min), and median blood loss was 17 mL (range 0-690 mL). No postoperative complications of Clavien-Dindo classification Grade III or IV were encountered. We arranged operative times and calculated cumulative sum values, allowing differentiation of three phases: phase I, Cases 1-25; phase II, Cases 26-50; and phase III, Cases 51-80. Our data suggested three phases of the learning curve in robotic-assisted surgery for rectal cancer. The first 25 cases formed the learning phase.

Interactive Learning: The Casewriting Method as an Entire Semester Course for Higher Education.

ERIC Educational Resources Information Center

Bowen, Brent D.

This guide explains the reasons for employing the case method as a tool in the academic discipline of aviation. It promotes the use of case writing as a unique opportunity to derive even further benefits from case analysis. The benefits to students of using case writing as a learning strategy include a focus on the strategy of a real situation;…

Assessing the Impact of Student Learning Style Preferences

NASA Astrophysics Data System (ADS)

Davis, Stacey M.; Franklin, Scott V.

2004-09-01

Students express a wide range of preferences for learning environments. We are trying to measure the manifestation of learning styles in various learning environments. In particular, we are interested in performance in an environment that disagrees with the expressed learning style preference, paying close attention to social (group vs. individual) and auditory (those who prefer to learn by listening) environments. These are particularly relevant to activity-based curricula which typically emphasize group-work and de-emphasize lectures. Our methods include multiple-choice assessments, individual student interviews, and a study in which we attempt to isolate the learning environment.

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.

Tips for Science Teachers Having Students with Disabilities.

ERIC Educational Resources Information Center

Keller, Ed C., Jr.

This document highlights strategies for teaching science students with common learning disabilities. For each learning disability listed, there are sections on courtesy and several teaching methods with mitigative teaching strategies. Highlighted disabilities include Attention Deficit Disorder (ADD), Emotional Disabilities, Epilepsy, Hearing…

Does Enjoyment Accompany Learning? A Student Perceptions Inquiry.

ERIC Educational Resources Information Center

Blai, Boris, Jr.

1979-01-01

Discusses a study conducted at Harcum Junior College, a private, two-year, women's college, to elicit students' perceptions of a variety of learning experiences/teaching methods and of their relative enjoyment levels with regard to these experiences. Includes the questionnaire. (AYC)

The effectiveness of humane teaching methods in veterinary education.

PubMed

Knight, Andrew

2007-01-01

Animal use resulting in harm or death has historically played an integral role in veterinary education, in disciplines such as surgery, physiology, biochemistry, anatomy, pharmacology, and parasitology. However, many non-harmful alternatives now exist, including computer simulations, high quality videos, ''ethically-sourced cadavers'' such as from animals euthanased for medical reasons, preserved specimens, models and surgical simulators, non-invasive self-experimentation, and supervised clinical experiences. Veterinary students seeking to use such methods often face strong opposition from faculty members, who usually cite concerns about their teaching efficacy. Consequently, studies of veterinary students were reviewed comparing learning outcomes generated by non-harmful teaching methods with those achieved by harmful animal use. Of eleven published from 1989 to 2006, nine assessed surgical training--historically the discipline involving greatest harmful animal use. 45.5% (5/11) demonstrated superior learning outcomes using more humane alternatives. Another 45.5% (5/11) demonstrated equivalent learning outcomes, and 9.1% (1/11) demonstrated inferior learning outcomes. Twenty one studies of non-veterinary students in related academic disciplines were also published from 1968 to 2004. 38.1% (8/21) demonstrated superior, 52.4% (11/21) demonstrated equivalent, and 9.5% (2/21) demonstrated inferior learning outcomes using humane alternatives. Twenty nine papers in which comparison with harmful animal use did not occur illustrated additional benefits of humane teaching methods in veterinary education, including: time and cost savings, enhanced potential for customisation and repeatability of the learning exercise, increased student confidence and satisfaction, increased compliance with animal use legislation, elimination of objections to the use of purpose-killed animals, and integration of clinical perspectives and ethics early in the curriculum. The evidence demonstrates that veterinary educators can best serve their students and animals, while minimising financial and time burdens, by introducing well-designed teaching methods not reliant on harmful animal use.

Training, Simulation, the Learning Curve, and How to Reduce Complications in Urology.

PubMed

Brunckhorst, Oliver; Volpe, Alessandro; van der Poel, Henk; Mottrie, Alexander; Ahmed, Kamran

2016-04-01

Urology is at the forefront of minimally invasive surgery to a great extent. These procedures produce additional learning challenges and possess a steep initial learning curve. Training and assessment methods in surgical specialties such as urology are known to lack clear structure and often rely on differing operative flow experienced by individuals and institutions. This article aims to assess current urology training modalities, to identify the role of simulation within urology, to define and identify the learning curves for various urologic procedures, and to discuss ways to decrease complications in the context of training. A narrative review of the literature was conducted through December 2015 using the PubMed/Medline, Embase, and Cochrane Library databases. Evidence of the validity of training methods in urology includes observation of a procedure, mentorship and fellowship, e-learning, and simulation-based training. Learning curves for various urologic procedures have been recommended based on the available literature. The importance of structured training pathways is highlighted, with integration of modular training to ensure patient safety. Valid training pathways are available in urology. The aim in urology training should be to combine all of the available evidence to produce procedure-specific curricula that utilise the vast array of training methods available to ensure that we continue to improve patient outcomes and reduce complications. The current evidence for different training methods available in urology, including simulation-based training, was reviewed, and the learning curves for various urologic procedures were critically analysed. Based on the evidence, future pathways for urology curricula have been suggested to ensure that patient safety is improved. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Intellectual Innovation: A Paradigm Shift in Workforce Development

DTIC Science & Technology

2016-08-01

varying learning abilities and disabilities , and require vary­ ing lengths of time to learn and Although experienced employees need less training...training courses or objectives, organizations should develop a tailored plan that focuses on what each employee needs to learn . Time and effort are... learns in a different way, which can include the use of visual and/or audible as well as the hands­on method of instruc­ tion. Employees also have

Learning Rotation-Invariant Local Binary Descriptor.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2017-08-01

In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors, such as local binary pattern and its variants, which require strong prior knowledge, local binary feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local binary descriptors, such as compact binary face descriptor and simultaneous local binary feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same binary descriptor. Then, we construct a codebook by a clustering method on the learned binary codes, and obtain a histogram feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Discriminative graph embedding for label propagation.

PubMed

Nguyen, Canh Hao; Mamitsuka, Hiroshi

2011-09-01

In many applications, the available information is encoded in graph structures. This is a common problem in biological networks, social networks, web communities and document citations. We investigate the problem of classifying nodes' labels on a similarity graph given only a graph structure on the nodes. Conventional machine learning methods usually require data to reside in some Euclidean spaces or to have a kernel representation. Applying these methods to nodes on graphs would require embedding the graphs into these spaces. By embedding and then learning the nodes on graphs, most methods are either flexible with different learning objectives or efficient enough for large scale applications. We propose a method to embed a graph into a feature space for a discriminative purpose. Our idea is to include label information into the embedding process, making the space representation tailored to the task. We design embedding objective functions that the following learning formulations become spectral transforms. We then reformulate these spectral transforms into multiple kernel learning problems. Our method, while being tailored to the discriminative tasks, is efficient and can scale to massive data sets. We show the need of discriminative embedding on some simulations. Applying to biological network problems, our method is shown to outperform baselines.

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

NASA Technical Reports Server (NTRS)

Hearn, Tristan A.

2015-01-01

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Using Deep Learning to Analyze the Voices of Stars.

NASA Astrophysics Data System (ADS)

Boudreaux, Thomas Macaulay

2018-01-01

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and compare the performance of different deep learning algorithms, including Artifical Neural Netoworks, and Convolutional Neural Networks, in classifing these synthetic data sets as either pulsators, or not observed to vary stars.

Deep learning methods for protein torsion angle prediction.

PubMed

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

A delivery mode study: The effect of self-paced video learning on first-year college students' achievement in calculus

NASA Astrophysics Data System (ADS)

Oktaviyanthi, Rina; Herman, Tatang

2016-10-01

In this paper, the effect of two different modes of deliver are proposed. The use of self-paced video learning and conventional learning methods in mathematics are compared. The research design classified as a quasi-experiment. The participants were 80 students in the first-year college and divided into two groups. One group as an experiment class received self-paced video learning method and the other group as a control group taught by conventional learning method. Pre and posttest were employed to measure the students' achievement, while questionnaire and interviews were applied to support the pre and posttest data. Statistical analysis included the independent samples t-test showed differences (p < 0.05) in posttest between the experimental and control groups, it means that the use of self-paced video contributed on students' achievement and students' attitudes. In addition, related to corresponding to the students' answer, there are five positive gains in using self-paced video in learning Calculus, such as appropriate learning for both audio and visual of students' characteristics, useful to learn Calculus, assisting students to be more engaging and paying attention in learning, helping students in making the concepts of Calculus are visible, interesting media and motivating students to learn independently.

Triangular model integrating clinical teaching and assessment

PubMed Central

Abdelaziz, Adel; Koshak, Emad

2014-01-01

Structuring clinical teaching is a challenge facing medical education curriculum designers. A variety of instructional methods on different domains of learning are indicated to accommodate different learning styles. Conventional methods of clinical teaching, like training in ambulatory care settings, are prone to the factor of coincidence in having varieties of patient presentations. Accordingly, alternative methods of instruction are indicated to compensate for the deficiencies of these conventional methods. This paper presents an initiative that can be used to design a checklist as a blueprint to guide appropriate selection and implementation of teaching/learning and assessment methods in each of the educational courses and modules based on educational objectives. Three categories of instructional methods were identified, and within each a variety of methods were included. These categories are classroom-type settings, health services-based settings, and community service-based settings. Such categories have framed our triangular model of clinical teaching and assessment. PMID:24624002

Triangular model integrating clinical teaching and assessment.

PubMed

Abdelaziz, Adel; Koshak, Emad

2014-01-01

Structuring clinical teaching is a challenge facing medical education curriculum designers. A variety of instructional methods on different domains of learning are indicated to accommodate different learning styles. Conventional methods of clinical teaching, like training in ambulatory care settings, are prone to the factor of coincidence in having varieties of patient presentations. Accordingly, alternative methods of instruction are indicated to compensate for the deficiencies of these conventional methods. This paper presents an initiative that can be used to design a checklist as a blueprint to guide appropriate selection and implementation of teaching/learning and assessment methods in each of the educational courses and modules based on educational objectives. Three categories of instructional methods were identified, and within each a variety of methods were included. These categories are classroom-type settings, health services-based settings, and community service-based settings. Such categories have framed our triangular model of clinical teaching and assessment.

Person Re-Identification via Distance Metric Learning With Latent Variables.

PubMed

Sun, Chong; Wang, Dong; Lu, Huchuan

2017-01-01

In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.

A literature review about usability evaluation methods for e-learning platforms.

PubMed

Freire, Luciana Lopes; Arezes, Pedro Miguel; Campos, José Creissac

2012-01-01

The usability analysis of information systems has been the target of several research studies over the past thirty years. These studies have highlighted a great diversity of points of view, including researchers from different scientific areas such as Ergonomics, Computer Science, Design and Education. Within the domain of information ergonomics, the study of tools and methods used for usability evaluation dedicated to E-learning presents evidence that there is a continuous and dynamic evolution of E-learning systems, in many different contexts -academics and corporative. These systems, also known as LMS (Learning Management Systems), can be classified according to their educational goals and their technological features. However, in these systems the usability issues are related with the relationship/interactions between user and system in the user's context. This review is a synthesis of research project about Information Ergonomics and embraces three dimensions, namely the methods, models and frameworks that have been applied to evaluate LMS. The study also includes the main usability criteria and heuristics used. The obtained results show a notorious change in the paradigms of usability, with which it will be possible to discuss about the studies carried out by different researchers that were focused on usability ergonomic principles aimed at E-learning.

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

PubMed

Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T

2016-05-01

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.

Can Dialectical Behavior Therapy Be Learned in Highly Structured Learning Environments? Results from a Randomized Controlled Dissemination Trial

ERIC Educational Resources Information Center

Dimeff, Linda A.; Woodcock, Eric A.; Harned, Melanie S.; Beadnell, Blair

2011-01-01

This study evaluated the efficacy of methods of training community mental health providers (N=132) in dialectical behavior therapy (DBT) distress tolerance skills, including (a) Linehan's (1993a) Skills Training Manual for Borderline Personality Disorder (Manual), (b) a multimedia e-Learning course covering the same content (e-DBT), and (c) a…

The Contribution of Teachers' Peer-and Self-Assessment for the Implementation of Active Learning Strategies: Perceptions of Ethiopia Primary School Teachers

ERIC Educational Resources Information Center

Mengistie, Solomon Melesse

2014-01-01

The present study tries to investigate the contribution of primary school teachers' peer- and self- assessment for effective implementation of active learning in their actual classrooms. In this study, areas in which self-reflection and peer assessment include three broad categories, such as methods of teaching and learning, instructional resource…

Arts Integration: A Strategy to Improve Teaching and Learning, Promote Personal Competencies, and Turn Around Low-Performing Schools

ERIC Educational Resources Information Center

Biscoe, Belinda; Wilson, Kirk

2015-01-01

This paper connects the dots between arts integration, students' personal competencies, and school turnaround. Its thesis is that by intertwining art forms and methods with content in all subject areas, students learn more about art and the other subjects and build their personal competencies for learning. The paper includes the story of an…

Neuroscience and Learning: Lessons from Studying the Involvement of a Region of Cerebellar Cortex in Eyeblink Classical Conditioning

ERIC Educational Resources Information Center

Villarreal, Ronald P.; Steinmetz, Joseph E.

2005-01-01

How the nervous system encodes learning and memory processes has interested researchers for 100 years. Over this span of time, a number of basic neuroscience methods has been developed to explore the relationship between learning and the brain, including brain lesion, stimulation, pharmacology, anatomy, imaging, and recording techniques. In this…

The learner's perspective in GP teaching practices with multi-level learners: a qualitative study.

PubMed

Thomson, Jennifer S; Anderson, Katrina; Haesler, Emily; Barnard, Amanda; Glasgow, Nicholas

2014-03-19

Medical students, junior hospital doctors on rotation and general practice (GP) registrars are undertaking their training in clinical general practices in increasing numbers in Australia. Some practices have four levels of learner. This study aimed to explore how multi-level teaching (also called vertical integration of GP education and training) is occurring in clinical general practice and the impact of such teaching on the learner. A qualitative research methodology was used with face-to-face, semi-structured interviews of medical students, junior hospital doctors, GP registrars and GP teachers in eight training practices in the region that taught all levels of learners. Interviews were audio-recorded and transcribed. Qualitative analysis was conducted using thematic analysis techniques aided by the use of the software package N-Vivo 9. Primary themes were identified and categorised by the co-investigators. 52 interviews were completed and analysed. Themes were identified relating to both the practice learning environment and teaching methods used.A practice environment where there is a strong teaching culture, enjoyment of learning, and flexible learning methods, as well as learning spaces and organised teaching arrangements, all contribute to positive learning from a learners' perspective.Learners identified a number of innovative teaching methods and viewed them as positive. These included multi-level learner group tutorials in the practice, being taught by a team of teachers, including GP registrars and other health professionals, and access to a supernumerary GP supervisor (also termed "GP consultant teacher"). Other teaching methods that were viewed positively were parallel consulting, informal learning and rural hospital context integrated learning. Vertical integration of GP education and training generally impacted positively on all levels of learner. This research has provided further evidence about the learning culture, structures and teaching processes that have a positive impact on learners in the clinical general practice setting where there are multiple levels of learners. It has also identified some innovative teaching methods that will need further examination. The findings reinforce the importance of the environment for learning and learner centred approaches and will be important for training organisations developing vertically integrated practices and in their training of GP teachers.

Investigation of blended learning video resources to teach health students clinical skills: An integrative review.

PubMed

Coyne, Elisabeth; Rands, Hazel; Frommolt, Valda; Kain, Victoria; Plugge, Melanie; Mitchell, Marion

2018-04-01

The aim of this review is to inform future educational strategies by synthesising research related to blended learning resources using simulation videos to teach clinical skills for health students. An integrative review methodology was used to allow for the combination of diverse research methods to better understand the research topic. This review was guided by the framework described by Whittemore and Knafl (2005), DATA SOURCES: Systematic search of the following databases was conducted in consultation with a librarian using the following databases: SCOPUS, MEDLINE, COCHRANE, PsycINFO databases. Keywords and MeSH terms: clinical skills, nursing, health, student, blended learning, video, simulation and teaching. Data extracted from the studies included author, year, aims, design, sample, skill taught, outcome measures and findings. After screening the articles, extracting project data and completing summary tables, critical appraisal of the projects was completed using the Mixed Methods Appraisal Tool (MMAT). Ten articles met all the inclusion criteria and were included in this review. The MMAT scores varied from 50% to 100%. Thematic analysis was undertaken and we identified the following three themes: linking theory to practice, autonomy of learning and challenges of developing a blended learning model. Blended learning allowed for different student learning styles, repeated viewing, and enabled links between theory and practice. The video presentation needed to be realistic and culturally appropriate and this required both time and resources to create. A blended learning model, which incorporates video-assisted online resources, may be a useful tool to teach clinical skills to students of health including nursing. Blended learning not only increases students' knowledge and skills, but is often preferred by students due to its flexibility. Copyright © 2018 Elsevier Ltd. All rights reserved.

Regularized learning of linear ordered-statistic constant false alarm rate filters (Conference Presentation)

NASA Astrophysics Data System (ADS)

Havens, Timothy C.; Cummings, Ian; Botts, Jonathan; Summers, Jason E.

2017-05-01

The linear ordered statistic (LOS) is a parameterized ordered statistic (OS) that is a weighted average of a rank-ordered sample. LOS operators are useful generalizations of aggregation as they can represent any linear aggregation, from minimum to maximum, including conventional aggregations, such as mean and median. In the fuzzy logic field, these aggregations are called ordered weighted averages (OWAs). Here, we present a method for learning LOS operators from training data, viz., data for which you know the output of the desired LOS. We then extend the learning process with regularization, such that a lower complexity or sparse LOS can be learned. Hence, we discuss what 'lower complexity' means in this context and how to represent that in the optimization procedure. Finally, we apply our learning methods to the well-known constant-false-alarm-rate (CFAR) detection problem, specifically for the case of background levels modeled by long-tailed distributions, such as the K-distribution. These backgrounds arise in several pertinent imaging problems, including the modeling of clutter in synthetic aperture radar and sonar (SAR and SAS) and in wireless communications.

Status of knowledge on student-learning environments in nursing homes: A mixed-method systematic review.

PubMed

Husebø, Anne Marie Lunde; Storm, Marianne; Våga, Bodil Bø; Rosenberg, Adriana; Akerjordet, Kristin

2018-04-01

To give an overview of empirical studies investigating nursing homes as a learning environment during nursing students' clinical practice. A supportive clinical learning environment is crucial to students' learning and for their development into reflective and capable practitioners. Nursing students' experience with clinical practice can be decisive in future workplace choices. A competent workforce is needed for the future care of older people. Opportunities for maximum learning among nursing students during clinical practice studies in nursing homes should therefore be explored. Mixed-method systematic review using PRISMA guidelines, on learning environments in nursing homes, published in English between 2005-2015. Search of CINAHL with Full Text, Academic Search Premier, MEDLINE and SocINDEX with Full Text, in combination with journal hand searches. Three hundred and thirty-six titles were identified. Twenty studies met the review inclusion criteria. Assessment of methodological quality was based on the Mixed Methods Appraisal Tool. Data were extracted and synthesised using a data analysis method for integrative reviews. Twenty articles were included. The majority of the studies showed moderately high methodological quality. Four main themes emerged from data synthesis: "Student characteristic and earlier experience"; "Nursing home ward environment"; "Quality of mentoring relationship and learning methods"; and "Students' achieved nursing competencies." Nursing home learning environments may be optimised by a well-prepared academic-clinical partnership, supervision by encouraging mentors and high-quality nursing care of older people. Positive learning experiences may increase students' professional development through achievement of basic nursing skills and competencies and motivate them to choose the nursing home as their future workplace. An optimal learning environment can be ensured by thorough preplacement preparations in academia and in nursing home wards, continuous supervision and facilitation of team learning. © 2018 John Wiley & Sons Ltd.

Matrix Treatment of Ray Optics.

ERIC Educational Resources Information Center

Quon, W. Steve

1996-01-01

Describes a method to combine two learning experiences--optical physics and matrix mathematics--in a straightforward laboratory experiment that allows engineering/physics students to integrate a variety of learning insights and technical skills, including using lasers, studying refraction through thin lenses, applying concepts of matrix…

Information Technology and Academic Productivity.

ERIC Educational Resources Information Center

Massy, William F.; Zemsky, Robert

1996-01-01

Enumerates the challenges of adopting information technology (IT)-based teaching and learning strategies in higher education. Concerns addressed include whether IT should supplant rather than augment traditional teaching methods, the financing of IT acquisition, change of teaching and learning processes to increase productivity per person, and…

Analysis of critical thinking ability of VII grade students based on the mathematical anxiety level through learning cycle 7E model

NASA Astrophysics Data System (ADS)

Widyaningsih, E.; Waluya, S. B.; Kurniasih, A. W.

2018-03-01

This study aims to know mastery learning of students’ critical thinking ability with learning cycle 7E, determine whether the critical thinking ability of the students with learning cycle 7E is better than students’ critical thinking ability with expository model, and describe the students’ critical thinking phases based on the mathematical anxiety level. The method is mixed method with concurrent embedded. The population is VII grade students of SMP Negeri 3 Kebumen academic year 2016/2017. Subjects are determined by purposive sampling, selected two students from each level of mathematical anxiety. Data collection techniques include test, questionnaire, interview, and documentation. Quantitative data analysis techniques include mean test, proportion test, difference test of two means, difference test of two proportions and for qualitative data used Miles and Huberman model. The results show that: (1) students’ critical thinking ability with learning cycle 7E achieve mastery learning; (2) students’ critical thinking ability with learning cycle 7E is better than students’ critical thinking ability with expository model; (3) description of students’ critical thinking phases based on the mathematical anxiety level that is the lower the mathematical anxiety level, the subjects have been able to fulfil all of the indicators of clarification, assessment, inference, and strategies phases.

Single image super-resolution based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

2018-03-01

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

Evaluation of medical students of teacher-based and student-based teaching methods in Infectious diseases course.

PubMed

Ghasemzadeh, I; Aghamolaei, T; Hosseini-Parandar, F

2015-01-01

Introduction: In recent years, medical education has changed dramatically and many medical schools in the world have been trying for expand modern training methods. Purpose of the research is to appraise the medical students of teacher-based and student-based teaching methods in Infectious diseases course, in the Medical School of Hormozgan Medical Sciences University. Methods: In this interventional study, a total of 52 medical scholars that used Section in this Infectious diseases course were included. About 50% of this course was presented by a teacher-based teaching method (lecture) and 50% by a student-based teaching method (problem-based learning). The satisfaction of students regarding these methods was assessed by a questionnaire and a test was used to measure their learning. information are examined with using SPSS 19 and paired t-test. Results: The satisfaction of students of student-based teaching method (problem-based learning) was more positive than their satisfaction of teacher-based teaching method (lecture).The mean score of students in teacher-based teaching method was 12.03 (SD=4.08) and in the student-based teaching method it was 15.50 (SD=4.26) and where is a considerable variation among them (p<0.001). Conclusion: The use of the student-based teaching method (problem-based learning) in comparison with the teacher-based teaching method (lecture) to present the Infectious diseases course led to the student satisfaction and provided additional learning opportunities.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

PubMed Central

Goldstein, Benjamin A.; Navar, Ann Marie; Carter, Rickey E.

2017-01-01

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. PMID:27436868

Curriculum renewal in child psychiatry.

PubMed

Hanson, M; Tiberius, R; Charach, A; Ulzen, T; Sackin, D; Jain, U; Reiter, S; Shomair, G

1999-11-01

To ensure uniform design and evaluation of a clerkship curriculum for child and adolescent psychiatry teaching common disorders and problems in an efficient manner across 5 teaching sites and to include structures for continuous improvement. The curriculum committee selected for course inclusion disorders and problems of child psychiatry that were commonly encountered by primary care physicians. Instruction methods that encouraged active student learning were selected. Course coordination across sites was encouraged by several methods: involving faculty, adopting a centralized examination format, and aligning teaching methods with examination format. Quantitative and qualitative methods were used to measure students' perceptions of the course's value. These evaluative results were reviewed, and course modifications were implemented and reevaluated. The average adjusted student return rate for course evaluation questionnaires for the 3-year study period was 63%. Clerks' ratings of course learning value demonstrated that the course improved significantly and continually across all sites, according to a Scheffé post-hoc analysis. Analysis of student statements from focus-group transcripts contributed to course modifications, such as the Brief Focused Interview (BFI). Our curriculum in child psychiatry, which focused on common problems and used active learning methods, was viewed as a valuable learning experience by clinical clerks. Curriculum coordination across multiple teaching sites was accomplished by including faculty in the process and by using specific teaching and examination strategies. Structures for continuous course improvement were effective.

The study of effectiveness of blended learning approach for medical training courses.

PubMed

Karamizadeh, Z; Zarifsanayei, N; Faghihi, A A; Mohammadi, H; Habibi, M

2012-01-01

Blended learning as a method of learning that includes face to face learning, pure E-learning and didactic learning. This study aims to investigate the efficacy of medical education by this approach. This interventional study was performed in 130 students at different clinical levels participating in class sessions on "congenital adrenal hyperplasia and ambiguous genitalia". Sampling was done gradually during 6 months and all of them filled a pretest questionnaire and received an educational compact disk. One week later, a presence class session was held in a question and answer and problem solving method. Two to four weeks later, they filled a posttest questionnaire. There was a significant correlation between pretest and posttest scores and the posttest scores were significantly more than the pretest ones. Sub-specialized residents had the most and the students had the least attitude towards blended learning approach. There was a significant correlation between the research samples' accessibility to computer and their attitude and satisfaction to blended learning approach. Findings generally showed that the blended learning was an effective approach in making a profound learning of academic subjects.

Limitations in learning: How treatment verifications fail and what to do about it?

PubMed

Richardson, Susan; Thomadsen, Bruce

The purposes of this study were: to provide dialog on why classic incident learning systems have been insufficient for patient safety improvements, discuss failures in treatment verification, and to provide context to the reasons and lessons that can be learned from these failures. Historically, incident learning in brachytherapy is performed via database mining which might include reading of event reports and incidents followed by incorporating verification procedures to prevent similar incidents. A description of both classic event reporting databases and current incident learning and reporting systems is given. Real examples of treatment failures based on firsthand knowledge are presented to evaluate the effectiveness of verification. These failures will be described and analyzed by outlining potential pitfalls and problems based on firsthand knowledge. Databases and incident learning systems can be limited in value and fail to provide enough detail for physicists seeking process improvement. Four examples of treatment verification failures experienced firsthand by experienced brachytherapy physicists are described. These include both underverification and oververification of various treatment processes. Database mining is an insufficient method to affect substantial improvements in the practice of brachytherapy. New incident learning systems are still immature and being tested. Instead, a new method of shared learning and implementation of changes must be created. Copyright © 2017 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Interprofessional online learning for primary healthcare: findings from a scoping review

PubMed Central

Reeves, Scott; Fletcher, Simon; McLoughlin, Clodagh; Yim, Alastair; Patel, Kunal D

2017-01-01

Objectives This article presents the findings from a scoping review which explored the nature of interprofessional online learning in primary healthcare. The review was informed by the following questions: What is the nature of evidence on online postgraduate education for primary healthcare interprofessional teams? What learning approaches and study methods are used in this context? What is the range of reported outcomes for primary healthcare learners, their organisations and the care they deliver to patients/clients? Setting The review explored the global literature on interprofessional online learning in primary healthcare settings. Results The review found that the 23 included studies employed a range of different e-learning methods with contrasting course durations, use of theory, participant mix, approaches to accreditation and assessment of learning. Most of the included studies reported outcomes associated with learner reactions and positive changes in participant attitudes/perceptions and improvement in knowledge/skills as a result of engagement in an e-learning course. In contrast, fewer studies reported changes in participant behaviours, changes in organisational practice and improvements to patients/clients. Conclusions A number of educational, methodological and outcome implications are be offered. E-learning can enhance an education experience, support development, ease time constraints, overcome geographic limitations and can offer greater flexibility. However, it can also contribute to the isolation of learners and its benefits can be negated by technical problems. PMID:28780560

The 21st century skills with model eliciting activities on linear program

NASA Astrophysics Data System (ADS)

Handajani, Septriana; Pratiwi, Hasih; Mardiyana

2018-04-01

Human resources in the 21st century are required to master various forms of skills, including critical thinking skills and problem solving. The teaching of the 21st century is a teaching that integrates literacy skills, knowledge, skills, attitudes, and mastery of ICT. This study aims to determine whether there are differences in the effect of applying Model Elliciting Activities (MEAs) that integrates 21st century skills, namely 4C and conventional learning to learning outcomes. This research was conducted at Vocational High School in the odd semester of 2017 and uses the experimental method. The experimental class is treated MEAs that integrates 4C skills and the control class is given conventional learning. Methods of data collection in this study using the method of documentation and test methods. The data analysis uses Z-test. Data obtained from experiment class and control class. The result of this study showed there are differences in the effect of applying MEAs that integrates 4C skills and conventional learning to learning outcomes. Classes with MEAs that integrates 4C skills give better learning outcomes than the ones in conventional learning classes. This happens because MEAs that integrates 4C skills can improved creativity skills, communication skills, collaboration skills, and problem-solving skills.

Offline eLearning for undergraduates in health professions: A systematic review of the impact on knowledge, skills, attitudes and satisfaction

PubMed Central

Rasmussen, Kristine; Belisario, José Marcano; Wark, Petra A; Molina, Joseph Antonio; Loong, Stewart Lee; Cotic, Ziva; Papachristou, Nikos; Riboli–Sasco, Eva; Car, Lorainne Tudor; Musulanov, Eve Marie; Kunz, Holger; Zhang, Yanfeng; George, Pradeep Paul; Heng, Bee Hoon; Wheeler, Erica Lynette; Al Shorbaji, Najeeb; Svab, Igor; Atun, Rifat; Majeed, Azeem; Car, Josip

2014-01-01

Background The world is short of 7.2 million health–care workers and this figure is growing. The shortage of teachers is even greater, which limits traditional education modes. eLearning may help overcome this training need. Offline eLearning is useful in remote and resource–limited settings with poor internet access. To inform investments in offline eLearning, we need to establish its effectiveness in terms of gaining knowledge and skills, students’ satisfaction and attitudes towards eLearning. Methods We conducted a systematic review of offline eLearning for students enrolled in undergraduate, health–related university degrees. We included randomised controlled trials that compared offline eLearning to traditional learning or an alternative eLearning method. We searched the major bibliographic databases in August 2013 to identify articles that focused primarily on students’ knowledge, skills, satisfaction and attitudes toward eLearning, and health economic information and adverse effects as secondary outcomes. We also searched reference lists of relevant studies. Two reviewers independently extracted data from the included studies. We synthesized the findings using a thematic summary approach. Findings Forty–nine studies, including 4955 students enrolled in undergraduate medical, dentistry, nursing, psychology, or physical therapy studies, met the inclusion criteria. Eleven of the 33 studies testing knowledge gains found significantly higher gains in the eLearning intervention groups compared to traditional learning, whereas 21 did not detect significant differences or found mixed results. One study did not test for differences. Eight studies detected significantly higher skill gains in the eLearning intervention groups, whilst the other 5 testing skill gains did not detect differences between groups. No study found offline eLearning as inferior. Generally no differences in attitudes or preference of eLearning over traditional learning were observed. No clear trends were found in the comparison of different modes of eLearning. Most of the studies were small and subject to several biases. Conclusions Our results suggest that offline eLearning is equivalent and possibly superior to traditional learning regarding knowledge, skills, attitudes and satisfaction. Although a robust conclusion cannot be drawn due to variable quality of the evidence, these results justify further investment into offline eLearning to address the global health care workforce shortage. PMID:24976964

Effects of problem-based learning in Chinese radiology education

PubMed Central

Zhang, Song; Xu, Jiancheng; Wang, Hongwei; Zhang, Dong; Zhang, Qichuan; Zou, Liguang

2018-01-01

Abstract Background: In recent years, the problem-based learning (PBL) teaching method has been extensively applied as an experimental educational method in Chinese radiology education. However, the results of individual studies were inconsistent and inconclusive. A meta-analysis was performed to evaluate the effects of PBL on radiology education in China. Methods: Databases of Chinese and English languages were searched from inception up to November 2017. The standard mean difference (SMD) with its 95% confidence interval (95% CI) was used to determine the over effects of PBL compared with the traditional teaching method. Results: Seventeen studies involving 1487 participants were included in this meta-analysis. Of them, 16 studies provided sufficient data for the pooled analysis and showed that PBL teaching method had a positive effect on achieving higher theoretical scores compared with the traditional teaching method (SMD = 1.20, 95% CI [0.68, 1.71]). Thirteen studies provided sufficient data on skill scores, and a significant difference in favor of PBL was also observed (SMD = 2.10, 95% CI [1.38, 2.83]). Questionnaire surveys were applied in most of the included studies and indicated positive effects of PBL on students’ learning interest, scope of knowledge, team spirit, and oral expression. Conclusion: The result shows that PBL appears to be more effective on radiology education than traditional teaching method in China. However, the heterogeneity of the included studies cannot be neglected. Further well-designed studies about this topic are needed to confirm the above findings. PMID:29489669

Effects of e-learning, lectures, and role playing on nursing students' knowledge acquisition, retention and satisfaction.

PubMed

Pourghaznein, Tayebeh; Sabeghi, Hakimeh; Shariatinejad, Keyvan

2015-01-01

Nursing education can maintain its dynamic quality when it moves toward innovation and modern methods of teaching and learning. Therefore, teachers are required to employ up to date methods in their teaching plans. This study evaluated the effects of e-learning, lectures, and role playing on nursing students' learning, retention, and satisfaction. Sixty nursing students were selected as an experiment and control groups during two consecutive semesters. The educational content was presented as e-learning and role playing during one semester (experiment group) and as lectures in the next semester (control group). A questionnaire containing three parts was used to assess demographics, learning and satisfaction statuses. The questionnaire also included a final openended question to evaluate the students' ideas about the whole course. The mean scores of posttest were 16.13 ± 1.37 using role playing, 15.50 ± 1.44 using e-learning and 16.45 ± 1.23 using lectures. The differences between the mean scores of posttest and pretest were 12.84 ± 1.43, 12.56 ± 1.57, and 13.73 ± 1.53 in the mentioned methods, respectively. Lectures resulted in significantly better learning compared to role playing and e-learning. In contrast, retention rates were significantly lower using lectures than using role playing and e-learning. Students' satisfaction from e-learning was significantly lower than lecturing and role playing. Due to the lower rates of retention following lectures, the teachers are recommended to use student- centered approaches in their lectures. Since students' satisfaction with e-learning was lower than the other methods, further studies are suggested to explore the problems of e-learning in Iran.

Online faculty development for creating E-learning materials.

PubMed

Niebuhr, Virginia; Niebuhr, Bruce; Trumble, Julie; Urbani, Mary Jo

2014-01-01

Faculty who want to develop e-learning materials face pedagogical challenges of transforming instruction for the online environment, especially as many have never experienced online learning themselves. They face technical challenges of learning new software and time challenges of not all being able to be in the same place at the same time to learn these new skills. The objective of the Any Day Any Place Teaching (ADAPT) faculty development program was to create an online experience in which faculty could learn to produce e-learning materials. The ADAPT curriculum included units on instructional design, copyright principles and peer review, all for the online environment, and units on specific software tools. Participants experienced asynchronous and synchronous methods, including a learning management system, PC-based videoconferencing, online discussions, desktop sharing, an online toolbox and optional face-to-face labs. Project outcomes were e-learning materials developed and participants' evaluations of the experience. Likert scale responses for five instructional units (quantitative) were analyzed for distance from neutral using one-sample t-tests. Interview data (qualitative) were analyzed with assurance of data trustworthiness and thematic analysis techniques. Participants were 27 interprofessional faculty. They evaluated the program instruction as easy to access, engaging and logically presented. They reported increased confidence in new skills and increased awareness of copyright issues, yet continued to have time management challenges and remained uncomfortable about peer review. They produced 22 new instructional materials. Online faculty development methods are helpful for faculty learning to create e-learning materials. Recommendations are made to increase the success of such a faculty development program.

The Surgical Learning and Instructional Portfolio: What Residents at a Single Institution Are Learning

PubMed Central

Webb, Travis P; Merkley, Taylor R

2011-01-01

Background The Accreditation Council for Graduate Medical Education (ACGME) Learning Portfolio is recommended as a tool to develop and document reflective, practice-based learning and improvement. There is no consensus regarding the appropriate content of a learning portfolio in medical education. Studying lessons selected for inclusion in their learning portfolios by surgical trainees could help identify useful subject matter for this purpose. Methods Each month, all residents in our surgery residency program submit entries into their individual Surgical Learning and Instructional Portfolio (SLIP). The SLIP entries from July 2008 to 2009 (n = 420) were deidentified and randomized using a random number generator. We conducted a thematic content analysis of 50 random portfolio entries to identify lessons learned. Two independent raters analyzed the “3 lessons learned” portion of the portfolio entries and identified themes and subthemes using the constant comparative method used in grounded theory. Results The collaborative coding process resulted in theme saturation after the identification of 7 themes and their subthemes. Themes in decreasing order of frequency included complications, disease epidemiology, disease presentation, surgical management of disease, medical management of disease, operative techniques, and pathophysiology. Junior residents chose to focus on a broad array of foundational topics including disease presentation, epidemiology, and overall management of diseases, whereas postgraduate year-4 (PGY-4) and PGY-5 residents most frequently chose to focus on complications as learning points. Conclusions Lessons learned reflect perceived needs of the trainees based on training year. When given a template to follow, junior and senior residents choose to reflect on different subject matter to meet their learning goals. PMID:22379531

Using Corporate-Based Methods To Assess Technical Communication Programs.

ERIC Educational Resources Information Center

Faber, Brenton; Bekins, Linn; Karis, Bill

2002-01-01

Investigates methods of program assessment used by corporate learning sites and profiles value added methods as a way to both construct and evaluate academic programs in technical communication. Examines and critiques assessment methods from corporate training environments including methods employed by corporate universities and value added…

A Belgian Approach to Learning Disabilities.

ERIC Educational Resources Information Center

Hayes, Cheryl W.

The paper reviews Belgian philosophy toward the education of learning disabled students and cites the differences between American behaviorally-oriented theory and Belgian emphasis on identifying the underlying causes of the disability. Academic methods observed in Belgium (including psychodrama and perceptual motor training) are discussed and are…

Carry On-with Excellence.

ERIC Educational Resources Information Center

Thompson, Helen

2002-01-01

Discussion of the expanded role of teacher for library media specialists focuses on characteristics of teaching excellence based on a story about the life of Nathaniel Bowditch. Highlights include lifelong learning; caring about learners; believing everyone can learn; reflecting on teaching methods; understanding human nature; and contributing to…

Biography: Learning To Do, Teaching To Do.

ERIC Educational Resources Information Center

Smith, Louis M.

This symposium paper reports on developing biographical qualitative research methods, mixing personal narrative and conceptualization. The section on "Learning To Do" begins with an autobiographical report on a study of an urban classroom during which the author developed his early techniques for qualitative research, including the…

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

PubMed

Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

2018-03-30

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

Measuring strategic control in implicit learning: how and why?

PubMed

Norman, Elisabeth

2015-01-01

Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge are consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodological and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures.

Measuring strategic control in implicit learning: how and why?

PubMed Central

Norman, Elisabeth

2015-01-01

Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge are consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodological and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures. PMID:26441809

A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning.

PubMed

Suemitsu, Atsuo; Dang, Jianwu; Ito, Takayuki; Tiede, Mark

2015-10-01

Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /æ/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning.

Impact of e-learning on nurses' and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis.

PubMed

Lahti, Mari; Hätönen, Heli; Välimäki, Maritta

2014-01-01

To review the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction related to e-learning. We conducted a systematic review and meta-analysis of randomized controlled trials (RCT) to assess the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction. Electronic databases including MEDLINE (1948-2010), CINAHL (1981-2010), Psychinfo (1967-2010) and Eric (1966-2010) were searched in May 2010 and again in December 2010. All RCT studies evaluating the effectiveness of e-learning and differentiating between traditional learning methods among nurses were included. Data was extracted related to the purpose of the trial, sample, measurements used, index test results and reference standard. An extraction tool developed for Cochrane reviews was used. Methodological quality of eligible trials was assessed. 11 trials were eligible for inclusion in the analysis. We identified 11 randomized controlled trials including a total of 2491 nurses and student nurses'. First, the random effect size for four studies showed some improvement associated with e-learning compared to traditional techniques on knowledge. However, the difference was not statistically significant (p=0.39, MD 0.44, 95% CI -0.57 to 1.46). Second, one study reported a slight impact on e-learning on skills, but the difference was not statistically significant, either (p=0.13, MD 0.03, 95% CI -0.09 to 0.69). And third, no results on nurses or student nurses' satisfaction could be reported as the statistical data from three possible studies were not available. Overall, there was no statistical difference between groups in e-learning and traditional learning relating to nurses' or student nurses' knowledge, skills and satisfaction. E-learning can, however, offer an alternative method of education. In future, more studies following the CONSORT and QUOROM statements are needed to evaluate the effects of these interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology.

PubMed

Min, Hua; Mobahi, Hedyeh; Irvin, Katherine; Avramovic, Sanja; Wojtusiak, Janusz

2017-09-16

Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient's cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient's race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P < 0.1) than methods without ontologies. This study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.

A literature review of empirical research on learning analytics in medical education

PubMed Central

Saqr, Mohammed

2018-01-01

The number of publications in the field of medical education is still markedly low, despite recognition of the value of the discipline in the medical education literature, and exponential growth of publications in other fields. This necessitates raising awareness of the research methods and potential benefits of learning analytics (LA). The aim of this paper was to offer a methodological systemic review of empirical LA research in the field of medical education and a general overview of the common methods used in the field in general. Search was done in Medline database using the term “LA.” Inclusion criteria included empirical original research articles investigating LA using qualitative, quantitative, or mixed methodologies. Articles were also required to be written in English, published in a scholarly peer-reviewed journal and have a dedicated section for methods and results. A Medline search resulted in only six articles fulfilling the inclusion criteria for this review. Most of the studies collected data about learners from learning management systems or online learning resources. Analysis used mostly quantitative methods including descriptive statistics, correlation tests, and regression models in two studies. Patterns of online behavior and usage of the digital resources as well as predicting achievement was the outcome most studies investigated. Research about LA in the field of medical education is still in infancy, with more questions than answers. The early studies are encouraging and showed that patterns of online learning can be easily revealed as well as predicting students’ performance. PMID:29599699

A literature review of empirical research on learning analytics in medical education.

PubMed

Saqr, Mohammed

2018-01-01

The number of publications in the field of medical education is still markedly low, despite recognition of the value of the discipline in the medical education literature, and exponential growth of publications in other fields. This necessitates raising awareness of the research methods and potential benefits of learning analytics (LA). The aim of this paper was to offer a methodological systemic review of empirical LA research in the field of medical education and a general overview of the common methods used in the field in general. Search was done in Medline database using the term "LA." Inclusion criteria included empirical original research articles investigating LA using qualitative, quantitative, or mixed methodologies. Articles were also required to be written in English, published in a scholarly peer-reviewed journal and have a dedicated section for methods and results. A Medline search resulted in only six articles fulfilling the inclusion criteria for this review. Most of the studies collected data about learners from learning management systems or online learning resources. Analysis used mostly quantitative methods including descriptive statistics, correlation tests, and regression models in two studies. Patterns of online behavior and usage of the digital resources as well as predicting achievement was the outcome most studies investigated. Research about LA in the field of medical education is still in infancy, with more questions than answers. The early studies are encouraging and showed that patterns of online learning can be easily revealed as well as predicting students' performance.

Teaching and learning recursive programming: a review of the research literature

NASA Astrophysics Data System (ADS)

McCauley, Renée; Grissom, Scott; Fitzgerald, Sue; Murphy, Laurie

2015-01-01

Hundreds of articles have been published on the topics of teaching and learning recursion, yet fewer than 50 of them have published research results. This article surveys the computing education research literature and presents findings on challenges students encounter in learning recursion, mental models students develop as they learn recursion, and best practices in introducing recursion. Effective strategies for introducing the topic include using different contexts such as recurrence relations, programming examples, fractal images, and a description of how recursive methods are processed using a call stack. Several studies compared the efficacy of introducing iteration before recursion and vice versa. The paper concludes with suggestions for future research into how students learn and understand recursion, including a look at the possible impact of instructor attitude and newer pedagogies.

Improvement in Generic Problem-Solving Abilities of Students by Use of Tutor-Less Problem-Based Learning in a Large Classroom Setting

ERIC Educational Resources Information Center

Klegeris, Andis; Bahniwal, Manpreet; Hurren, Heather

2013-01-01

Problem-based learning (PBL) was originally introduced in medical education programs as a form of small-group learning, but its use has now spread to large undergraduate classrooms in various other disciplines. Introduction of new teaching techniques, including PBL-based methods, needs to be justified by demonstrating the benefits of such…

The utility of adaptive eLearning in cervical cytopathology education.

PubMed

Samulski, T Danielle; Taylor, Laura A; La, Teresa; Mehr, Chelsea R; McGrath, Cindy M; Wu, Roseann I

2018-02-01

Adaptive eLearning allows students to experience a self-paced, individualized curriculum based on prior knowledge and learning ability. The authors investigated the effectiveness of adaptive online modules in teaching cervical cytopathology. eLearning modules were created that covered basic concepts in cervical cytopathology, including artifacts and infections, squamous lesions (SL), and glandular lesions (GL). The modules used student responses to individualize the educational curriculum and provide real-time feedback. Pathology trainees and faculty from the authors' institution were randomized into 2 groups (SL or GL), and identical pre-tests and post-tests were used to compare the efficacy of eLearning modules versus traditional study methods (textbooks and slide sets). User experience was assessed with a Likert scale and free-text responses. Sixteen of 17 participants completed the SL module, and 19 of 19 completed the GL module. Participants in both groups had improved post-test scores for content in the adaptive eLearning module. Users indicated that the module was effective in presenting content and concepts (Likert scale [from 1 to 5], 4.3 of 5.0), was an efficient and convenient way to review the material (Likert scale, 4.4 of 5.0), and was more engaging than lectures and texts (Likert scale, 4.6 of 5.0). Users favored the immediate feedback and interactivity of the module. Limitations included the inability to review prior content and slow upload time for images. Learners demonstrated improvement in their knowledge after the use of adaptive eLearning modules compared with traditional methods. Overall, the modules were viewed positively by participants. Adaptive eLearning modules can provide an engaging and effective adjunct to traditional teaching methods in cervical cytopathology. Cancer Cytopathol 2018;126:129-35. © 2017 American Cancer Society. © 2017 American Cancer Society.

A study of active learning methods for named entity recognition in clinical text.

PubMed

Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

2015-12-01

Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random sampling, the best uncertainty based method saved 42% annotations in words. But the best diversity based method reduced only 7% annotation effort. In the simulated setting, AL methods, particularly uncertainty-sampling based approaches, seemed to significantly save annotation cost for the clinical NER task. The actual benefit of active learning in clinical NER should be further evaluated in a real-time setting. Copyright © 2015 Elsevier Inc. All rights reserved.

Increasing health worker capacity through distance learning: a comprehensive review of programmes in Tanzania

PubMed Central

2010-01-01

Background Tanzania, like many developing countries, faces a crisis in human resources for health. The government has looked for ways to increase the number and skills of health workers, including using distance learning in their training. In 2008, the authors reviewed and assessed the country's current distance learning programmes for health care workers, as well as those in countries with similar human resource challenges, to determine the feasibility of distance learning to meet the need of an increased and more skilled health workforce. Methods Data were collected from 25 distance learning programmes at health training institutions, universities, and non-governmental organizations throughout the country from May to August 2008. Methods included internet research; desk review; telephone, email and mail-in surveys; on-site observations; interviews with programme managers, instructors, students, information technology specialists, preceptors, health care workers and Ministry of Health and Social Welfare representatives; and a focus group with national HIV/AIDS care and treatment organizations. Results Challenges include lack of guidelines for administrators, instructors and preceptors of distance learning programmes regarding roles and responsibilities; absence of competencies for clinical components of curricula; and technological constraints such as lack of access to computers and to the internet. Insufficient funding resulted in personnel shortages, lack of appropriate training for personnel, and lack of materials for students. Nonetheless, current and prospective students expressed overwhelming enthusiasm for scale-up of distance learning because of the unique financial and social benefits offered by these programs. Participants were retained as employees in their health care facilities, and remained in their communities and supported their families while advancing their careers. Space in health training institutions was freed up for new students entering in-residence pre-service training. Conclusions A blended print-based distance learning model is most feasible at the national level due to current resource and infrastructure constraints. With an increase in staffing; improvement of infrastructure, coordination and curricula; and decentralization to the zonal or district level, distance learning can be an effective method to increase both the skills and the numbers of qualified health care workers capable of meeting the health care needs of the Tanzanian population. PMID:21194417

Joint seismic data denoising and interpolation with double-sparsity dictionary learning

NASA Astrophysics Data System (ADS)

Zhu, Lingchen; Liu, Entao; McClellan, James H.

2017-08-01

Seismic data quality is vital to geophysical applications, so that methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double-sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data, while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the data set without introducing pseudo-Gibbs artifacts when compared to other directional multi-scale transform methods such as curvelets.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Cancer Detection and Diagnosis Methods - Annual Plan

Cancer.gov

Early cancer detection is a proven life-saving strategy. Learn about the research opportunities NCI supports, including liquid biopsies and other less-invasive methods, for detecting early cancers and precancerous growths.

The effectiveness of nurses' ability to interpret basic electrocardiogram strips accurately using different learning modalities.

PubMed

Spiva, LeeAnna; Johnson, Kimberly; Robertson, Bethany; Barrett, Darcy T; Jarrell, Nicole M; Hunter, Donna; Mendoza, Inocencia

2012-02-01

Historically, the instructional method of choice has been traditional lecture or face-to-face education; however, changes in the health care environment, including resource constraints, have necessitated examination of this practice. A descriptive pre-/posttest method was used to determine the effectiveness of alternative teaching modalities on nurses' knowledge and confidence in electrocardiogram (EKG) interpretation. A convenience sample of 135 nurses was recruited in an integrated health care system in the Southeastern United States. Nurses attended an instructor-led course, an online learning (e-learning) platform with no study time or 1 week of study time, or an e-learning platform coupled with a 2-hour post-course instructor-facilitated debriefing with no study time or 1 week of study time. Instruments included a confidence scale, an online EKG test, and a course evaluation. Statistically significant differences in knowledge and confidence were found for individual groups after nurses participated in the intervention. Statistically significant differences were found in pre-knowledge and post-confidence when groups were compared. Organizations that use various instructional methods to educate nurses in EKG interpretation can use different teaching modalities without negatively affecting nurses' knowledge or confidence in this skill. Copyright 2012, SLACK Incorporated.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

The Impact of Team-Based Learning on Nervous System Examination Knowledge of Nursing Students.

PubMed

Hemmati Maslakpak, Masomeh; Parizad, Naser; Zareie, Farzad

2015-12-01

Team-based learning is one of the active learning approaches in which independent learning is combined with small group discussion in the class. This study aimed to determine the impact of team-based learning in nervous system examination knowledge of nursing students. This quasi-experimental study was conducted on 3(rd) grade nursing students, including 5th semester (intervention group) and 6(th) semester (control group). The traditional lecture method and the team-based learning method were used for educating the examination of the nervous system for intervention and control groups, respectively. The data were collected by a test covering 40-questions (multiple choice, matching, gap-filling and descriptive questions) before and after intervention in both groups. Individual Readiness Assurance Test (RAT) and Group Readiness Assurance Test (GRAT) used to collect data in the intervention group. In the end, the collected data were analyzed by SPSS ver. 13 using descriptive and inferential statistical tests. In team-based learning group, mean and standard deviation was 13.39 (4.52) before the intervention, which had been increased to 31.07 (3.20) after the intervention and this increase was statistically significant. Also, there was a statistically significant difference between the scores of RAT and GRAT in team-based learning group. Using team-based learning approach resulted in much better improvement and stability in the nervous system examination knowledge of nursing students compared to traditional lecture method; therefore, this method could be efficiently used as an effective educational approach in nursing education.

Recent Empirical Studies of the Pedagogical Effects of Interactive Video Instruction in "Soft Skill" Areas.

ERIC Educational Resources Information Center

Cronin, Michael W.; Cronin, Karen A.

1992-01-01

Recent empirical research has identified significant advantages for interactive video instruction over traditional teaching methods in "soft skill" (humanities and social sciences) areas, including cognitive achievement, transfer of learning to performance, learning motivation, student achievement across uncontrolled student characteristics, user…

Educating for Wisdom and Compassion: Creating Conditions for Timeless Learning

ERIC Educational Resources Information Center

Miller, John P.

2005-01-01

Blending philosophy, research, and three decades of practice, the author offers an engaging discussion of essential principles of timeless learning, including attention, contemplation, connection, participation, responsibility, wholeness, and joy. Educators in today's schools can apply these principles, models, and methods to inform instruction in…

Adult Education Research Conference Proceedings (28th, Laramie, Wyoming, May 21-23, 1987).

ERIC Educational Resources Information Center

Inkster, Robert P., Comp.

Selected authors and titles include: "Radical Adult Education" (Armstrong); "Learning Liberation: A Comparative Analysis of Feminist Consciousness Raising and Freire's Conscientization Method" (Butterwick); "The Aging Worker's Potential and Training Practices" (Chene); "The Interaction of Teaching Style and Learning Style on Traditional and…

Transformative Learning Experiences of International Graduate Students from Asian Countries

ERIC Educational Resources Information Center

Kumi-Yeboah, Alex; James, Waynne

2014-01-01

This article investigates the transformative learning experiences of international graduate students from Asian countries. Data collection consisted of quantitative and qualitative methods. Participants included international graduate students from Asia, in the Colleges of Arts and Sciences and Engineering. Overall, 82.3% of the participants…

Psychosocial Functioning of Young Children with Learning Problems

ERIC Educational Resources Information Center

Gadeyne, Els; Ghesquiere, Pol; Onghena, Patrick

2004-01-01

Background: In this study, psychosocial functioning of different groups of young children with learning problems was investigated using a diverse set of psychosocial variables (including behaviour problems, academic motivation, social preference, and self-concept). Methods: For this purpose, children with low academic achievement, with a specific…

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.

Web-based learning: pros, cons and controversies.

PubMed

Cook, David A

2007-01-01

Advantages of web-based learning (WBL) in medical education include overcoming barriers of distance and time, economies of scale, and novel instructional methods, while disadvantages include social isolation, up-front costs, and technical problems. Web-based learning is purported to facilitate individualised instruction, but this is currently more vision than reality. More importantly, many WBL instructional designs fail to incorporate principles of effective learning, and WBL is often used for the wrong reasons (e.g., for the sake of technology). Rather than trying to decide whether WBL is superior to or equivalent to other instructional media (research addressing this question will always be confounded), we should accept it as a potentially powerful instructional tool, and focus on learning when and how to use it. Educators should recognise that high fidelity, multimedia, simulations, and even WBL itself will not always be necessary to effectively facilitate learning.

Conventional vs. e-learning in nursing education: A systematic review and meta-analysis.

PubMed

Voutilainen, Ari; Saaranen, Terhi; Sormunen, Marjorita

2017-03-01

By and large, in health professions training, the direction of the effect of e-learning, positive or negative, strongly depends on the learning outcome in question as well as on learning methods which e-learning is compared to. In nursing education, meta-analytically generated knowledge regarding the comparisons between conventional and e-learning is scarce. The aim of this review is to discover the size of the effect of e-learning on learning outcomes in nursing education and to assess the quality of studies in which e-learning has been compared to conventional learning. A systematic search of six electronic databases, PubMed, Ovid MEDLINE®, CINAHL (EBSCOhost), Cochrane Library, PsycINFO, and ERIC, was conducted in order to identify relevant peer-reviewed English language articles published between 2011 and 2015. The quality of the studies included as well as the risk of bias in each study was assessed. A random-effects meta-analysis was performed to generate a pooled mean difference in the learning outcome. Altogether, 10 studies were eligible for the quality assessment and meta-analysis. Nine studies were evaluated as good quality studies, but not without a risk of bias. Performance bias caused a high risk in nearly all the studies. In the meta-analysis, an e-learning method resulted in test scores that were, on average, five points higher than a conventional method on a 0-100 scale. Heterogeneity between the studies was very large. The size and direction of the effect of a learning method on learning outcomes appeared to be strongly situational. We suggest that meta-regressions should be performed instead of basic meta-analyses in order to reveal factors that cause variation in the learning outcomes of nursing education. It might be necessary to perform separate meta-analyses between e-learning interventions aimed at improving nursing knowledge and those aimed at improving nursing skills. Copyright © 2016 Elsevier Ltd. All rights reserved.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro

2017-07-01

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

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.

Vaccination learning experiences of nursing students: a grounded theory study.

PubMed

Ildarabadi, Eshagh; Karimi Moonaghi, Hossein; Heydari, Abbas; Taghipour, Ali; Abdollahimohammad, Abdolghani

2015-01-01

This study aimed to explore the experiences of nursing students being trained to perform vaccinations. The grounded theory method was applied to gather information through semi-structured interviews. The participants included 14 undergraduate nursing students in their fifth and eighth semesters of study in a nursing school in Iran. The information was analyzed according to Strauss and Corbin's method of grounded theory. A core category of experiential learning was identified, and the following eight subcategories were extracted: students' enthusiasm, vaccination sensitivity, stress, proper educational environment, absence of prerequisites, students' responsibility for learning, providing services, and learning outcomes. The vaccination training of nursing students was found to be in an acceptable state. However, some barriers to effective learning were identified. As such, the results of this study may provide empirical support for attempts to reform vaccination education by removing these barriers.

STEM learning on electricity using arduino-phet based experiment to improve 8th grade students’ STEM literacy

NASA Astrophysics Data System (ADS)

Prima, E. C.; Oktaviani, T. D.; Sholihin, H.

2018-05-01

Technology is the application of scientific knowledge for practical purposes, especially in industry. One way to support the development of the technology is by integrating the use of technology and build the technology with the learning process in the form of STEM (science, technology, engineering, mathematics) Learning approach. Applying STEM Learning could improve Students’ STEM Literacy. The learning approach is applied in every aspect of Learning including the application of STEM Learning in the lesson plan and worksheet. The method used in this research is weak experimental method. One group class (N=15) is taken and learn using STEM Learning approach. The topic choosen is the electricity topic which is separated into electrical circuit and parameters. The learning process is separated into 3 meetings. 15 Students are given a STEM Literacy test item before and after the lesson. The result of the normalized gain shows there are improvement in students’ STEM Literacy by < \\overline{g}> 0.16 categorieed as low improvement. The most higher improvement is the students’ technology literacy, because students learn using the same technology in every meeting. This factor influences students’ technology literacy so the result is higher than another.

The Learning Preferences among Nursing Students in the King Saud University in Saudi Arabia: A Cross-Sectional Survey

PubMed Central

Almutairi, Adel F.; Alhelih, Eyad M.; Alshehry, Abdualrahman S.

2017-01-01

Objective The present study aimed to identify the most common learning preferences among the nursing students in Saudi Arabia and to investigate the associations of certain demographic variables with the learning preferences. Methods All the undergraduate nursing students in the nursing college were requested to participate in this descriptive cross-sectional study. An Arabic version of the Felder-Silverman learning style model (FSLSM) questionnaire was used to examine the learning preferences among undergraduate nursing students. Results A total of 56 (43%) completed questionnaires were included in the final analysis. Results of the present study indicate that the most common learning preferences among the nursing students were visual (67.9%), followed by active (50%) and sequential (37.5%) learning preferences. The verbal style was the least common learning preference (3.6%) among the nursing students. There was no association between gender and learning preferences (p > .05). Conclusion The present study concluded that the visual, active, and sequential styles are the commonest learning preferences among the nursing students. The nursing educators should emphasize the use of this information in their teaching methods to improve learning skills among the nursing students. PMID:28630767

[Which learning methods are expected for ultrasound training? Blended learning on trial].

PubMed

Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

2014-10-01

Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (< 50%). A total of 176 trainees participated in this survey. Participants preferred an e-learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

Student midwives and paramedic students' experiences of shared learning in pre-hospital childbirth.

PubMed

Feltham, Christina; Foster, Julie; Davidson, Tom; Ralph, Stewart

2016-06-01

To explore the experiences of midwifery and paramedic students undertaking interprofessional learning. A one day interprofessional learning workshop incorporating peer assisted learning for undergraduate pre-registration midwifery and paramedic students was developed based on collaborative practice theory and simulation based learning. Twenty-five student midwives and thirty-one paramedic students participated in one of two identical workshops conducted over separate days. Videoed focus group sessions were held following the workshop sessions in order to obtain qualitative data around student experience. Qualitative data analysis software (ATLAS.ti) was used to collate the transcriptions from the focus group sessions and the video recordings were scrutinised. Thematic analysis was adopted. Four main themes were identified around the understanding of each other's roles and responsibilities, the value of interprofessional learning, organisation and future learning. Students appeared to benefit from a variety of learning opportunities including interprofessional learning and peer assisted learning through the adoption of both formal and informal teaching methods, including simulation based learning. A positive regard for each other's profession including professional practice, professional governing bodies, professional codes and scope of practice was apparent. Students expressed a desire to undertake similar workshops with other professional students. Interprofessional learning workshops were found to be a positive experience for the students involved. Consideration needs to be given to developing interprofessional learning with other student groups aligned with midwifery at appropriate times in relation to stage of education. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transformational Teaching: Theoretical Underpinnings, Basic Principles, and Core Methods

PubMed Central

Slavich, George M.; Zimbardo, Philip G.

2012-01-01

Approaches to classroom instruction have evolved considerably over the past 50 years. This progress has been spurred by the development of several learning principles and methods of instruction, including active learning, student-centered learning, collaborative learning, experiential learning, and problem-based learning. In the present paper, we suggest that these seemingly different strategies share important underlying characteristics and can be viewed as complimentary components of a broader approach to classroom instruction called transformational teaching. Transformational teaching involves creating dynamic relationships between teachers, students, and a shared body of knowledge to promote student learning and personal growth. From this perspective, instructors are intellectual coaches who create teams of students who collaborate with each other and with their teacher to master bodies of information. Teachers assume the traditional role of facilitating students’ acquisition of key course concepts, but do so while enhancing students’ personal development and attitudes toward learning. They accomplish these goals by establishing a shared vision for a course, providing modeling and mastery experiences, challenging and encouraging students, personalizing attention and feedback, creating experiential lessons that transcend the boundaries of the classroom, and promoting ample opportunities for preflection and reflection. We propose that these methods are synergistically related and, when used together, maximize students’ potential for intellectual and personal growth. PMID:23162369

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Discussion on teaching reform of environmental planning and management

NASA Astrophysics Data System (ADS)

Zhang, Qiugen; Chen, Suhua; Xie, Yu; Wei, Li'an; Ding, Yuan

2018-05-01

The curriculum of environmental planning and management is an environmental engineering major curriculum established by the teaching steering committee of environmental science and engineering of Education Ministry, which is the core curriculum of Chinese engineering education professional certification. It plays an important role in cultivating environmental planning and environmental management ability of environmental engineering major. The selection and optimization of the course teaching content of environmental planning and management were discussed which including curriculum teaching content updating and optimizing and teaching resource system construction. The comprehensive application of teaching method was discussed which including teaching method synthesis and teaching method. The final combination of the assessment method was also discussed which including the formative assessment normal grades and the final result of the course examination. Through the curriculum comprehensive teaching reform, students' knowledge had been broadened, the subject status and autonomy of learning had been enhanced, students' learning interest had been motivated, the ability of students' finding, analyzing and solving problems had been improved. Students' innovative ability and positive spirit had been well cultivated.

Introduction to a New Approach to Experiential Learning.

ERIC Educational Resources Information Center

Jackson, Lewis; MacIsaac, Doug

1994-01-01

A process model for experiential learning (EL) in adult education begins with the characteristics and needs of adult learners and conceptual foundations of EL. It includes methods and techniques for in-class and field-based experiences, building a folio (point-in-time performance assessment), and portfolio construction (assessing transitional…

Credit for Learning Gained in Life and Work Experience.

ERIC Educational Resources Information Center

Strange, John

1980-01-01

Prime features of sound college programs that assess for credit the prior experiential learning of adults are outlined. Faculty judgment underlies all evaluation methods, which include oral exams, written reports, actual performance, or appraisals of advanced professional knowledge. Work of the Council for the Advancement of Experiential Learning…

Self-Assessment as a Process for Inclusion

ERIC Educational Resources Information Center

Bourke, Roseanna; Mentis, Mandia

2013-01-01

There are multiple ways that assessment is positioned within education: as a method for accountability, a strategy to attract funding and an approach to support learning. Different assessment practices portray students in different ways and can serve to include or exclude them in their learning and assessment. Students are often categorised and…

Leadership Learning through Student-Centered and Inquiry-Focused Approaches to Teaching Adaptive Leadership

ERIC Educational Resources Information Center

Haber-Curran, Paige; Tillapaugh, Daniel

2013-01-01

This qualitative study examines student learning about leadership across three sections of a capstone course in an undergraduate leadership minor. Qualitative methods were informed by exploratory case study analysis and phenomenology. Student-centered and inquiry-focused pedagogical approaches, including case-in-point, action inquiry, and…

Designing Online Instruction for Postsecondary Students with Learning Disabilities

ERIC Educational Resources Information Center

Simoncelli, Andrew; Hinson, Janice

2010-01-01

This research details the methodologies that could be used to better deliver online course content to students with learning disabilities. Research has shown how the design of the course affects the students' attitudes and performance. This article details the methodology and pedagogical side of the delivery including instructional methods that…

Assessing the Value of the Enviroscape Watershed Learning Module

ERIC Educational Resources Information Center

Edwards, Warren Patrick

2013-01-01

Scope and Method of Study: The researcher's evaluation of the West Atlanta Watershed Alliance's (WAWA) programs highlighted that few if any of the offered educational programs included a program evaluation, especially the most promising, the Enviroscape® Watershed learning module. The education programs that were customized and developed by the…

Calibrated Peer Review for Computer-Assisted Learning of Biological Research Competencies

ERIC Educational Resources Information Center

Clase, Kari L.; Gundlach, Ellen; Pelaez, Nancy J.

2010-01-01

Recently, both science and technology faculty have been recognizing biological research competencies that are valued but rarely assessed. Some of these valued learning outcomes include scientific methods and thinking, critical assessment of primary papers, quantitative reasoning, communication, and putting biological research into a historical and…

Financing Lifelong Learning for All: An International Perspective. Working Paper.

ERIC Educational Resources Information Center

Burke, Gerald

Recent international discussions provide information on various countries' responses to lifelong learning, including the following: (1) existing unmet needs and emerging needs for education and training; (2) funds required compared with what was provided; and (3) methods for acquiring additional funds, among them efficiency measures leading to…

Guiding Children's Reading through Experiences. Second Edition.

ERIC Educational Resources Information Center

Gans, Roma

This discussion of methods, experiences, and theory provides educators and parents with an approach to learning to read that emphasizes learning to think and becoming a functional reader who can enjoy using this skill throughout life. Included are simple, inexpensive suggestions for improving reading, such as more attention to regular school…

Working towards Skills: Perspectives on Workforce Development in SMEs. Research Report.

ERIC Educational Resources Information Center

Hughes, Maria; Keddie, Vince; Webb, Peter; Corney, Mark

Research into workforce development (WD) considered the relationship between corporate assessments of workers' development needs and WD strategies; how learning at work takes place; and what learning methods are used and their effectiveness. Focus was on practice in small and medium-sized enterprises (SMEs). Methodology included a literature…

Beyond Learning by Doing: The Brain Compatible Approach.

ERIC Educational Resources Information Center

Roberts, Jay W.

2002-01-01

Principles of brain-based learning, including pattern and meaning making, parallel processing, and the role of stress and threat, are explained, along with their connections to longstanding practices of experiential education. The Brain Compatible Approach is one avenue for clarifying to mainstream educators how and why experiential methods are…

On the Design of Effective Distance Teaching Courses.

ERIC Educational Resources Information Center

Sparkes, John J.

This paper offers pragmatic guidance for designing effective distance education courses, including the need for designers to be aware of their educational aims, and the selection of different media and methods for effective learning. Discussion of a brief taxonomy of types of learning--knowledge, skills, understanding, and attitudes--is followed…

Teaching with Cases: Learning to Question.

ERIC Educational Resources Information Center

Boehrer, John; Linsky, Marty

1990-01-01

This chapter discusses the origins of the case method, looks at the question of what is a case, gives ideas about learning in case teaching, the purposes it can serve in the classroom, the ground rules for case discussion, including the role of questions, and new directions for case teaching. (MLW)

Exploratory Analysis in Learning Analytics

ERIC Educational Resources Information Center

Gibson, David; de Freitas, Sara

2016-01-01

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex…

Transformative Learning through Education Abroad: A Case Study of a Community College Program

ERIC Educational Resources Information Center

Brenner, Ashley A.

2014-01-01

This case study examined how participating in a short-term education abroad program fostered transformative learning for a small group of community college students. As a participant-observer, I utilized ethnographic methods, including interviews, observations, and document analysis, to understand students' perceptions of their experiences…

Learning Science in a Second Language

ERIC Educational Resources Information Center

Lindquist, Bill; Loynachan, Courtney

2016-01-01

Courtney Loynachan was a student in Dr. Lindquist's summer 2014 "Teaching Science in the Elementary School" methods course at Hamline University in Saint Paul, Minnesota. The course included an exploration of the power of writing as a learning tool for science with a particular focus on the use of science notebooks. Throughout the…

The Role of Museum Exhibits in Teaching Textile Science

ERIC Educational Resources Information Center

Diddi, Sonali; Marcketti, Sara B.

2014-01-01

The concept of learning outside of the traditional, formal classroom setting is an important component of family and consumer sciences (FCS) educational pedagogy. Methods of learning beyond the FCS classroom include visiting museums, accessing archives--both in person and virtually--and participating in field studies (Roehl, 2013). Although many…

Reaching More Students through Thinking in Physics

ERIC Educational Resources Information Center

Coletta, Vincent P.

2017-01-01

Thinking in Physics (TIP) is a new curriculum that is more effective than commonly used interactive engagement methods for students who have the greatest difficulty learning physics. Research has shown a correlation between learning in physics and other factors, including scientific reasoning ability. The TIP curriculum addresses those factors.…

The Language of Mathematics: The Importance of Teaching and Learning Mathematical Vocabulary

ERIC Educational Resources Information Center

Riccomini, Paul J.; Smith, Gregory W.; Hughes, Elizabeth M.; Fries, Karen M.

2015-01-01

Vocabulary understanding is a major contributor to overall comprehension in many content areas, including mathematics. Effective methods for teaching vocabulary in all content areas are diverse and long standing. Teaching and learning the language of mathematics is vital for the development of mathematical proficiency. Students' mathematical…

77 FR 62244 - National Institute of Mental Health; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-12

... investigators, to include the Unit on Learning and Decision Making, the Section on Integrative Neuroimaging, the Section on Neurocircuitry, the Section on Cognitive Neuropsychology, the Section on Functional Imaging Methods, the Unit on Learning and Plasticity, and the Section on Neuroadaptation and Protein Metabolism...

Graduates' Perceptions of Learning Affordances in Longitudinal Integrated Clerkships: A Dual-Institution, Mixed-Methods Study.

PubMed

Latessa, Robyn A; Swendiman, Robert A; Parlier, Anna Beth; Galvin, Shelley L; Hirsh, David A

2017-09-01

The authors explored affordances that contribute to participants' successful learning in longitudinal integrated clerkships (LICs). This dual-institutional, mixed-methods study included electronic surveys and semistructured interviews of LIC graduates who completed their core clinical (third) year of medical school. These LIC graduates took part in LICs at Harvard Medical School from 2004 to 2013 and the University of North Carolina School of Medicine-Asheville campus from 2009 to 2013. The survey questions asked LIC graduates to rate components of LICs that they perceived as contributing to successful learning. A research assistant interviewed a subset of study participants about their learning experiences. The authors analyzed aggregate data quantitatively and performed a qualitative content analysis on interview data. The graduates reported multiple affordances that they perceive contributed to successful learning in their LIC. The most reported components included continuity and relationships with preceptors, patients, place, and peers, along with integration of and flexibility within the curriculum. As LIC models grow in size and number, and their structures and processes evolve, learners' perceptions of affordances may guide curriculum planning. Further research is needed to investigate to what degree and by what means these affordances support learning in LICs and other models of clinical education.

Changing to Concept-Based Curricula: The Process for Nurse Educators

PubMed Central

Baron, Kristy A.

2017-01-01

Background: The complexity of health care today requires nursing graduates to use effective thinking skills. Many nursing programs are revising curricula to include concept-based learning that encourages problem-solving, effective thinking, and the ability to transfer knowledge to a variety of situations—requiring nurse educators to modify their teaching styles and methods to promote student-centered learning. Changing from teacher-centered learning to student-centered learning requires a major shift in thinking and application. Objective: The focus of this qualitative study was to understand the process of changing to concept-based curricula for nurse educators who previously taught in traditional curriculum designs. Methods: The sample included eight educators from two institutions in one Western state using a grounded theory design. Results: The themes that emerged from participants’ experiences consisted of the overarching concept, support for change, and central concept, finding meaning in the change. Finding meaning is supported by three main themes: preparing for the change, teaching in a concept-based curriculum, and understanding the teaching-learning process. Conclusion: Changing to a concept-based curriculum required a major shift in thinking and application. Through support, educators discovered meaning to make the change by constructing authentic learning opportunities that mirrored practice, refining the change process, and reinforcing benefits of teaching. PMID:29399236

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.

Framework for e-learning assessment in dental education: a global model for the future.

PubMed

Arevalo, Carolina R; Bayne, Stephen C; Beeley, Josie A; Brayshaw, Christine J; Cox, Margaret J; Donaldson, Nora H; Elson, Bruce S; Grayden, Sharon K; Hatzipanagos, Stylianos; Johnson, Lynn A; Reynolds, Patricia A; Schönwetter, Dieter J

2013-05-01

The framework presented in this article demonstrates strategies for a global approach to e-curricula in dental education by considering a collection of outcome assessment tools. By combining the outcomes for overall assessment, a global model for a pilot project that applies e-assessment tools to virtual learning environments (VLE), including haptics, is presented. Assessment strategies from two projects, HapTEL (Haptics in Technology Enhanced Learning) and UDENTE (Universal Dental E-learning), act as case-user studies that have helped develop the proposed global framework. They incorporate additional assessment tools and include evaluations from questionnaires and stakeholders' focus groups. These measure each of the factors affecting the classical teaching/learning theory framework as defined by Entwistle in a standardized manner. A mathematical combinatorial approach is proposed to join these results together as a global assessment. With the use of haptic-based simulation learning, exercises for tooth preparation assessing enamel and dentine were compared to plastic teeth in manikins. Equivalence for student performance for haptic versus traditional preparation methods was established, thus establishing the validity of the haptic solution for performing these exercises. Further data collected from HapTEL are still being analyzed, and pilots are being conducted to validate the proposed test measures. Initial results have been encouraging, but clearly the need persists to develop additional e-assessment methods for new learning domains.

Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

PubMed

Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

2016-05-01

Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Team-Based Learning in US Colleges and Schools of Pharmacy

PubMed Central

Copeland, Jeffrey; Franks, Andrea S.; Karimi, Reza; McCollum, Marianne; Riese, David J.; Lin, Anne Y.F.

2013-01-01

Objective. To characterize the use of team-based learning (TBL) in US colleges and schools of pharmacy, including factors that may affect implementation and perceptions of faculty members regarding the impact of TBL on educational outcomes. Methods. Respondents identified factors that inhibit or enable TBL use and its impact on student learning. Results were stratified by type of institution (public/private), class size, and TBL experience. Results. Sixty-nine of 100 faculty members (69%) representing 43 (86%) institutions responded. Major factors considered to enable TBL implementation included a single campus and student and administration buy-in. Inhibiting factors included distant campuses, faculty resistance, and lack of training. Compared with traditional lectures, TBL is perceived to enhance student engagement, improve students’ preparation for class, and promote achievement of course outcomes. In addition, TBL is perceived to be more effective than lectures at fostering learning in all 6 domains of Bloom’s Taxonomy. Conclusions. Despite potential implementation challenges, faculty members perceive that TBL improves student engagement and learning. PMID:23966718

The Development of Interactive Mathematics Learning Material Based on Local Wisdom with .swf Format

NASA Astrophysics Data System (ADS)

Abadi, M. K.; Asih, E. C. M.; Jupri, A.

2018-05-01

Learning materials used by students and schools in Serang district are lacking because they do not contain local wisdom content. The aim of this study is to improve the deficiencies in learning materials used by students by making interactive materials based on local wisdom content with format .swf. The method in this research is research and development (RnD) with ADDIE model. In making this interactive learning materials in accordance with the stages of the ADDIE study. The results of this study include interactive learning materials based on local wisdom. This learning material is suitable for digital students.

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

PubMed Central

Sajid, Muhammad R.; Abothenain, Fayha; Salam, Yezan; AlJayar, Dina; Obeidat, Akef

2016-01-01

Objectives To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. Methods This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. Results A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Conclusions Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.  PMID:27591930

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

The Role of Spirituality in Transition to Parenthood: Qualitative Research Using Transformative Learning Theory.

PubMed

Klobučar, Nataša Rijavec

2016-08-01

This article presents results of a qualitative study of 12 adult couples making transition to parenthood. The aim of the study was to research the meaning of transition to parenthood through the lens of transformative learning theory. Transformative learning theory explains learning through meaning-making of that life experience. In this paper, the spiritual dimension of learning is emphasized. An important part of research methodology included biographical method, using semi-structured interviews before and after the birth of the first child. The research showed that transformative learning occurs in different spheres of life during transition to parenthood. This paper discusses the spiritual dimension of learning, meaning-making and presents results of the research.

Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning.

PubMed

Gorban, A N; Mirkes, E M; Zinovyev, A

2016-12-01

Most of machine learning approaches have stemmed from the application of minimizing the mean squared distance principle, based on the computationally efficient quadratic optimization methods. However, when faced with high-dimensional and noisy data, the quadratic error functionals demonstrated many weaknesses including high sensitivity to contaminating factors and dimensionality curse. Therefore, a lot of recent applications in machine learning exploited properties of non-quadratic error functionals based on L 1 norm or even sub-linear potentials corresponding to quasinorms L p (0

[Distance learning in postgraduate training of professionals. Example of occupational medicine specialization].

PubMed

Marcinkiewicz, Andrzej; Cybart, Adam; Chromińska-Szosland, Dorota

2002-01-01

The rapid development of science, technology, economy and the society has one along with the wide recognition of lifelong education and learning society concepts. Scientific centres worldwide conduct research how the access to the information and multimedia technology could bring about positive changes in our lives including improvement in education and the learning environment. Mankind development in conformity with social progress and sustainable development faces a new educational concept of learning society and open education in the information age, supported with multimedia and data processing technology. Constrains in resources availability for broadening the access to education had led to search for alternative, more time and cost-effective systems of education. One of them is distance learning, applied with success in many countries. The benefits of distance learning are well proven and can be extended to occupational medicine. Major advantages include: the integration of studies with work experience, flexibility, allowing studies to be matched to work requirements, perceived work and leisure timing, continuity of career progression. Likewise is in Poland this form of education becomes more and more popular. The distance education systems have been seen as an investment in human resource development. The vast variety of courses and educational stages makes possible the modern method of knowledge to be easily accessible. Experience of the School of Public Health in Łódź in distance learning had shown remarkable benefits of the method with comparable quality of intramural and distance learning in respect of the knowledge and experience gained by students.

Interprofessional online learning for primary healthcare: findings from a scoping review.

PubMed

Reeves, Scott; Fletcher, Simon; McLoughlin, Clodagh; Yim, Alastair; Patel, Kunal D

2017-08-04

This article presents the findings from a scoping review which explored the nature of interprofessional online learning in primary healthcare. The review was informed by the following questions: What is the nature of evidence on online postgraduate education for primary healthcare interprofessional teams? What learning approaches and study methods are used in this context? What is the range of reported outcomes for primary healthcare learners, their organisations and the care they deliver to patients/clients? The review explored the global literature on interprofessional online learning in primary healthcare settings. The review found that the 23 included studies employed a range of different e-learning methods with contrasting course durations, use of theory, participant mix, approaches to accreditation and assessment of learning. Most of the included studies reported outcomes associated with learner reactions and positive changes in participant attitudes/perceptions and improvement in knowledge/skills as a result of engagement in an e-learning course. In contrast, fewer studies reported changes in participant behaviours, changes in organisational practice and improvements to patients/clients. A number of educational, methodological and outcome implications are be offered. E-learning can enhance an education experience, support development, ease time constraints, overcome geographic limitations and can offer greater flexibility. However, it can also contribute to the isolation of learners and its benefits can be negated by technical problems. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

PubMed

Chai, Hua; Li, Zi-Na; Meng, De-Yu; Xia, Liang-Yong; Liang, Yong

2017-10-12

Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

Developing a multi-method approach to data collection and analysis for explaining the learning during simulation in undergraduate nurse education.

PubMed

Bland, Andrew J; Tobbell, Jane

2015-11-01

Simulation has become an established feature of undergraduate nurse education and as such requires extensive investigation. Research limited to pre-constructed categories imposed by some questionnaire and interview methods may only provide partial understanding. This is problematic in understanding the mechanisms of learning in simulation-based education as contemporary distributed theories of learning posit that learning can be understood as the interaction of individual identity with context. This paper details a method of data collection and analysis that captures interaction of individuals within the simulation experience which can be analysed through multiple lenses, including context and through the lens of both researcher and learner. The study utilised a grounded theory approach involving 31 under-graduate third year student nurses. Data was collected and analysed through non-participant observation, digital recordings of simulation activity and focus group deconstruction of their recorded simulation by the participants and researcher. Focus group interviews enabled further clarification. The method revealed multiple levels of dynamic data, concluding that in order to better understand how students learn in social and active learning strategies, dynamic data is required enabling researchers and participants to unpack what is happening as it unfolds in action. Copyright © 2015 Elsevier Ltd. All rights reserved.

[E-learning and university nursing education: an overview of reviews].

PubMed

De Caro, Walter; Marucci, Anna Rita; Giordani, Mauro; Sansoni, Julita

2014-01-01

The increasing use of digital technologies and e-learning in nursing education and the health professions was also reflected in the time to many studies and reviews. The aim of this overview was to analyze education through e-learning technologies for nursing and health professional students. A comprehensive search of literature was conducted using database PubMed/MEDLINE, Ebsco/CINAHL, 2003-2013. The search strategy resulted in the inclusion, in first instance, of 9732 items. After the reduction of duplicates, applying limits and other parameters of inclusion/exclusion and, at the end, evaluation of quality through AMSTARD check list, we included in this overview, 22 reviews. The analized reviews were allowed to spread in different topic areas: study population (students and faculty), e-learning methods (blended learning Game/3D/situated learning) and evaluation (information technology, learning satisfaction comparison of e-learning with the traditional teaching methods) This overview demonstrates that e-learning in nursing academic education is a valid alternative to traditional learning. If e-learning activities are well structured and modulated, some advantages and economies are clear possible. Regard effects of e-learning on the improvement of ability, data are at the momenti limited when compared to traditional learning. Often e-learning appear as an adjunct respect traditional learning, but is necessary consider e-learning and digital tecnology as priority for the future of education of nursing students.

Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

PubMed

Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

2014-10-01

Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

Effect of chronotype and student learning time on mathematical ability based on self-regulated learning

NASA Astrophysics Data System (ADS)

Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.

2018-05-01

One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.

The Challenges of Nursing Students in the Clinical Learning Environment: A Qualitative Study

PubMed Central

Jamshidi, Nahid; Molazem, Zahra; Sharif, Farkhondeh; Torabizadeh, Camellia; Najafi Kalyani, Majid

2016-01-01

Background/Aim. Clinical learning is a main part of nursing education. Students' exposure to clinical learning environment is one of the most important factors affecting the teaching-learning process in clinical settings. Identifying challenges of nursing students in the clinical learning environment could improve training and enhance the quality of its planning and promotion of the students. We aimed to explore Iranian nursing students' challenges in the clinical learning environment. Materials and Methods. This is a qualitative study using the content analysis approach. The participants consisted of seventeen nursing students and three nursing instructors. The participants were selected through purposive sampling method and attended semistructured interviews and focus groups. Results. Three themes emerged after data analysis, including ineffective communications, inadequate readiness, and emotional reactions. Conclusion. Nursing students in Iran are faced with many challenges in the clinical learning environment. All challenges identified in this study affected the students' learning in clinical setting. Therefore, we recommend that the instructors prepare students with a specific focus on their communication and psychological needs. PMID:27366787

Assessment of the usability of a digital learning technology prototype for monitoring intracranial pressure 1

PubMed Central

de Carvalho, Lilian Regina; Évora, Yolanda Dora Martinez; Zem-Mascarenhas, Silvia Helena

2016-01-01

ABSTRACT Objective: to assess the usability of a digital learning technology prototype as a new method for minimally invasive monitoring of intracranial pressure. Method: descriptive study using a quantitative approach on assessing the usability of a prototype based on Nielsen's ten heuristics. Four experts in the area of Human-Computer interaction participated in the study. Results: the evaluation delivered eight violated heuristics and 31 usability problems in the 32 screens of the prototype. Conclusion: the suggestions of the evaluators were critical for developing an intuitive, user-friendly interface and will be included in the final version of the digital learning technology. PMID:27579932

Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

PubMed Central

Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2018-01-01

Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. Results The AL methods produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p = 0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275 to 0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers’ different models during the training phase, compared to the variance of the induced models’ AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods. The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p = 0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p = 0.29), as was the difference between the Combination_XA and Exploitation methods (p = 0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired t-test, the difference between the intra-labeler AUC standard deviation when using the consensus label, versus that value when using the other two labeling strategies, was significant only when using the passive learning method (p = 0.014), but not when using any of the three AL methods. Conclusions The use of AL methods, (a) reduces intra-labeler variability in the performance of the induced models during the training phase, and thus reduces the risk of halting the process at a local minimum that is significantly different in performance from the rest of the learned models; and (b) reduces Inter-labeler performance variance, and thus reduces the dependence on the use of a particular labeler. In addition, the use of a consensus label, agreed upon by a rather uneven group of labelers, might be at least as good as using the gold standard labeler, who might not be available, and certainly better than randomly selecting one of the group’s individual labelers. Finally, using the AL methods when provided by the consensus label reduced the intra-labeler AUC variance during the learning phase, compared to using passive learning. PMID:28456512

Online eLearning for undergraduates in health professions: A systematic review of the impact on knowledge, skills, attitudes and satisfaction.

PubMed

George, Pradeep Paul; Papachristou, Nikos; Belisario, José Marcano; Wang, Wei; Wark, Petra A; Cotic, Ziva; Rasmussen, Kristine; Sluiter, René; Riboli-Sasco, Eva; Tudor Car, Lorainne; Musulanov, Eve Marie; Molina, Joseph Antonio; Heng, Bee Hoon; Zhang, Yanfeng; Wheeler, Erica Lynette; Al Shorbaji, Najeeb; Majeed, Azeem; Car, Josip

2014-06-01

Health systems worldwide are facing shortages in health professional workforce. Several studies have demonstrated the direct correlation between the availability of health workers, coverage of health services, and population health outcomes. To address this shortage, online eLearning is increasingly being adopted in health professionals' education. To inform policy-making, in online eLearning, we need to determine its effectiveness. We performed a systematic review of the effectiveness of online eLearning through a comprehensive search of the major databases for randomised controlled trials that compared online eLearning to traditional learning or alternative learning methods. The search period was from January 2000 to August 2013. We included articles which primarily focused on students' knowledge, skills, satisfaction and attitudes toward eLearning and cost-effectiveness and adverse effects as secondary outcomes. Two reviewers independently extracted data from the included studies. Due to significant heterogeneity among the included studies, we presented our results as a narrative synthesis. Fifty-nine studies, including 6750 students enrolled in medicine, dentistry, nursing, physical therapy and pharmacy studies, met the inclusion criteria. Twelve of the 50 studies testing knowledge gains found significantly higher gains in the online eLearning intervention groups compared to traditional learning, whereas 27 did not detect significant differences or found mixed results. Eleven studies did not test for differences. Six studies detected significantly higher skill gains in the online eLearning intervention groups, whilst 3 other studies testing skill gains did not detect differences between groups and 1 study showed mixed results. Twelve studies tested students' attitudes, of which 8 studies showed no differences in attitudes or preferences for online eLearning. Students' satisfaction was measured in 29 studies, 4 studies showed higher satisfaction for online eLearning and 20 studies showed no difference in satisfaction between online eLearning and traditional learning. Risk of bias was high for several of the included studies. The current evidence base suggests that online eLearning is equivalent, possibly superior to traditional learning. These findings present a potential incentive for policy makers to cautiously encourage its adoption, while respecting the heterogeneity among the studies.

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

PubMed

Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2017-09-01

Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. The AL methods: produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p=0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275-0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers' different models during the training phase, compared to the variance of the induced models' AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p=0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p=0.29), as was the difference between the Combination_XA and Exploitation methods (p=0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired t-test, the difference between the intra-labeler AUC standard deviation when using the consensus label, versus that value when using the other two labeling strategies, was significant only when using the passive learning method (p=0.014), but not when using any of the three AL methods. The use of AL methods, (a) reduces intra-labeler variability in the performance of the induced models during the training phase, and thus reduces the risk of halting the process at a local minimum that is significantly different in performance from the rest of the learned models; and (b) reduces Inter-labeler performance variance, and thus reduces the dependence on the use of a particular labeler. In addition, the use of a consensus label, agreed upon by a rather uneven group of labelers, might be at least as good as using the gold standard labeler, who might not be available, and certainly better than randomly selecting one of the group's individual labelers. Finally, using the AL methods: when provided by the consensus label reduced the intra-labeler AUC variance during the learning phase, compared to using passive learning. Copyright © 2017 Elsevier B.V. All rights reserved.

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Comparing two Bayes methods based on the free energy functions in Bernoulli mixtures.

PubMed

Yamazaki, Keisuke; Kaji, Daisuke

2013-08-01

Hierarchical learning models are ubiquitously employed in information science and data engineering. The structure makes the posterior distribution complicated in the Bayes method. Then, the prediction including construction of the posterior is not tractable though advantages of the method are empirically well known. The variational Bayes method is widely used as an approximation method for application; it has the tractable posterior on the basis of the variational free energy function. The asymptotic behavior has been studied in many hierarchical models and a phase transition is observed. The exact form of the asymptotic variational Bayes energy is derived in Bernoulli mixture models and the phase diagram shows that there are three types of parameter learning. However, the approximation accuracy or interpretation of the transition point has not been clarified yet. The present paper precisely analyzes the Bayes free energy function of the Bernoulli mixtures. Comparing free energy functions in these two Bayes methods, we can determine the approximation accuracy and elucidate behavior of the parameter learning. Our results claim that the Bayes free energy has the same learning types while the transition points are different. Copyright © 2013 Elsevier Ltd. All rights reserved.

Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.

PubMed

Chalmers, Eric; Contreras, Edgar Bermudez; Robertson, Brandon; Luczak, Artur; Gruber, Aaron

2017-04-17

The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks. For example, in robot navigation, environment-centric information may include the robot's geographic location, while agent-centric information may include sensor readings of various nearby obstacles. We propose that these situations provide an opportunity for a very natural style of knowledge transfer, in which the agent learns to predict actions' environmental consequences using agent-centric information. These predictions contain important information about the affordances and dangers present in a novel environment, and can effectively transfer knowledge from agent-centric to environment-centric learning systems. Using several example problems including spatial navigation and network routing, we show that our knowledge transfer approach can allow faster and lower cost learning than existing alternatives.

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

The development of a primary dental care outreach course.

PubMed

Waterhouse, P; Maguire, A; Tabari, D; Hind, V; Lloyd, J

2008-02-01

The aim of this work was to develop the first north-east based primary dental care outreach (PDCO) course for clinical dental undergraduate students at Newcastle University. The process of course design will be described and involved review of the existing Bachelor of Dental Surgery (BDS) degree course in relation to previously published learning outcomes. Areas were identified where the existing BDS course did not meet fully these outcomes. This was followed by setting the PDCO course aims and objectives, intended learning outcomes, curriculum and structure. The educational strategy and methods of teaching and learning were subsequently developed together with a strategy for overall quality control of the teaching and learning experience. The newly developed curriculum was aligned with appropriate student assessment methods, including summative, formative and ipsative elements.

"We're living what we're learning": student perspectives in distance learning degree and certificate programs in public health.

PubMed

Cannon, M M; Umble, K E; Steckler, A; Shay, S

2001-01-01

The authors used surveys and interviews to study participants' motivations for enrolling and perceptions of the weaknesses and strengths of two distance learning programs administered by the University of North Carolina at Chapel Hill School of Public Health (SPH): the MPH in Public Health Leadership and a certificate program organized collaboratively with the Mahidol University SPH in Thailand. Chief motivations were career advancement, job performance improvement, convenience, and obtaining a degree from a reputable institution. Strengths included the curriculum, networking opportunities, and administrative and technical support. Concerns included quality of interaction with faculty and instructional methods.

Design and validation of general biology learning program based on scientific inquiry skills

NASA Astrophysics Data System (ADS)

Cahyani, R.; Mardiana, D.; Noviantoro, N.

2018-03-01

Scientific inquiry is highly recommended to teach science. The reality in the schools and colleges is that many educators still have not implemented inquiry learning because of their lack of understanding. The study aims to1) analyze students’ difficulties in learning General Biology, 2) design General Biology learning program based on multimedia-assisted scientific inquiry learning, and 3) validate the proposed design. The method used was Research and Development. The subjects of the study were 27 pre-service students of general elementary school/Islamic elementary schools. The workflow of program design includes identifying learning difficulties of General Biology, designing course programs, and designing instruments and assessment rubrics. The program design is made for four lecture sessions. Validation of all learning tools were performed by expert judge. The results showed that: 1) there are some problems identified in General Biology lectures; 2) the designed products include learning programs, multimedia characteristics, worksheet characteristics, and, scientific attitudes; and 3) expert validation shows that all program designs are valid and can be used with minor revisions. The first section in your paper.

Quantum neuromorphic hardware for quantum artificial intelligence

NASA Astrophysics Data System (ADS)

Prati, Enrico

2017-08-01

The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.

The Impact of Sound-Field Systems on Learning and Attention in Elementary School Classrooms

ERIC Educational Resources Information Center

Dockrell, Julie E.; Shield, Bridget

2012-01-01

Purpose: The authors evaluated the installation and use of sound-field systems to investigate the impact of these systems on teaching and learning in elementary school classrooms. Methods: The evaluation included acoustic surveys of classrooms, questionnaire surveys of students and teachers, and experimental testing of students with and without…

The Feasibility of E-Learning Implementation in an Iranian University

ERIC Educational Resources Information Center

Mirzamohammadi, M. H.

2017-01-01

The present research aimed to investigate the feasibility of e-learning implementation in an Iranian comprehensive university (included medical and non-medical fields) to provide appropriate solutions in this regard. To achieve this objective, seven research questions were formed. Surveying method was applied for data collection in this study.…

A Customizable Language Learning Support System Using Ontology-Driven Engine

ERIC Educational Resources Information Center

Wang, Jingyun; Mendori, Takahiko; Xiong, Juan

2013-01-01

This paper proposes a framework for web-based language learning support systems designed to provide customizable pedagogical procedures based on the analysis of characteristics of both learner and course. This framework employs a course-centered ontology and a teaching method ontology as the foundation for the student model, which includes learner…

Assessing Student Openness to Inquiry-Based Learning in Precalculus

ERIC Educational Resources Information Center

Cooper, Thomas; Bailey, Brad; Briggs, Karen; Holliday, John

2017-01-01

The authors have completed a 2-year quasi-experimental study on the use of inquiry-based learning (IBL) in precalculus. This study included six traditional lecture-style courses and seven modified Moore method courses taught by three instructors. Both quantitative and qualitative analyses were used to investigate the attitudes and beliefs of the…

Hospitable Kinship in Theological Education: Cross-Cultural Perspectives on Teaching and Learning as Gift Exchange

ERIC Educational Resources Information Center

Wimberly, Anne E. Streaty

2004-01-01

Using an autobiographical approach for pedagogical reflection, the author raises questions about how to include "hospitable kinship" and "gift exchange" in teaching and learning. Her experience with a Zimbabwean community circle of hospitable kinship has prompted her to consider how this method of community formation might be…

Learning-Method Choices and Personal Characteristics in Solving a Physical Education Problem

ERIC Educational Resources Information Center

Vincent-Morin, Madeleine; Lafont, Lucile

2005-01-01

The goal of this study was to identify the relationships between the learning choices made by pupils and their personal characteristics, including cognitive style (field dependence--independence), a motivational variable (feeling of self-efficacy), and a cognitive variable (task representation). The participants were 64 twelve-year-old sixth…

The Issue of Death and Dying: Employing Problem-Based Learning in Nursing Education.

ERIC Educational Resources Information Center

Mok, Esther; Lee, Wai Man; Wong, Frances Kam-yuet

2002-01-01

Hong Kong nursing students used journals to problem-based learning (PBL) related to dying patients. Increased self-awareness, positive attitude toward death, and culturally sensitive care resulted. PBL methods included information searches, interviews with experts and patients, and tutorials for sharing feelings and information. (Contains 21…

Developing "Reflective" Development Practitioners through an Action-Learning Curriculum: Problems and Challenges in a South African Context.

ERIC Educational Resources Information Center

Luckett, S.; Luckett, K.

1999-01-01

A South African university's community development program attempted to integrate Checkland's soft-systems method into Kolb's learning-cycle theory. Evaluation revealed shortcomings in the curriculum design, including the assumption of learner autonomy, necessity of assessing students individually, and difficulty of allowing learners to construct…

Automatic Selection of Suitable Sentences for Language Learning Exercises

ERIC Educational Resources Information Center

Pilán, Ildikó; Volodina, Elena; Johansson, Richard

2013-01-01

In our study we investigated second and foreign language (L2) sentence readability, an area little explored so far in the case of several languages, including Swedish. The outcome of our research consists of two methods for sentence selection from native language corpora based on Natural Language Processing (NLP) and machine learning (ML)…

Tic Tac Toe Math. Instructional Guide.

ERIC Educational Resources Information Center

Cooper, Richard

This instructional guide and set of three companion workbooks are intended for use in an arithmetic course based on the Tic Tac Toe method of addition and multiplication, which is an alternative means of learning to add and multiply that was developed for students whose learning disabilities (including difficulty in distinguishing left from right…

The Search for Methods of Group Instruction as Effective as One-to-One Tutoring.

ERIC Educational Resources Information Center

Bloom, Benjamin S.

1984-01-01

Summarizes research exploring six solutions to the "2 sigma problem" of devising teaching-learning conditions that will enable the majority of students under group instruction to achieve at levels now possible only when students are tutored. Recommendations include using mastery learning, improving the home environment, and emphasizing higher…

Interactive and Authentic e-Learning Tools for Criminal Justice Education

ERIC Educational Resources Information Center

Miner-Romanoff, Karen; McCombs, Jonathan; Chongwony, Lewis

2017-01-01

This mixed-method study tested the effectiveness of two experiential e-learning tools for criminal justice courses. The first tool was a comprehensive video series, including a criminal trial and interviews with the judge, defense counsel, prosecution, investigators and court director (virtual trial), in order to enhance course and learning…

Technologies for Foreign Language Learning: A Review of Technology Types and Their Effectiveness

ERIC Educational Resources Information Center

Golonka, Ewa M.; Bowles, Anita R.; Frank, Victor M.; Richardson, Dorna L.; Freynik, Suzanne

2014-01-01

This review summarizes evidence for the effectiveness of technology use in foreign language (FL) learning and teaching, with a focus on empirical studies that compare the use of newer technologies with more traditional methods or materials. The review of over 350 studies (including classroom-based technologies, individual study tools,…

"Almost Everything We Do Includes Inquiry": Fostering Inquiry-Based Teaching and Learning with Preschool Teachers

ERIC Educational Resources Information Center

Hollingsworth, Heidi L.; Vandermaas-Peeler, Maureen

2017-01-01

Given the increased emphasis on science in early learning standards, two studies were conducted to investigate preschool teachers' efficacy for teaching science and their inquiry-based teaching practices. Fifty-one teachers completed a survey of their efficacy for teaching science and understanding of inquiry methods. Teachers reported moderate…

Exploring Dimensions of Social Inclusion among Alternative Learning Centres in the USA

ERIC Educational Resources Information Center

Henderson, Dawn X.; Barnes, Rachelle Redmond

2016-01-01

Increasing disparities in out-of-school suspension and dropout rates have led a number of school districts to develop alternative models of education to include alternative learning centres (ALCs). Using an exploratory mixed methods design, this study explores dimensions of social inclusion among ALCs, located in the southeastern region of the…

The Impact of OER on Teaching and Learning Practice

ERIC Educational Resources Information Center

Weller, Martin; de los Arcos, Bea; Farrow, Rob; Pitt, Beck; McAndrew, Patrick

2015-01-01

The OER Research Hub has been investigating the impact of OER, using eleven hypotheses, and a mixed methods approach to establish an evidence base. This paper explores the findings relating to teaching and learning. The findings reveal a set of direct impacts, including an increase in factors relating to student performance, increased reflection…

Pragmatics & Language Learning. Volume 14

ERIC Educational Resources Information Center

Bardovi-Harlig, Kathleen, Ed.; Félix-Brasdefer, J. César, Ed.

2016-01-01

This volume contains a selection of papers presented at the 2014 International Conference of Pragmatics and Language Learning at Indiana University. It includes fourteen papers on a variety of topics, with a diversity of first and second languages, and a wide range of methods used to collect pragmatic data in L2 and FL settings. This volume is…

Small Learning Communities Sense of Belonging to Reach At-Risk Students of Promise

ERIC Educational Resources Information Center

Hackney, Debbie

2011-01-01

The research design is a quantitative causal comparative method. The Florida Comprehensive Assessment Test (FCAT) which measures student scores included assessments in mathematics and reading. The design study called for an examination of how type of small learning community (SLC) or the type non-SLC high school environment affected student…

Articulatory Control in Childhood Apraxia of Speech in a Novel Word-Learning Task

ERIC Educational Resources Information Center

Case, Julie; Grigos, Maria I.

2016-01-01

Purpose: Articulatory control and speech production accuracy were examined in children with childhood apraxia of speech (CAS) and typically developing (TD) controls within a novel word-learning task to better understand the influence of planning and programming deficits in the production of unfamiliar words. Method: Participants included 16…

An Investigation of Higher-Order Thinking Skills in Smaller Learning Community Social Studies Classrooms

ERIC Educational Resources Information Center

Fischer, Christopher; Bol, Linda; Pribesh, Shana

2011-01-01

This study investigated the extent to which higher-order thinking skills are promoted in social studies classes in high schools that are implementing smaller learning communities (SLCs). Data collection in this mixed-methods study included classroom observations and in-depth interviews. Findings indicated that higher-order thinking was rarely…

Teaching and Learning. An Introduction to New Methods and Resources in Higher Education.

ERIC Educational Resources Information Center

MacKenzie, Norman; And Others

Proceeding at a different rate in each country, a world movement toward mass higher education is taking place. For this reason, attention should be given to the teaching-learning process in universities and to media innovations. The latter include television, language laboratories, teaching machines, electronic response systems, reprographic…

Social and Individual Frame Factors in L2 Learning: Comparative Aspects.

ERIC Educational Resources Information Center

Ekstrand, Lars H.

A large number of factors are considered in their role in second language learning. Individual factors include language aptitude, personality, attitudes and motivation, and the role of the speaker's native language. Teacher factors involve the method of instruction, the sex of the teacher, and a teacher's training and competence, while…

Student Self-Reported Learning Outcomes of Field Trips: The Pedagogical Impact

ERIC Educational Resources Information Center

Alon, Nirit Lavie; Tal, Tali

2015-01-01

In this study, we used the classification and regression trees (CART) method to draw relationships between student self-reported learning outcomes in 26 field trips to natural environments and various characteristics of the field trip that include variables associated with preparation and pedagogy. We wished to examine the extent to which the…

Lessons Learned from the Whole Child and Coordinated School Health Approaches

ERIC Educational Resources Information Center

Rasberry, Catherine N.; Slade, Sean; Lohrmann, David K.; Valois, Robert F.

2015-01-01

Background: The new Whole School, Whole Community, Whole Child (WSCC) model, designed to depict links between health and learning, is founded on concepts of coordinated school health (CSH) and a whole child approach to education. Methods: The existing literature, including scientific articles and key publications from national agencies and…

Lessons Learned Designing and Using an Online Discussion Forum for Care Coordinators in Primary Care.

PubMed

Ferrante, Jeanne M; Friedman, Asia; Shaw, Eric K; Howard, Jenna; Cohen, Deborah J; Shahidi, Laleh

2015-10-18

While an increasing number of researchers are using online discussion forums for qualitative research, few authors have documented their experiences and lessons learned to demonstrate this method's viability and validity in health services research. We comprehensively describe our experiences, from start to finish, of designing and using an asynchronous online discussion forum for collecting and analyzing information elicited from care coordinators in Patient-Centered Medical Homes across the United States. Our lessons learned from each phase, including planning, designing, implementing, using, and ending this private online discussion forum, provide some recommendations for other health services researchers considering this method. An asynchronous online discussion forum is a feasible, efficient, and effective method to conduct a qualitative study, particularly when subjects are health professionals. © The Author(s) 2015.

Survey on deep learning for radiotherapy.

PubMed

Meyer, Philippe; Noblet, Vincent; Mazzara, Christophe; Lallement, Alex

2018-07-01

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

PubMed

Zhang, Jing; Song, Yanlin; Xia, Fan; Zhu, Chenjing; Zhang, Yingying; Song, Wenpeng; Xu, Jianguo; Ma, Xuelei

2017-09-01

Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm. Large size of the training dataset is critical to increase the diagnostic accuracy. The performance of the trained machine could be tested by new images before clinical use. Real-time diagnosis, easy to use and potential high accuracy were the advantages of AI for IOPD. In sum, AI with deep learning technology is a promising method to help rapid and accurate IOPD. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

NASA Astrophysics Data System (ADS)

Silversides, Katherine; Melkumyan, Arman; Wyman, Derek; Hatherly, Peter

2015-04-01

The mining of stratiform ore deposits requires a means of determining the location of stratigraphic boundaries. A variety of geophysical logs may provide the required data but, in the case of banded iron formation hosted iron ore deposits in the Hamersley Ranges of Western Australia, only one geophysical log type (natural gamma) is collected for this purpose. The information from these logs is currently processed by slow manual interpretation. In this paper we present an alternative method of automatically identifying recurring stratigraphic markers in natural gamma logs from multiple drill holes. Our approach is demonstrated using natural gamma geophysical logs that contain features corresponding to the presence of stratigraphically important marker shales. The host stratigraphic sequence is highly consistent throughout the Hamersley and the marker shales can therefore be used to identify the stratigraphic location of the banded iron formation (BIF) or BIF hosted ore. The marker shales are identified using Gaussian Processes (GP) trained by either manual or active learning methods and the results are compared to the existing geological interpretation. The manual method involves the user selecting the signatures for improving the library, whereas the active learning method uses the measure of uncertainty provided by the GP to select specific examples for the user to consider for addition. The results demonstrate that both GP methods can identify a feature, but the active learning approach has several benefits over the manual method. These benefits include greater accuracy in the identified signatures, faster library building, and an objective approach for selecting signatures that includes the full range of signatures across a deposit in the library. When using the active learning method, it was found that the current manual interpretation could be replaced in 78.4% of the holes with an accuracy of 95.7%.

The impact of E-learning in medical education.

PubMed

Ruiz, Jorge G; Mintzer, Michael J; Leipzig, Rosanne M

2006-03-01

The authors provide an introduction to e-learning and its role in medical education by outlining key terms, the components of e-learning, the evidence for its effectiveness, faculty development needs for implementation, evaluation strategies for e-learning and its technology, and how e-learning might be considered evidence of academic scholarship. E-learning is the use of Internet technologies to enhance knowledge and performance. E-learning technologies offer learners control over content, learning sequence, pace of learning, time, and often media, allowing them to tailor their experiences to meet their personal learning objectives. In diverse medical education contexts, e-learning appears to be at least as effective as traditional instructor-led methods such as lectures. Students do not see e-learning as replacing traditional instructor-led training but as a complement to it, forming part of a blended-learning strategy. A developing infrastructure to support e-learning within medical education includes repositories, or digital libraries, to manage access to e-learning materials, consensus on technical standardization, and methods for peer review of these resources. E-learning presents numerous research opportunities for faculty, along with continuing challenges for documenting scholarship. Innovations in e-learning technologies point toward a revolution in education, allowing learning to be individualized (adaptive learning), enhancing learners' interactions with others (collaborative learning), and transforming the role of the teacher. The integration of e-learning into medical education can catalyze the shift toward applying adult learning theory, where educators will no longer serve mainly as the distributors of content, but will become more involved as facilitators of learning and assessors of competency.

[Learning how to learn for specialist further education].

PubMed

Breuer, G; Lütcke, B; St Pierre, M; Hüttl, S

2017-02-01

The world of medicine is becoming from year to year more complex. This necessitates efficient learning processes, which incorporate the principles of adult education but with unchanged periods of further education. The subject matter must be processed, organized, visualized, networked and comprehended. The learning process should be voluntary and self-driven with the aim of learning the profession and becoming an expert in a specialist field. Learning is an individual process. Despite this, the constantly cited learning styles are nowadays more controversial. An important factor is a healthy mixture of blended learning methods, which also use new technical possibilities. These include a multitude of e‑learning options and simulations, which partly enable situative learning in a "shielded" environment. An exemplary role model of the teacher and feedback for the person in training also remain core and sustainable aspects in medical further education.

E learning in surgery.

PubMed

Aryal, Kamal Raj; Pereira, Jerome

2014-12-01

E learning means use of electronic media and information technologies in education. Virtual learning environment (VLE) provides learning platforms consisting of online tools, databases and managed resources. This article is a review of use of E learning in medical and surgical education including available evidence favouring this approach. E learning has been shown to be more effective, less costly and more satisfying to the students than the traditional methods. E learning cannot however replace direct consultant supervision at their place of work in surgical trainees and a combination of both called blended learning has been shown to be most useful. As an example of university-based qualification, one such programme is presented to clarify the components and the process of E learning. Increasing use of E learning and occasional face to face focussed supervision by the teacher is likely to enhance surgical training in the future.

Solving ill-posed inverse problems using iterative deep neural networks

NASA Astrophysics Data System (ADS)

Adler, Jonas; Öktem, Ozan

2017-12-01

We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the ‘gradient’ component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 × 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

Mining reflective continuing medical education data for family physician learning needs.

PubMed

Lewis, Denice Colleen; Pluye, Pierre; Rodriguez, Charo; Grad, Roland

2016-04-06

A mixed methods research (sequential explanatory design) studied the potential of mining the data from the consumers of continuing medical education (CME) programs, for the developers of CME programs. The quantitative data generated by family physicians, through applying the information assessment method to CME content, was presented to key informants from the CME planning community through a qualitative description study.The data were revealed to have many potential applications including supporting the creation of CME content, CME program planning and personal learning portfolios.

Exploring Normalization and Network Reconstruction Methods using In Silico and In Vivo Models

EPA Science Inventory

Abstract: Lessons learned from the recent DREAM competitions include: The search for the best network reconstruction method continues, and we need more complete datasets with ground truth from more complex organisms. It has become obvious that the network reconstruction methods t...

A Student Experiment Method for Learning the Basics of Embedded Software Technologies Including Hardware/Software Co-design

NASA Astrophysics Data System (ADS)

Kambe, Hidetoshi; Mitsui, Hiroyasu; Endo, Satoshi; Koizumi, Hisao

The applications of embedded system technologies have spread widely in various products, such as home appliances, cellular phones, automobiles, industrial machines and so on. Due to intensified competition, embedded software has expanded its role in realizing sophisticated functions, and new development methods like a hardware/software (HW/SW) co-design for uniting HW and SW development have been researched. The shortfall of embedded SW engineers was estimated to be approximately 99,000 in the year 2006, in Japan. Embedded SW engineers should understand HW technologies and system architecture design as well as SW technologies. However, a few universities offer this kind of education systematically. We propose a student experiment method for learning the basics of embedded system development, which includes a set of experiments for developing embedded SW, developing embedded HW and experiencing HW/SW co-design. The co-design experiment helps students learn about the basics of embedded system architecture design and the flow of designing actual HW and SW modules. We developed these experiments and evaluated them.

"It's like we're grasping at anything": caregivers' education needs and preferred learning methods.

PubMed

Mastel-Smith, Beth; Stanley-Hermanns, Melinda

2012-07-01

In this qualitative descriptive study, we explored caregivers' educational needs and preferred methods of information delivery. Descriptions are based on five focus groups (N = 29) conducted with ethnically diverse, current and past family caregivers, including those who had previously attended a structured educational program. Themes arose from verbatim data transcriptions and coded themes. Four categories of educational needs were identified: (a) respite, (b) caregiving essentials, (c) self-care, and (d) the emotional aspects of caregiving. Advantages and disadvantages of learning methods are discussed, along with reasons for and outcomes of attending caregiver workshops. An informed caregiver model is proposed. Health care providers must assess educational needs and strive to provide appropriate information as dictated by the care recipient's condition and caregiver's expressed desires. Innovative methods of delivering information that are congruent with different caregiving circumstances and learning preferences must be developed and tested.

Construction of teacher knowledge in context: Preparing elementary teachers to teach mathematics and science

NASA Astrophysics Data System (ADS)

Lowery, Maye Norene Vail

1998-12-01

The purposes of this study were to further the understanding of how preservice teacher construct teacher knowledge and pedagogical content knowledge of elementary mathematics and science and to determine the extent of that knowledge in a school-based setting. Preservice teachers, university instructors, inservice teachers, and other school personnel were involved in this context-specific study. Evidence of the preservice teachers' knowledge construction (its acquisition, its dimensions, and the social context) was collected through the use of a qualitative methodology. Collected data included individual and group interviews, course documents, artifacts, and preservice teaching portfolios. Innovative aspects of this integrated mathematics and science elementary methods course included standards-based instruction with immediate access to field experiences. Grade-level teams of preservice and inservice teachers planned and implemented lessons in mathematics and science for elementary students. An on-site, portable classroom building served as a mathematics and science teaching and learning laboratory. A four-stage analysis was performed, revealing significant patterns of learning. An ecosystem of learning within a constructivist learning environment was identified to contain three systems: the university system; the school system; and the cohort of learners system. A mega system for the construction of teacher knowledge was revealed in the final analysis. Learning venues were discovered to be the conduits of learning in a situated learning context. Analysis and synthesis of data revealed an extensive acquisition of teacher knowledge and pedagogical content knowledge through identified learning components. Patience, flexibility, and communication were identified as necessities for successful teaching. Learning components included: collaboration with inservice teachers; implementation of discovery learning and hands-on/minds-on learning; small groupwork; lesson planning; classroom management; and application of standards-based instruction. Prolonged, extensive classroom involvement provided familiarity with the ability levels of elementary students. Gains in positive attitudes and confidence in teaching mathematics and science were identified as direct results of this experience. This may be attributed to the immersion in the school-based setting (hands-on) and the standards-based approach (minds-on) methods course. The results are written in case study form using thick description with an emphasis on preservice teachers.

Learning style and concept acquisition of community college students in introductory biology

NASA Astrophysics Data System (ADS)

Bobick, Sandra Burin

This study investigated the influence of learning style on concept acquisition within a sample of community college students in a general biology course. There are two subproblems within the larger problem: (1) the influence of demographic variables (age, gender, number of college credits, prior exposure to scientific information) on learning style, and (2) the correlations between prior scientific knowledge, learning style and student understanding of the concept of the gene. The sample included all students enrolled in an introductory general biology course during two consecutive semesters at an urban community college. Initial data was gathered during the first week of the semester, at which time students filled in a short questionnaire (age, gender, number of college credits, prior exposure to science information either through reading/visual sources or a prior biology course). Subjects were then given the Inventory of Learning Processes-Revised (ILP-R) which measures general preferences in five learning styles; Deep Learning; Elaborative Learning, Agentic Learning, Methodical Learning and Literal Memorization. Subjects were then given the Gene Conceptual Knowledge pretest: a 15 question objective section and an essay section. Subjects were exposed to specific concepts during lecture and laboratory exercises. At the last lab, students were given the Genetics Conceptual Knowledge Posttest. Pretest/posttest gains were correlated with demographic variables and learning styles were analyzed for significant correlations. Learning styles, as the independent variable in a simultaneous multiple regression, were significant predictors of results on the gene assessment tests, including pretest, posttest and gain. Of the learning styles, Deep Learning accounted for the greatest positive predictive value of pretest essay and pretest objective results. Literal Memorization was a significant negative predictor for posttest essay, essay gain and objective gain. Simultaneous multiple regression indicated that demographic variables were significant positive predictors for Methodical, Deep and Elaborative Learning Styles. Stepwise multiple regression resulted in number of credits, Read Science and gender (female) as significant predictors of learning styles. The findings of this study emphasize the importance of learning styles in conceptual understanding of the gene and the correlation of nonformal exposure to science information with learning style and conceptual understanding.

Maslow and Motherboards: Taking a Hierarchical View of Technology Planning.

ERIC Educational Resources Information Center

Johnson, Doug

2003-01-01

Presents a planning model for educational uses of technology that is based on Maslow's hierarchy of needs. Topics include established infrastructure; effective administration; extensive resources; enhanced teaching, including creating distance learning opportunities; empowered students, including evaluation methods and information literacy skills;…

The effects of academic literacy instruction on engagement and conceptual understanding of biology of ninth-grade students

NASA Astrophysics Data System (ADS)

Larson, Susan C.

Academic language, discourse, vocabulary, motivation, and comprehension of complex texts and concepts are keys to learning subject-area content. The need for a disciplinary literacy approach in high school classrooms accelerates as students become increasing disengaged in school and as content complexity increases. In the present quasi-experimental mixed-method study, a ninth-grade biology unit was designed with an emphasis on promoting academic literacy skills, discourse, meaningful constructivist learning, interest development, and positive learning experiences in order to learn science content. Quantitative and qualitative analyses on a variety of measures completed by 222 students in two high schools revealed that those who received academic literacy instruction in science class performed at significantly higher levels of conceptual understanding of biology content, academic language and vocabulary use, reasoned thought, engagement, and quality of learning experience than control-group students receiving traditionally-organized instruction. Academic literacy was embedded into biology instruction to engage students in meaning-making discourses of science to promote learning. Academic literacy activities were organized according the phases of interest development to trigger and sustain interest and goal-oriented engagement throughout the unit. Specific methods included the Generative Vocabulary Matrix (GVM), scenario-based writing, and involvement in a variety of strategically-placed discourse activities to sustain or "boost" engagement for learning. Traditional instruction for the control group included teacher lecture, whole-group discussion, a conceptual organizer, and textbook reading. Theoretical foundations include flow theory, sociocultural learning theory, and interest theory. Qualitative data were obtained from field notes and participants' journals. Quantitative survey data were collected and analyzed using the Experience Sampling Method (ESM) to measure cognitive and emotional states, revealing patterns of engagement, quality of experience, and flow over the course of the instructional unit. Conceptual understanding was measured using the state persuasive writing rubric to analyze science essays in which students supported a claim with scientific evidence. The study contributes an Engagement Model of Academic Literacy for Learning (EngageALL), a Rubric for Academic Persuasive Writing (RAPW), a unique classification system for analyzing academic vocabulary, and suggestions for situated professional development around a research-based planning framework. A discussion addresses a new direction for future research that explores academic identity development.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

A Novel Teaching Tool Combined With Active-Learning to Teach Antimicrobial Spectrum Activity.

PubMed

MacDougall, Conan

2017-03-25

Objective. To design instructional methods that would promote long-term retention of knowledge of antimicrobial pharmacology, particularly the spectrum of activity for antimicrobial agents, in pharmacy students. Design. An active-learning approach was used to teach selected sessions in a required antimicrobial pharmacology course. Students were expected to review key concepts from the course reader prior to the in-class sessions. During class, brief concept reviews were followed by active-learning exercises, including a novel schematic method for learning antimicrobial spectrum of activity ("flower diagrams"). Assessment. At the beginning of the next quarter (approximately 10 weeks after the in-class sessions), 360 students (three yearly cohorts) completed a low-stakes multiple-choice examination on the concepts in antimicrobial spectrum of activity. When data for students was pooled across years, the mean number of correct items was 75.3% for the items that tested content delivered with the active-learning method vs 70.4% for items that tested content delivered via traditional lecture (mean difference 4.9%). Instructor ratings on student evaluations of the active-learning approach were high (mean scores 4.5-4.8 on a 5-point scale) and student comments were positive about the active-learning approach and flower diagrams. Conclusion. An active-learning approach led to modestly higher scores in a test of long-term retention of pharmacology knowledge and was well-received by students.

A Novel Teaching Tool Combined With Active-Learning to Teach Antimicrobial Spectrum Activity

PubMed Central

2017-01-01

Objective. To design instructional methods that would promote long-term retention of knowledge of antimicrobial pharmacology, particularly the spectrum of activity for antimicrobial agents, in pharmacy students. Design. An active-learning approach was used to teach selected sessions in a required antimicrobial pharmacology course. Students were expected to review key concepts from the course reader prior to the in-class sessions. During class, brief concept reviews were followed by active-learning exercises, including a novel schematic method for learning antimicrobial spectrum of activity (“flower diagrams”). Assessment. At the beginning of the next quarter (approximately 10 weeks after the in-class sessions), 360 students (three yearly cohorts) completed a low-stakes multiple-choice examination on the concepts in antimicrobial spectrum of activity. When data for students was pooled across years, the mean number of correct items was 75.3% for the items that tested content delivered with the active-learning method vs 70.4% for items that tested content delivered via traditional lecture (mean difference 4.9%). Instructor ratings on student evaluations of the active-learning approach were high (mean scores 4.5-4.8 on a 5-point scale) and student comments were positive about the active-learning approach and flower diagrams. Conclusion. An active-learning approach led to modestly higher scores in a test of long-term retention of pharmacology knowledge and was well-received by students. PMID:28381885

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

Use of a machine learning framework to predict substance use disorder treatment success

PubMed Central

Kelmansky, Diana; van der Laan, Mark; Sahker, Ethan; Jones, DeShauna; Arndt, Stephan

2017-01-01

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed. PMID:28394905

Use of a machine learning framework to predict substance use disorder treatment success.

PubMed

Acion, Laura; Kelmansky, Diana; van der Laan, Mark; Sahker, Ethan; Jones, DeShauna; Arndt, Stephan

2017-01-01

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

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.

Construction of a model demonstrating neural pathways and reflex arcs.

PubMed

Chan, V; Pisegna, J M; Rosian, R L; DiCarlo, S E

1996-12-01

Employment opportunities in the future will require higher skills and an understanding of mathematics and science. As a result of the growing number of careers that require solid science and mathematics training, the methods of science education are undergoing major reform. To adequately equip students for technologically advanced positions, new teaching methods must be developed that prepare tomorrow's workforce for the challenges of the 21st century. One such method is the use of models. By actively building and manipulating concrete models that represent scientific concepts, students are involved in the most basic level of Piaget's learning scheme: the sensorimotor stage. Models are useful in reaching all students at the foundational levels of learning, and further learning experiences are rapidly moved through higher learning levels. This success ensures greater comprehension and understanding compared with the traditional methods of rote memorization. We developed an exercise for the construction of an inexpensive, easy-to-build model demonstrating neural pathways and reflex arcs. Our exercise also includes many supplemental teaching tools. The exercise is designed to fulfill the need of sound physiological teaching materials for high school students.

Pressure Ulcer Prevention: Where Practice and Education Meet.

PubMed

Bos, Brenda S; Wangen, Tina M; Elbing, Carl E; Rowekamp, Debra J; Kruggel, Heather A; Conlon, Patricia M; Scroggins, Leann M; Schad, Shauna P; Neumann, Julie A; Barth, Melissa M; Grubbs, Pamela L; Sievers, Beth A

2016-01-01

This article describes the processes used to implement a pressure ulcer management program in a Midwest academic medical center, which led to a decrease in reportable pressure ulcers. A learning needs assessment was completed, and a workgroup was formed to address the learning needs. Methods, materials, and processes included lectures, technology-enhanced learning, and interactive stations with mannequins and pressure ulcer moulages. The processes and outcome measures used to measure effectiveness of the program are discussed.

Analysis of precision and accuracy in a simple model of machine learning

NASA Astrophysics Data System (ADS)

Lee, Julian

2017-12-01

Machine learning is a procedure where a model for the world is constructed from a training set of examples. It is important that the model should capture relevant features of the training set, and at the same time make correct prediction for examples not included in the training set. I consider the polynomial regression, the simplest method of learning, and analyze the accuracy and precision for different levels of the model complexity.

Mentoring, coaching and action learning: interventions in a national clinical leadership development programme.

PubMed

McNamara, Martin S; Fealy, Gerard M; Casey, Mary; O'Connor, Tom; Patton, Declan; Doyle, Louise; Quinlan, Christina

2014-09-01

To evaluate mentoring, coaching and action learning interventions used to develop nurses' and midwives' clinical leadership competencies and to describe the programme participants' experiences of the interventions. Mentoring, coaching and action learning are effective interventions in clinical leadership development and were used in a new national clinical leadership development programme, introduced in Ireland in 2011. An evaluation of the programme focused on how participants experienced the interventions. A qualitative design, using multiple data sources and multiple data collection methods. Methods used to generate data on participant experiences of individual interventions included focus groups, individual interviews and nonparticipant observation. Seventy participants, including 50 programme participants and those providing the interventions, contributed to the data collection. Mentoring, coaching and action learning were positively experienced by participants and contributed to the development of clinical leadership competencies, as attested to by the programme participants and intervention facilitators. The use of interventions that are action-oriented and focused on service development, such as mentoring, coaching and action learning, should be supported in clinical leadership development programmes. Being quite different to short attendance courses, these interventions require longer-term commitment on the part of both individuals and their organisations. In using mentoring, coaching and action learning interventions, the focus should be on each participant's current role and everyday practice and on helping the participant to develop and demonstrate clinical leadership skills in these contexts. © 2014 John Wiley & Sons Ltd.

Active learning of introductory optics: real-time physics labs, interactive lecture demonstrations and magic

NASA Astrophysics Data System (ADS)

Sokoloff, David R.

2005-10-01

Widespread physics education research has shown that most introductory physics students have difficulty learning essential optics concepts - even in the best of traditional courses, and that well-designed active learning approaches can remedy this problem. This mini-workshop and the associated poster session will provide direct experience with methods for promoting students' active involvement in the learning process in lecture and laboratory. Participants will have hands-on experience with activities from RealTime Physics labs and Interactive Lecture Demonstrations - a learning strategy for large (and small) lectures, including specially designed Optics Magic Tricks. The poster will provide more details on these highly effective curricula.

Nordic-Baltic Student Teachers' Identification of and Interest in Plant and Animal Species: The Importance of Species Identification and Biodiversity for Sustainable Development

NASA Astrophysics Data System (ADS)

Palmberg, Irmeli; Berg, Ida; Jeronen, Eila; Kärkkäinen, Sirpa; Norrgård-Sillanpää, Pia; Persson, Christel; Vilkonis, Rytis; Yli-Panula, Eija

2015-10-01

Knowledge of species, interest in nature, and nature experiences are the factors that best promote interest in and understanding of environmental issues, biodiversity and sustainable life. The aim of this study is to investigate how well student teachers identify common local species, their interest in and ideas about species identification, and their perceptions of the importance of species identification and biodiversity for sustainable development. Totally 456 student teachers for primary schools were tested using an identification test and a questionnaire consisting of fixed and open questions. A combination of quantitative and qualitative methods was used to get a more holistic view of students' level of knowledge and their preferred learning methods. The student teachers' ability to identify very common species was low, and only 3 % were able to identify most of the tested species. Experiential learning outdoors was suggested by the majority of students as the most efficient learning method, followed by experiential learning indoors, project work and experimental learning. They looked upon the identification of plants and animals as `important' or `very important' for citizens today and for sustainable development. Likewise, they looked upon biodiversity as `important' or `very important' for sustainable development. Our conclusion is that teaching and learning methods for identification and knowledge of species and for education of biodiversity and sustainable development should always include experiential and project-based methods in authentic environments.

Technology-based vs. traditional instruction. A comparison of two methods for teaching the skill of performing a 12-lead ECG.

PubMed

Jeffries, Pamela R; Woolf, Shirley; Linde, Beverly

2003-01-01

The purpose of this study was to compare the effectiveness of an interactive, multimedia CD-ROM with traditional methods of teaching the skill of performing a 12-lead ECG. A randomized pre/posttest experimental design was used. Seventy-seven baccalaureate nursing students in a required, senior-level critical-care course at a large midwestern university were recruited for the study. Two teaching methods were compared. The traditional method included a self-study module, a brief lecture and demonstration by an instructor, and hands-on experience using a plastic manikin and a real 12-lead ECG machine in the learning laboratory. The second method covered the same content using an interactive, multimedia CD-ROM embedded with virtual reality and supplemented with a self-study module. There were no significant (p < .05) baseline differences in pretest scores between the two groups and no significant differences by group in cognitive gains, student satisfaction with their learning method, or perception of self-efficacy in performing the skill. Overall results indicated that both groups were satisfied with their instructional method and were similar in their ability to demonstrate the skill correctly on a live, simulated patient. This evaluation study is a beginning step to assess new and potentially more cost-effective teaching methods and their effects on student learning outcomes and behaviors, including the transfer of skill acquisition via a computer simulation to a real patient.

Assessment of Cognitive Communications Interest Areas for NASA Needs and Benefits

NASA Technical Reports Server (NTRS)

Knoblock, Eric J.; Madanayake, Arjuna

2017-01-01

This effort provides a survey and assessment of various cognitive communications interest areas, including node-to-node link optimization, intelligent routing/networking, and learning algorithms, and is conducted primarily from the perspective of NASA space communications needs and benefits. Areas of consideration include optimization methods, learning algorithms, and candidate implementations/technologies. Assessments of current research efforts are provided with mention of areas for further investment. Other considerations, such as antenna technologies and cognitive radio platforms, are briefly provided as well.

A combined learning algorithm for prostate segmentation on 3D CT images.

PubMed

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2017-11-01

Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate. © 2017 American Association of Physicists in Medicine.

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Optimizing How We Teach Research Methods

ERIC Educational Resources Information Center

Cvancara, Kristen E.

2017-01-01

Courses: Research Methods (undergraduate or graduate level). Objective: The aim of this exercise is to optimize the ability for students to integrate an understanding of various methodologies across research paradigms within a 15-week semester, including a review of procedural steps and experiential learning activities to practice each method, a…

Strategy to Promote Active Learning of an Advanced Research Method

ERIC Educational Resources Information Center

McDermott, Hilary J.; Dovey, Terence M.

2013-01-01

Research methods courses aim to equip students with the knowledge and skills required for research yet seldom include practical aspects of assessment. This reflective practitioner report describes and evaluates an innovative approach to teaching and assessing advanced qualitative research methods to final-year psychology undergraduate students. An…

75 FR 13515 - Office of Innovation and Improvement (OII); Overview Information; Ready-to-Learn Television...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-22

... on rigorous scientifically based research methods to assess the effectiveness of a particular... activities and programs; and (B) Includes research that-- (i) Employs systematic, empirical methods that draw... or observational methods that provide reliable and valid data across evaluators and observers, across...

Intercultural Sourcebook: Cross-Cultural Training Methods. Volume 2.

ERIC Educational Resources Information Center

Fowler, Sandra M., Ed.; Mumford, Monica G., Ed.

This comprehensive collection of training methods and exercises used by top trainers in the cross-cultural field contains resources essential for cross-cultural learning. This second volume of the collection includes articles by 34 leading cross-cultural trainers and covers new or divergent training methods for cross-cultural skill development and…

Integration of Traditional and E-Learning Methods to Improve Learning Outcomes for Dental Students in Histopathology.

PubMed

Ariana, Armin; Amin, Moein; Pakneshan, Sahar; Dolan-Evans, Elliot; Lam, Alfred K

2016-09-01

Dental students require a basic ability to explain and apply general principles of pathology to systemic, dental, and oral pathology. Although there have been recent advances in electronic and online resources, the academic effectiveness of using self-directed e-learning tools in pathology courses for dental students is unclear. The aim of this study was to determine if blended learning combining e-learning with traditional learning methods of lectures and tutorials would improve students' scores and satisfaction over those who experienced traditional learning alone. Two consecutive cohorts of Bachelor of Dentistry and Oral Health students taking the general pathology course at Griffith University in Australia were compared. The control cohort experienced traditional methods only, while members of the study cohort were also offered self-directed learning materials including online resources and online microscopy classes. Final assessments for the course were used to compare the differences in effectiveness of the intervention, and students' satisfaction with the teaching format was evaluated using questionnaires. On the final course assessments, students in the study cohort had significantly higher scores than students in the control cohort (p<0.01). Analysis of questionnaire results showed improved student satisfaction with the course in the study cohort. These findings suggest that the use of e-learning tools such as virtual microscopy and interactive online resources for delivering pathology instruction can be an effective supplement for developing dental students' competence, confidence, and satisfaction.

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.

Health promotion in medical education: lessons from a major undergraduate curriculum implementation.

PubMed

Wylie, Ann; Leedham-Green, Kathleen

2017-11-01

Despite the economic, environmental and patient-related imperatives to prepare medical students to become health promoting doctors, health promotion remains relatively deprioritised in medical curricula. This paper uses an in-depth case study of a health promotion curriculum implementation at a large UK medical school to provide insights into the experiences of teachers and learners across a range of topics, pedagogies, and teaching & assessment modalities. Topics included smoking cessation, behavioural change approaches to obesity, exercise prescribing, social prescribing, maternal and child health, public and global health; with pedagogies ranging from e-learning to practice-based project work. Qualitative methods including focus groups, analysis of reflective learning submissions, and evaluation data are used to illuminate motivations, frustrations, practicalities, successes and limiting factors. Over this three year implementation, a range of challenges have been highlighted including: how adequately to prepare and support clinical teachers; the need to establish relevance and importance to strategic learners; the need for experiential learning in clinical environments to support classroom-based activities; and the need to rebalance competing aspects of the curriculum. Conclusions are drawn about heterogeneous deep learning over standardised surface learning, and the impacts, both positive and negative, of different assessment modalities on these types of learning.

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

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Home Care Learning Model for Medical Students in Chile: A Mixed Methods Study

PubMed Central

Gonzalez, Carolina

2014-01-01

Introduction. The relevance of home care training is not questioned. However, there are no reported learning models to teach in this setting. Aims. To develop and evaluate a learning model to teach home care to medical students. Methods. Stage 1: Learning Model Design. Tutors teaching home care and a sample of medical students were invited to focus groups analyzed according to the grounded theory. Later, the researchers designed the learning model, which was approved by all participants. Stage 2: Learning Assessment. All students in their family medicine internship at Pontificia Universidad Catolica de Chile were invited to participate in a nonrandomized before-and-after pilot trial, assessing changes in their perception towards home care and satisfaction with the learning model. Results. Stage 1: Six tutors and eight students participated in the focus groups. The learning model includes activities before, during, and after the visits. Stage 2: 105 students (88.2%) participated. We observed improvement in all home care training domains (P ≤ 0.001) and a high satisfaction with the model. Students with previous home visit experiences and who participated with nurses and social workers reported more learning. Conclusions. We report an effective learning model to train medical students in home care. Limitations and recommendations for future studies are discussed. PMID:24967327

Learning style preferences of nursing students at two universities in Iran and Malaysia

PubMed Central

Abdollahimohammad, Abdolghani; Ja’afar, Rogayah

2014-01-01

Purpose: Learning style preferences vary within the nursing field and there is no consensus on a predominant learning style preference in nursing students. The current study compared the learning style preferences of nursing students at two universities in Iran and Malaysia. Methods: A purposive sampling method was used to collect data from the two study populations. Data were collected using the Learning Style Scale (LSS), which is a valid and reliable inventory. The LSS consists of 22 items with five subscales including perceptive, solitary, analytic, imaginative, and competitive. The questionnaires were distributed at the end of the academic year during regular class time for optimum response. The Mann-Whitney U-test was used to compare the learning style preferences between the two study populations. Results: A significant difference was found in perceptive, solitary, and analytic learning styles between two groups of nursing students. However, there was no significant difference in imaginative and competitive learning styles between the two groups. Most of the students were in the middle range of the learning styles. Conclusion: There were similarities and differences in learning style preferences between Zabol Medical Sciences University (ZBMU) and University Sains Malaysia (USM) nursing students. The USM nursing students were more sociable and analytic learners, whereas the ZBMU nursing students were more solitary and perceptive learners. PMID:25417864

Is the Recall of Verbal-Spatial Information from Working Memory Affected by Symptoms of ADHD?

ERIC Educational Resources Information Center

Caterino, Linda C.; Verdi, Michael P.

2012-01-01

Objective: The Kulhavy model for text learning using organized spatial displays proposes that learning will be increased when participants view visual images prior to related text. In contrast to previous studies, this study also included students who exhibited symptoms of ADHD. Method: Participants were presented with either a map-text or…

Latent Learning in the Work Place: The Placement Experiences of Student-Coaches

ERIC Educational Resources Information Center

Gomes, Rúben; Jones, Robyn L.; Batista, Paula; Mesquita, Isabel

2018-01-01

The aim of this study was to investigate the work-based internship experiences of eight student-coaches. This was particularly in terms of what precisely such coaches learned within the practical context, and how they engaged with unexpected situational events. The methods employed within the project included focus group interviews and participant…

An Exploration of E-Learning Benefits for Saudi Arabia: Toward Policy Reform

ERIC Educational Resources Information Center

Alrashidi, Abdulaziz

2013-01-01

Purpose: The purpose of this study was to examine policies and solutions addressing (a) improving education for citizens of the Kingdom of Saudi Arabia and (b) providing alternative instructional delivery methods, including e-learning for those living in remote areas. Theoretical Framework: The theoretical framework of this study was based on the…

How Does the Legal System Respond when Children with Learning Difficulties Are Victimized?

ERIC Educational Resources Information Center

Cederborg, Ann-Christin; Lamb, Michael E.

2006-01-01

Objective: To understand how the Swedish legal system perceives and handles mentally handicapped children who may have been victimized. Method: Twenty-two judicial districts in Sweden provided complete files on 39 District Court cases (including the Appeals Court files on 17 of these cases) involving children with learning difficulties or other…

Differentiating Delivery of Instruction with Online Learning Modules for Teacher Candidates

ERIC Educational Resources Information Center

Wilkinson, Colleen Ann

2013-01-01

Online learning has become a prevalent method of instruction in higher education. There are many reasons for this change in pedagogy, including rapid developments in technology, as well as the logistic challenges of enrolling in college, such as commuting and coordinating work schedules. The quality of online instruction and its impact on teacher…

The Journal of the Society for Accelerative Learning and Teaching. (Volume 5, Numbers 3 through Volume 6, Number 4).

ERIC Educational Resources Information Center

Journal of the Society for Accelerative Learning and Teaching, 1981

1981-01-01

Numbers 3 and 4 of volume 5 and numbers 1 through 4 of volume 6 of the journal, spanning fall 1980 through winter 1981, include articles concerning the individualized study center; consciousness, psychology, and education; suggestive-accelerative learning and suggestopedia; creativity; brain lateralization; the Lozanov method; biofeedback and…

An Effective Online Teaching Method: The Combination of Collaborative Learning with Initiation and Self-Regulation Learning with Feedback

ERIC Educational Resources Information Center

Tsai, Chia-Wen

2013-01-01

In modern business environments, work and tasks have become more complex and require more interdisciplinary skills to complete, including collaborative and computing skills for website design. However, the computing education in Taiwan can hardly be recognised as effective in developing and transforming students into competitive employees. In this…

Multimedia Environments in Mathematics Teacher Education: Preparing Regular and Special Educators for Inclusive Classrooms

ERIC Educational Resources Information Center

De La Paz, Susan; Hernandez-Ramos, Pedro; Barron, Linda

2004-01-01

A multimedia CD-ROM program, Mathematics Teaching and Learning in Inclusive Classrooms, was produced to help preservice teachers learn mathematics teaching methods in the context of inclusive classrooms. The contents include text resources, video segments of experts and of classroom lessons, images of student work, an electronic notebook, and a…

Relationship between Learning Problems and Attention Deficit in Childhood

ERIC Educational Resources Information Center

Ponde, Milena Pereira; Cruz-Freire, Antonio Carlos; Silveira, Andre Almeida

2012-01-01

Objective: To assess the impact of attention deficit on learning problems in a sample of schoolchildren in the city of Salvador, Bahia, Brazil. Method: All students enrolled in selected elementary schools were included in this study, making a total of 774 children. Each child was assessed by his or her teacher using a standardized scale. "The…

YouTube, Fanvids, Forums, Vlogs and Blogs: Informal Music Learning in a Convergent on- and Offline Music Community

ERIC Educational Resources Information Center

Waldron, Janice

2013-01-01

In this paper I examine the music learning and teaching in the Banjo Hangout online music community (www.banjohangout.org/) using cyber ethnographic methods of interview and participant observation conducted entirely through computer-mediated communication, which includes Skype and written narrative texts--forum posts, email, chat room…

The Challenges of Adopting the Learning Organisation Philosophy in a Singapore School

ERIC Educational Resources Information Center

Retna, Kala S.; Tee, Ng Pak

2006-01-01

Purpose: To report on a case study that examines how the Learning Organisation (LO) concept can be applied in a Singapore school and the challenges that the school faces in the process. Design/methodology/approach: A qualitative research inquiry was adopted using ethnographic methods. Data includes in-depth face-to-face interviews, observation of…

Methods of Work with Pupils-Immigrants at Russian Language Lessons in Primary School

ERIC Educational Resources Information Center

Zakirova, Venera G.; Kamalova, Lera A.

2016-01-01

In this article, the authors begin by outlining the basic principles of teaching children-migrants at the elementary school level. These principles include: (1) Learning Russian is focused on the development of children's ability to communicate; (2) Language is learned by migrant children as a mean of communication; (3) Students can see the…

The Effect of Self-Regulated Learning on Sixth-Grade Turkish Students' Mathematics Achievements and Motivational Beliefs

ERIC Educational Resources Information Center

Yidizli, Hülya; Saban, Ahmet

2016-01-01

This study examined the effect of self-regulated learning on sixth-grade Turkish students' mathematics achievements and motivational beliefs. Both quantitative and qualitative research methods were used in the study. Participants included sixth-grade students attending at TOKI 125. Year Middle School in Nevsehir (Turkey) during the 2014-2015…

Racing in the Marathon or Traveling to a Mysterious Place: Prospective Primary Teachers' Conceptions of Experienced and Ideal Learning

ERIC Educational Resources Information Center

Saban, Ahmet; Koçbeker-Eid, Beyhan Nazli; Saban, Aslihan

2014-01-01

In this study, Turkish primary teacher candidates' experienced and ideal conceptions of learning were examined through metaphors. The participants of this phenomenological study included 193 sophomores taking the "Principles and Methods of Teaching" course at Ahmet Kelesoglu Education Faculty, Necmettin Erbakan University, in the fall of…

The Nature and Use of Individualized Learning Plans as a Promising Career Intervention Strategy

ERIC Educational Resources Information Center

Solberg, V. Scott; Phelps, L. Allen; Haakenson, Kristin A.; Durham, Julie F.; Timmons, Joe

2012-01-01

Individualized learning plans (ILPs) are being implemented in high schools throughout the United States as strategic planning tools that help students align course plans with career aspirations and often include the development of postsecondary plans. Initial indications are that ILPs may be an important method for helping students achieve both…

New Learning Method of a Lecture of ‘Machine Fabrication’ by Self-study with Investigation and Presentation Incorporated

NASA Astrophysics Data System (ADS)

Kasuga, Yukio

A new teaching method was developed in learning ‘machine fabrication’ for the undergraduate students. This consists of a few times of lectures, grouping, decision of industrial products which each group wants to investigate, investigation work by library books and internet, arrangement of data containing characteristics of the products, employed materials and processing methods, presentation, discussions and revision followed by another presentation. This new method is derived from one of the Finland‧s way of primary school education. Their way of education is believed to have boosted up to the top ranking in PISA tests by OECD. After starting the new way of learning, students have fresh impressions on this lesson, especially for self-study, the way of investigation, collaborate work and presentation. Also, after four years of implementation, some improvements have been made including less use of internet, and determination of products and fabricating methods in advance which should be investigated. By this, students‧ lecture assessment shows further encouraging results.

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.

The charismatic journey of mastery learning.

PubMed

Inui, Thomas S

2015-11-01

A collection of articles in this issue examine the concept of mastery learning, underscoring that our journey is from a 19th-century construct for assuring skill development (i.e., completing a schedule of rotations driven by the calendar) to a 21st-century sequence of learning opportunities focused on acquiring mastery of special key competencies within clerkships or other activities. Mastery learning processes and standards have the potential to clarify learning goals and competency measurement issues in medical education. Although mastery learning methods originally focused on developing learners' competency with skillful procedures, the author of this Commentary posits that mastery learning methods may be usefully applied more extensively to broader domains of skillful practice, especially those practices that can be linked to outcomes of care. The transition to mastery-focused criteria for educational advancement is laudatory, but challenges will be encountered in the journey to mastery education. The author examines several of these potential challenges, including expansion of mastery learning approaches to effective but relational clinician advice-giving and counseling behaviors, developing criteria for choosing critical competencies that can be linked to outcomes, avoiding a excessively fragmented approach to mastery measurement, and dealing with "educational comorbidity."

Exploring MEDLINE Space with Random Indexing and Pathfinder Networks

PubMed Central

Cohen, Trevor

2008-01-01

The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search. PMID:18999236

Exploring MEDLINE space with random indexing and pathfinder networks.

PubMed

Cohen, Trevor

2008-11-06

The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search.

Other Resources Related to SAM

EPA Pesticide Factsheets

Learn more about websites and information related to EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM), including key EPA collaborators, laboratories, and research centers.

Learning challenges of nursing students in clinical environments: A qualitative study in Iran

PubMed Central

Baraz, Shahram; Memarian, Robabeh; Vanaki, Zohreh

2015-01-01

Background: Clinical learning environment is a complex social entity. This environment is effective on the learning process of nursing students in the clinical area. However, learning in clinical environment has several benefits, but it can be challenging, unpredictable, stressful, and constantly changing. In attention to clinical experiences and factors contributing to the learning of these experiences can waste a great deal of time and energy, impose heavy financial burden on educational systems, cause mental, familial and educational problems for students, and compromise the quality of patient care. Therefore, this study was carried out with the goal of determining the learning challenges of nursing students in clinical environments in Iran. Materials and Methods: In this qualitative study carried out in 2012–2013, 18 undergraduate nursing students were selected by using purposive sampling method from the Faculty of Nursing and Midwifery of Tehran and Shahid Beheshti Universities. Semi-structured interviews were used to collect data. The content analysis method was used to determine relevant themes. Results: Two themes were derived from the data analysis, which represented the students’ clinical learning challenges. These two themes included insufficient qualification of nursing instructors and unsupportive learning environment. Conclusions: Identification of the students’ clinical learning challenges and actions to remove or modify them will create more learning opportunities for the students, improve the achievement of educational goals, provide training to nursing students with the needed competencies to meet the complex demands of caring and for application of theories in practice, and improve the quality of healthcare services. PMID:26430679

Algorithm for personal identification in distance learning system based on registration of keyboard rhythm

NASA Astrophysics Data System (ADS)

Nikitin, P. V.; Savinov, A. N.; Bazhenov, R. I.; Sivandaev, S. V.

2018-05-01

The article describes the method of identifying a person in distance learning systems based on a keyboard rhythm. An algorithm for the organization of access control is proposed, which implements authentication, identification and verification of a person using the keyboard rhythm. Authentication methods based on biometric personal parameters, including those based on the keyboard rhythm, due to the inexistence of biometric characteristics without a particular person, are able to provide an advanced accuracy and inability to refuse authorship and convenience for operators of automated systems, in comparison with other methods of conformity checking. Methods of permanent hidden keyboard monitoring allow detecting the substitution of a student and blocking the key system.

Internet-based learning and applications for critical care medicine.

PubMed

Wolbrink, Traci A; Burns, Jeffrey P

2012-01-01

Recent changes in duty hour allowances and economic constraints are forcing a paradigm shift in graduate medical education in the United States. Internet-based learning is a rapidly growing component of postgraduate medical education, including the field of critical care medicine. Here, we define the key concepts of Internet-based learning, summarize the current literature, and describe how Internet-based learning may be uniquely suited for the critical care provider. A MEDLINE/PubMed search from January 2000 to July 2011 using the search terms: "e-learning," "Web-based learning," "computer-aided instruction," "adult learning," "knowledge retention," "intensive care," and "critical care." The growth of the Internet is marked by the development of new technologies, including more user-derived tools. Nonmedical fields have embraced Internet-based learning as a valuable teaching tool. A recent meta-analysis described Internet-based learning in the medical field as being more effective than no intervention and likely as efficacious as traditional teaching methods. Web sites containing interactive features are aptly suited for the adult learner, complementing the paradigm shift to more learner-centered education. Interactive cases, simulators, and games may allow for improvement in clinical care. The total time spent utilizing Internet-based resources, as well as the frequency of returning to those sites, may influence educational gains. Internet-based learning may provide an opportunity for assistance in the transformation of medical education. Many features of Web-based learning, including interactivity, make it advantageous for the adult medical learner, especially in the field of critical care medicine, and further work is necessary to develop a robust learning platform incorporating a variety of learning modalities for critical care providers.

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

NETS, software tool for development and evaluation of neural networks, provides simulation of neural-network algorithms plus computing environment for development of such algorithms. Uses back-propagation learning method for all of networks it creates. Enables user to customize patterns of connections between layers of network. Also provides features for saving, during learning process, values of weights, providing more-precise control over learning process. Written in ANSI standard C language. Machine-independent version (MSC-21588) includes only code for command-line-interface version of NETS 3.0.

Safety leadership and systems thinking: application and evaluation of a Risk Management Framework in the mining industry.

PubMed

Donovan, Sarah-Louise; Salmon, Paul M; Lenné, Michael G; Horberry, Tim

2017-10-01

Safety leadership is an important factor in supporting safety in high-risk industries. This article contends that applying systems-thinking methods to examine safety leadership can support improved learning from incidents. A case study analysis was undertaken of a large-scale mining landslide incident in which no injuries or fatalities were incurred. A multi-method approach was adopted, in which the Critical Decision Method, Rasmussen's Risk Management Framework and Accimap method were applied to examine the safety leadership decisions and actions which enabled the safe outcome. The approach enabled Rasmussen's predictions regarding safety and performance to be examined in the safety leadership context, with findings demonstrating the distribution of safety leadership across leader and system levels, and the presence of vertical integration as key to supporting the successful safety outcome. In doing so, the findings also demonstrate the usefulness of applying systems-thinking methods to examine and learn from incidents in terms of what 'went right'. The implications, including future research directions, are discussed. Practitioner Summary: This paper presents a case study analysis, in which systems-thinking methods are applied to the examination of safety leadership decisions and actions during a large-scale mining landslide incident. The findings establish safety leadership as a systems phenomenon, and furthermore, demonstrate the usefulness of applying systems-thinking methods to learn from incidents in terms of what 'went right'. Implications, including future research directions, are discussed.

Junior doctors' guide to portfolio learning and building.

PubMed

Kitchen, Mark

2012-10-01

A portfolio is a collection of evidence supporting an individual's achievement of competencies and learning outcomes. The material included in the portfolio must be reflected upon, as reflection provides the evidence that learning has taken place. Portfolio learning is important for two principal reasons: assessment of the trainee, and for lifelong learning and reflection. The ability of a portfolio to be used for both summative and formative assessment makes it a flexible and robust assessment method. A portfolio also demonstrates reflection and lifelong learning abilities. Reflective learning is key to postgraduate medical education: it is part of both the Foundation Programme curriculum and General Medical Council guidance on best practice. To ensure correct learning outcomes are identified and evidenced, the curriculum programme must be referred to and an educational supervisor should be consulted. Once identified, it is necessary to: identify how these outcomes can be met (learning needs); decide what needs to be done to meet these needs; reflect on what has been done; and evidence what has been done in the portfolio. Evidence could include written feedback, certificates of course completion, online learning modules, etc. A learning portfolio is a necessary tool for every postgraduate medical trainee. The portfolio serves to record and evidence all learning that has taken place, and thereon acts as a guide for future learning needs. The key process to portfolio building and learning is the provision of evidence by reflecting upon the learning that has taken place. © Blackwell Publishing Ltd 2012.

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

PubMed

VoPham, Trang; Hart, Jaime E; Laden, Francine; Chiang, Yao-Yi

2018-04-17

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

Is Self-Paced Instruction Really Worth It?

ERIC Educational Resources Information Center

Roberson, J. A.; Crowe, C. T.

1975-01-01

Describes a self-paced, learning-for-mastery course in undergraduate fluid mechanics. Includes the method of course assessment, method of student evaluation, and a description of the instructor's role and work load. Summarizes aspects of self-paced instruction considered favorable and unfavorable. (GS)

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

PubMed

Sajid, Muhammad R; Laheji, Abrar F; Abothenain, Fayha; Salam, Yezan; AlJayar, Dina; Obeidat, Akef

2016-09-04

To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Evaluation of an interprofessional educational curriculum pilot course for practitioners working with post-stroke patients.

PubMed

Olaisen, Rho Henry; Mariscal-Hergert, Cheryl; Shaw, Alissa; Macchiavelli, Cecilia; Marsheck, Joanna

2014-03-01

This report describes the design and evaluation of an interprofessional pilot training course aimed at pre-licensure practitioners working with post-stroke patients in community-based settings. The course was developed by community-based practitioners from nine health professions. Course learning activities included traditional methods (lectures) and interactive modules (problem-based learning and exchange-based learning). The study's aim was to assess the program's effectiveness in adapting and incorporating knowledge, skills and self-confidence when delivering tertiary care in therapeutic pool environments; gauge adoption of course principles into practice, and assess overall course satisfaction. Methods of evaluation included conceptual mapping of course format, pre- and post-questionnaires, daily reflection questionnaires, course satisfaction survey and adoption survey, 10 weeks follow-up. Overall, the findings indicate students' knowledge, skills and self-confidence in delivering effective post-stroke care increased following the training. Students reported adopting clinical practices in 10 weeks follow-up. Implications for designing interprofessional curricula are discussed.

Can learning style predict student satisfaction with different instruction methods and academic achievement in medical education?

PubMed

Gurpinar, Erol; Alimoglu, Mustafa Kemal; Mamakli, Sumer; Aktekin, Mehmet

2010-12-01

The curriculum of our medical school has a hybrid structure including both traditional training (lectures) and problem-based learning (PBL) applications. The purpose of this study was to determine the learning styles of our medical students and investigate the relation of learning styles with each of satisfaction with different instruction methods and academic achievement in them. This study was carried out with the participation of 170 first-year medical students (the participation rate was 91.4%). The researchers prepared sociodemographic and satisfaction questionnaires to determine the characteristics of the participants and their satisfaction levels with traditional training and PBL. The Kolb learning styles inventory was used to explore the learning styles of the study group. The participants completed all forms at the end of the first year of medical education. Indicators of academic achievement were scores of five theoretical block exams and five PBL exams performed throughout the academic year of 2008-2009. The majority of the participants took part in the "diverging" (n = 84, 47.7%) and "assimilating" (n = 73, 41.5%) groups. Numbers of students in the "converging" and "accommodating" groups were 11 (6.3%) and 8 (4.5%), respectively. In all learning style groups, PBL satisfaction scores were significantly higher than those of traditional training. Exam scores for "PBL and traditional training" did not differ among the four learning styles. In logistic regression analysis, learning style (assimilating) predicted student satisfaction with traditional training and success in theoretical block exams. Nothing predicted PBL satisfaction and success. This is the first study conducted among medical students evaluating the relation of learning style with student satisfaction and academic achievement. More research with larger groups is needed to generalize our results. Some learning styles may relate to satisfaction with and achievement in some instruction methods.

Effects of problem-based learning vs. traditional lecture on Korean nursing students' critical thinking, problem-solving, and self-directed learning.

PubMed

Choi, Eunyoung; Lindquist, Ruth; Song, Yeoungsuk

2014-01-01

Problem-based learning (PBL) is a method widely used in nursing education to develop students' critical thinking skills to solve practice problems independently. Although PBL has been used in nursing education in Korea for nearly a decade, few studies have examined its effects on Korean nursing students' learning outcomes, and few Korean studies have examined relationships among these outcomes. The objectives of this study are to examine outcome abilities including critical thinking, problem-solving, and self-directed learning of nursing students receiving PBL vs. traditional lecture, and to examine correlations among these outcome abilities. A quasi-experimental non-equivalent group pretest-posttest design was used. First-year nursing students (N=90) were recruited from two different junior colleges in two cities (GY and GJ) in South Korea. In two selected educational programs, one used traditional lecture methods, while the other used PBL methods. Standardized self-administered questionnaires of critical thinking, problem-solving, and self-directed learning abilities were administered before and at 16weeks (after instruction). Learning outcomes were significantly positively correlated, however outcomes were not statistically different between groups. Students in the PBL group improved across all abilities measured, while student scores in the traditional lecture group decreased in problem-solving and self-directed learning. Critical thinking was positively associated with problem-solving and self-directed learning (r=.71, and r=.50, respectively, p<.001); problem-solving was positively associated with self-directed learning (r=.75, p<.001). Learning outcomes of PBL were not significantly different from traditional lecture in this small underpowered study, despite positive trends. Larger studies are recommended to study effects of PBL on critical student abilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

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

Chen, G; Pan, X; Stayman, J

2014-06-15

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within themore » reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.« less

Social studying and learning among medical students: a scoping review.

PubMed

Keren, Daniela; Lockyer, Jocelyn; Ellaway, Rachel H

2017-10-01

Medical students study in social groups, which influence their learning, but few studies have investigated the characteristics of study groups and the impacts they have on students' learning. A scoping review was conducted on the topic of informal social studying and learning within medical education with the aim of appraising what is known regarding medical student attitudes to group study, the impact of group study on participants, and the methods that have been employed to study this. Using Arksey and O'Malley's scoping review principles, MEDLINE, EMBASE and CINAHL were searched, along with hand-searching and a targeted search of the grey literature; 18 peer reviewed and 17 grey literature records were included. Thematic conceptual analysis identified a number of themes, including: the nature of group study; the utility and value of group studying including social learning facilitating student engagement, social learning as a source of motivation and accountability, and social learning as a source of wellbeing; and student preferences related to group studying, including its homophilic nature, transgressiveness, and effectiveness. Despite these emerging factors, the evidence base for this phenomenon is small. The findings in this scoping review demonstrate a clear role for social interaction outside of the classroom, and encourage us to consider the factors in student networking, and the implications of this on medical students' academics. We also highlight areas in need of future research to allow us to better situate informal social learning within medical education and to enable educators to support this phenomenon.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Is literature search training for medical students and residents effective? a literature review.

PubMed

Just, Melissa L

2012-10-01

This literature review examines the effectiveness of literature searching skills instruction for medical students or residents, as determined in studies that either measure learning before and after an intervention or compare test and control groups. The review reports on the instruments used to measure learning and on their reliability and validity, where available. Finally, a summary of learning outcomes is presented. Fifteen studies published between 1998 and 2011 were identified for inclusion in the review. The selected studies all include a description of the intervention, a summary of the test used to measure learning, and the results of the measurement. Instruction generally resulted in improvement in clinical question writing, search strategy construction, article selection, and resource usage. Although the findings of most of the studies indicate that the current instructional methods are effective, the study designs are generally weak, there is little evidence that learning persists over time, and few validated methods of skill measurement have been developed.

Estimating the circuit delay of FPGA with a transfer learning method

NASA Astrophysics Data System (ADS)

Cui, Xiuhai; Liu, Datong; Peng, Yu; Peng, Xiyuan

2017-10-01

With the increase of FPGA (Field Programmable Gate Array, FPGA) functionality, FPGA has become an on-chip system platform. Due to increase the complexity of FPGA, estimating the delay of FPGA is a very challenge work. To solve the problems, we propose a transfer learning estimation delay (TLED) method to simplify the delay estimation of different speed grade FPGA. In fact, the same style different speed grade FPGA comes from the same process and layout. The delay has some correlation among different speed grade FPGA. Therefore, one kind of speed grade FPGA is chosen as a basic training sample in this paper. Other training samples of different speed grade can get from the basic training samples through of transfer learning. At the same time, we also select a few target FPGA samples as training samples. A general predictive model is trained by these samples. Thus one kind of estimation model is used to estimate different speed grade FPGA circuit delay. The framework of TRED includes three phases: 1) Building a basic circuit delay library which includes multipliers, adders, shifters, and so on. These circuits are used to train and build the predictive model. 2) By contrasting experiments among different algorithms, the forest random algorithm is selected to train predictive model. 3) The target circuit delay is predicted by the predictive model. The Artix-7, Kintex-7, and Virtex-7 are selected to do experiments. Each of them includes -1, -2, -2l, and -3 different speed grade. The experiments show the delay estimation accuracy score is more than 92% with the TLED method. This result shows that the TLED method is a feasible delay assessment method, especially in the high-level synthesis stage of FPGA tool, which is an efficient and effective delay assessment method.

Integrated method of teaching in Web Quest activity and its impact on undergraduate students' cognition and learning behaviors: a future trend in medical education.

PubMed

Badiyepeymaie Jahromi, Zohreh; Mosalanejad, Leili

2015-01-14

Web Quest is one of the new ways of teaching and learning that is based on research, and includes the principles of learning and cognitive activities, such as collaborative learning, social and cognitive learning, and active learning, and increases motivation. The aim of this study is to evaluate the Web Quest influence on students' learning behaviors. In this quasi-experimental study, which was performed on undergraduates taking a psychiatric course at Jahrom University of Medical Sciences, simple sampling was used to select the cases to be studied; the students entered the study through census and were trained according to Web Quest methodology. The procedure was to present the course as a case study and team work. Each topic included discussing concepts and then patient's treatment and the communicative principles for two weeks. Active participation of the students in response to the scenario and introduced problem was equal to preparing scientific videos about the disease and collecting the latest medical treatment for the disease from the Internet.Three questionnaires, including the self-directed learning Questionnaire, teamwork evaluation Questionnaire (value of team), and Buffard self-regulated Questionnaire, were the data gathering tools. The results showed that the average of self-regulated learning and self-directed learning (SDL) increased after the educational intervention. However, the increase was not significant. On the other hand, problem solving (P=0.001) and the value of teamwork (P=0.002), apart from increasing the average, had significant statistical values. In view of Web Quest's positive impacts on students' learning behaviors, problem solving and teamwork, the effective use of active learning and teaching practices and use of technology in medical education are recommended.

An advanced teaching scheme for integrating problem-based learning in control education

NASA Astrophysics Data System (ADS)

Juuso, Esko K.

2018-03-01

Engineering education needs to provide both theoretical knowledge and problem-solving skills. Many topics can be presented in lectures and computer exercises are good tools in teaching the skills. Learning by doing is combined with lectures to provide additional material and perspectives. The teaching scheme includes lectures, computer exercises, case studies, seminars and reports organized as a problem-based learning process. In the gradually refining learning material, each teaching method has its own role. The scheme, which has been used in teaching two 4th year courses, is beneficial for overall learning progress, especially in bilingual courses. The students become familiar with new perspectives and are ready to use the course material in application projects.

Weaving a Formal Methods Education with Problem-Based Learning

NASA Astrophysics Data System (ADS)

Gibson, J. Paul

The idea of weaving formal methods through computing (or software engineering) degrees is not a new one. However, there has been little success in developing and implementing such a curriculum. Formal methods continue to be taught as stand-alone modules and students, in general, fail to see how fundamental these methods are to the engineering of software. A major problem is one of motivation — how can the students be expected to enthusiastically embrace a challenging subject when the learning benefits, beyond passing an exam and achieving curriculum credits, are not clear? Problem-based learning has gradually moved from being an innovative pedagogique technique, commonly used to better-motivate students, to being widely adopted in the teaching of many different disciplines, including computer science and software engineering. Our experience shows that a good problem can be re-used throughout a student's academic life. In fact, the best computing problems can be used with children (young and old), undergraduates and postgraduates. In this paper we present a process for weaving formal methods through a University curriculum that is founded on the application of problem-based learning and a library of good software engineering problems, where students learn about formal methods without sitting a traditional formal methods module. The process of constructing good problems and integrating them into the curriculum is shown to be analagous to the process of engineering software. This approach is not intended to replace more traditional formal methods modules: it will better prepare students for such specialised modules and ensure that all students have an understanding and appreciation for formal methods even if they do not go on to specialise in them.

Distance Learning, 2002: Proceedings of the Annual Conference on Distance Teaching and Learning (18th, Madison, Wisconsin, August 14-16, 2002).

ERIC Educational Resources Information Center

Wisconsin Univ. System, Madison.

These proceedings contain 90 papers that address important human factors in distance education from several perspectives, provide insights into how those factors contribute to successful outcomes, and describe practical methods for implementing similar approaches in other settings. They include "Is Your E-Everything Accessible to Everyone?"…

An Evaluation of Independent Learning of the Japanese Hiragana System Using an Interactive CD

ERIC Educational Resources Information Center

Geraghty, Barbara; Quinn, Ann Marcus

2009-01-01

As Japanese uses three writing systems (hiragana, katakana, and the ideograms known as kanji), and as materials in the target language include all three, it is a major challenge to learn to read and write quickly. This paper focuses on interactive multi-media methods of teaching Japanese reading which foster learner autonomy. As little has been…

Mobile Microblogging: Using Twitter and Mobile Devices in an Online Course to Promote Learning in Authentic Contexts

ERIC Educational Resources Information Center

Hsu, Yu-Chang; Ching, Yu-Hui

2012-01-01

This research applied a mixed-method design to explore how best to promote learning in authentic contexts in an online graduate course in instructional message design. The students used Twitter apps on their mobile devices to collect, share, and comment on authentic design examples found in their daily lives. The data sources included tweets…

Development and Factor Analysis of an Instrument to Measure Preservice Teachers' Perceptions of Learning Objects

ERIC Educational Resources Information Center

Sahin, Sami

2010-01-01

The purpose of this study was to develop a questionnaire to measure student teachers' perception of digital learning objects. The participants included 308 voluntary senior students attending courses in a college of education of a public university in Turkey. The items were extracted to their related factors by the principal axis factoring method.…

Exploring the Effects of Social Skills Training on Social Skill Development on Student Behavior

ERIC Educational Resources Information Center

Seevers, Randy L.; Jones-Blank, Michelle

2008-01-01

Most children learn social skills from interaction with others--other children, family members, friends, and adults. Some children with disabilities need to learn social skills more directly. This may include the use of a specific curriculum and the use of individualized methods. The purpose of this study was to explore the effects of social…

A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Liu, Ran; Stamper, John; Davenport, Jodi

2018-01-01

Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…

Language Diversity & Practice in Higher Education: Can Discipline-Specific Language Instruction Improve Economics Learning Outcomes?

ERIC Educational Resources Information Center

Nguyen, Trien; Trimarchi, Angela; Williams, Julia

2012-01-01

In the field of second language acquisition, discipline-specific language instruction is becoming widely known as Content and Language Integrated Learning. This method includes any activity that involves teaching a subject in a second language for the purpose of teaching both the subject content and the language. Research has shown that this two…

The Effects of Argumentation Based Science Learning Approach on Creative Thinking Skills of Students

ERIC Educational Resources Information Center

Küçük Demir, Betül; Isleyen, Tevfik

2015-01-01

The aim of this study is to explore the effects of argumentation-based science learning (ABSL) approach on 9th Grade of Secondary Education students' creative thinking skills. The sample of the study included 22 9th grade of Secondary Education students in Bayburt in 2012-2013 academic year. In this study quantitative research method and…

A School-Randomized Clinical Trial of an Integrated Social-Emotional Learning and Literacy Intervention: Impacts after 1 School Year

ERIC Educational Resources Information Center

Jones, Stephanie M.; Brown, Joshua L.; Hoglund, Wendy L. G.; Aber, J. Lawrence

2010-01-01

Objective: To report experimental impacts of a universal, integrated school-based intervention in social-emotional learning and literacy development on change over 1 school year in 3rd-grade children's social-emotional, behavioral, and academic outcomes. Method: This study employed a school-randomized, experimental design and included 942…

Lights, Camera, Action: Advancing Learning, Research, and Program Evaluation through Video Production in Educational Leadership Preparation

ERIC Educational Resources Information Center

Friend, Jennifer; Militello, Matthew

2015-01-01

This article analyzes specific uses of digital video production in the field of educational leadership preparation, advancing a three-part framework that includes the use of video in (a) teaching and learning, (b) research methods, and (c) program evaluation and service to the profession. The first category within the framework examines videos…

Teaching and Learning in the Tropics: An Epistemic Exploration of "the Field" in a Development Studies Field Trip

ERIC Educational Resources Information Center

Patel, Kamna

2015-01-01

Development studies employs theories, tools and methods often found in geography, including the international field trip to a "developing" country. In 2013 and 2014, I led a two-week trip to Ethiopia. To better comprehend the effects of "the field" on students' learning, I introduced an assessed reflexive field diary to…

Using a binaural biomimetic array to identify bottom objects ensonified by echolocating dolphins

USGS Publications Warehouse

Heiweg, D.A.; Moore, P.W.; Martin, S.W.; Dankiewicz, L.A.

2006-01-01

The development of a unique dolphin biomimetic sonar produced data that were used to study signal processing methods for object identification. Echoes from four metallic objects proud on the bottom, and a substrate-only condition, were generated by bottlenose dolphins trained to ensonify the targets in very shallow water. Using the two-element ('binaural') receive array, object echo spectra were collected and submitted for identification to four neural network architectures. Identification accuracy was evaluated over two receive array configurations, and five signal processing schemes. The four neural networks included backpropagation, learning vector quantization, genetic learning and probabilistic network architectures. The processing schemes included four methods that capitalized on the binaural data, plus a monaural benchmark process. All the schemes resulted in above-chance identification accuracy when applied to learning vector quantization and backpropagation. Beam-forming or concatenation of spectra from both receive elements outperformed the monaural benchmark, with higher sensitivity and lower bias. Ultimately, best object identification performance was achieved by the learning vector quantization network supplied with beam-formed data. The advantages of multi-element signal processing for object identification are clearly demonstrated in this development of a first-ever dolphin biomimetic sonar. ?? 2006 IOP Publishing Ltd.

Blended E-learning in a Web-based virtual hospital: a useful tool for undergraduate education in urology.

PubMed

Horstmann, M; Renninger, M; Hennenlotter, J; Horstmann, C C; Stenzl, A

2009-08-01

E-learning is a teaching tool used successfully in many medical subspecialties. Experience with its use in urology, however, is scarce. We present our teaching experience with the INMEDEA simulator to teach urological care to medical students. The INMEDEA simulator is an interactive e-learning system built around a virtual hospital which includes a department of urology. It allows students to solve virtual patient cases online. In this study, students were asked to prepare two urological cases prior to discussion of the cases in small groups. This blended teaching approach was evaluated by students through anonymous questionnaires. Of 70 4th year medical students 76% judged this teaching method as good or very good. Eighty-seven percent felt that it offered a good way to understand urological diseases better and 72% felt that learning with this method was fun. Nevertheless, 30 out of 70 free text statements revealed that further improvements of the program, including an easier and more comfortable navigation and a faster supply of information are necessary. Virtual patient cases offer a practicable solution for teaching based on problem solving in urology with a high acceptance rate by students.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Basic practical skills teaching and learning in undergraduate medical education – a review on methodological evidence

PubMed Central

Vogel, Daniela; Harendza, Sigrid

2016-01-01

Objective: Practical skills are an essential part of physicians’ daily routine. Nevertheless, medical graduates’ performance of basic skills is often below the expected level. This review aims to identify and summarize teaching approaches of basic practical skills in undergraduate medical education which provide evidence with respect to effective students’ learning of these skills. Methods: Basic practical skills were defined as basic physical examination skills, routine skills which get better with practice, and skills which are also performed by nurses. We searched PubMed with different terms describing these basic practical skills. In total, 3467 identified publications were screened and 205 articles were eventually reviewed for eligibility. Results: 43 studies that included at least one basic practical skill, a comparison of two groups of undergraduate medical students and effects on students’ performance were analyzed. Seven basic practical skills and 15 different teaching methods could be identified. The most consistent results with respect to effective teaching and acquisition of basic practical skills were found for structured skills training, feedback, and self-directed learning. Simulation was effective with specific teaching methods and in several studies no differences in teaching effects were detected between expert or peer instructors. Multimedia instruction, when used in the right setting, also showed beneficial effects for basic practical skills learning. Conclusion: A combination of voluntary or obligatory self-study with multimedia applications like video clips in combination with a structured program including the possibility for individual exercise with personal feedback by peers or teachers might provide a good learning opportunity for basic practical skills. PMID:27579364

Introducing global health into the undergraduate medical school curriculum using an e-learning program: a mixed method pilot study.

PubMed

Gruner, Douglas; Pottie, Kevin; Archibald, Douglas; Allison, Jill; Sabourin, Vicki; Belcaid, Imane; McCarthy, Anne; Brindamour, Mahli; Augustincic Polec, Lana; Duke, Pauline

2015-09-02

Physicians need global health competencies to provide effective care to culturally and linguistically diverse patients. Medical schools are seeking innovative approaches to support global health learning. This pilot study evaluated e-learning versus peer-reviewed articles to improve conceptual knowledge of global health. A mixed methods study using a randomized-controlled trial (RCT) and qualitative inquiry consisting of four post-intervention focus groups. Outcomes included pre/post knowledge quiz and self-assessment measures based on validated tools from a Global Health CanMEDS Competency Model. RCT results were analyzed using SPSS-21 and focus group transcripts coded using NVivo-9 and recoded using thematic analysis. One hundred and sixty-one pre-clerkship medical students from three Canadian medical schools participated in 2012-2013: 59 completed all elements of the RCT, 24 participated in the focus groups. Overall, comparing pre to post results, both groups showed a significant increase in the mean knowledge (quiz) scores and for 5/7 self-assessed competencies (p < 0.05). These quantitative data were triangulated with the focus groups findings that revealed knowledge acquisition with both approaches. There was no statistically significant difference between the two approaches. Participants highlighted their preference for e-learning to introduce new global health knowledge and as a repository of resources. They also mentioned personal interest in global health, online convenience and integration into the curriculum as incentives to complete the e-learning. Beta version e-learning barriers included content overload and technical difficulties. Both the e-learning and the peer reviewed PDF articles improved global health conceptual knowledge. Many students however, preferred e-learning given its interactive, multi-media approach, access to links and reference materials and its capacity to engage and re-engage over long periods of time.

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

NASA Astrophysics Data System (ADS)

Wang, Hongcui; Kawahara, Tatsuya

CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

Brain Jogging Training to Improve Motivation and Learning Result of Tennis Skills

NASA Astrophysics Data System (ADS)

Tafaqur, M.; Komarudin; Mulyana; Saputra, M. Y.

2017-03-01

This research is aimed to determine the effect of brain jogging towards improvement of motivation and learning result of tennis skills. The method used in this research is experimental method. The population of this research is 15 tennis athletes of Core Siliwangi Bandung Tennis Club. The sampling technique used in this research is purposive sampling technique. Sample of this research is the 10 tennis athletes of Core Siliwangi Bandung Tennis Club. Design used for this research is pretest-posttest group design. Data analysis technique used in this research is by doing Instrument T-test to measure motivation using The Sport Motivation Scale questionnaire (SMS-28) and Instrument to measure learning result of tennis skill by using tennis skills test, which include: (1) forehand test, (2) backhand test, and (3) service placement test. The result of this research showed that brain jogging significantly impact the improvement of motivation and learning result of tennis skills.

The touchscreen operant platform for testing learning and memory in rats and mice

PubMed Central

Horner, Alexa E.; Heath, Christopher J.; Hvoslef-Eide, Martha; Kent, Brianne A.; Kim, Chi Hun; Nilsson, Simon R. O.; Alsiö, Johan; Oomen, Charlotte A.; Holmes, Andrew; Saksida, Lisa M.; Bussey, Timothy J.

2014-01-01

Summary An increasingly popular method of assessing cognitive functions in rodents is the automated touchscreen platform, on which a number of different cognitive tests can be run in a manner very similar to touchscreen methods currently used to test human subjects. This methodology is low stress (using appetitive, rather than aversive reinforcement), has high translational potential, and lends itself to a high degree of standardisation and throughput. Applications include the study of cognition in rodent models of psychiatric and neurodegenerative diseases (e.g., Alzheimer’s disease, schizophrenia, Huntington’s disease, frontotemporal dementia), and characterisation of the role of select brain regions, neurotransmitter systems and genes in rodents. This protocol describes how to perform four touchscreen assays of learning and memory: Visual Discrimination, Object-Location Paired-Associates Learning, Visuomotor Conditional Learning and Autoshaping. It is accompanied by two further protocols using the touchscreen platform to assess executive function, working memory and pattern separation. PMID:24051959

Differentiated Instruction in the Classroom

ERIC Educational Resources Information Center

Kelly, Gretchen

2013-01-01

Low achievement on standardized tests may be attributed to many factors, including teaching methods. Differentiated instruction has been identified as a teaching method using different learning modalities that appeal to varied student interests with individualized instruction. The purpose of this quantitative study was to compare whole-group…

Multiphase Method for Analysing Online Discussions

ERIC Educational Resources Information Center

Häkkinen, P.

2013-01-01

Several studies have analysed and assessed online performance and discourse using quantitative and qualitative methods. Quantitative measures have typically included the analysis of participation rates and learning outcomes in terms of grades. Qualitative measures of postings, discussions and context features aim to give insights into the nature…

Increasing health worker capacity through distance learning: a comprehensive review of programmes in Tanzania.

PubMed

Nartker, Anya J; Stevens, Liz; Shumays, Alyson; Kalowela, Martin; Kisimbo, Daniel; Potter, Katy

2010-12-31

Tanzania, like many developing countries, faces a crisis in human resources for health. The government has looked for ways to increase the number and skills of health workers, including using distance learning in their training. In 2008, the authors reviewed and assessed the country's current distance learning programmes for health care workers, as well as those in countries with similar human resource challenges, to determine the feasibility of distance learning to meet the need of an increased and more skilled health workforce. Data were collected from 25 distance learning programmes at health training institutions, universities, and non-governmental organizations throughout the country from May to August 2008. Methods included internet research; desk review; telephone, email and mail-in surveys; on-site observations; interviews with programme managers, instructors, students, information technology specialists, preceptors, health care workers and Ministry of Health and Social Welfare representatives; and a focus group with national HIV/AIDS care and treatment organizations. Challenges include lack of guidelines for administrators, instructors and preceptors of distance learning programmes regarding roles and responsibilities; absence of competencies for clinical components of curricula; and technological constraints such as lack of access to computers and to the internet. Insufficient funding resulted in personnel shortages, lack of appropriate training for personnel, and lack of materials for students.Nonetheless, current and prospective students expressed overwhelming enthusiasm for scale-up of distance learning because of the unique financial and social benefits offered by these programs. Participants were retained as employees in their health care facilities, and remained in their communities and supported their families while advancing their careers. Space in health training institutions was freed up for new students entering in-residence pre-service training. A blended print-based distance learning model is most feasible at the national level due to current resource and infrastructure constraints. With an increase in staffing; improvement of infrastructure, coordination and curricula; and decentralization to the zonal or district level, distance learning can be an effective method to increase both the skills and the numbers of qualified health care workers capable of meeting the health care needs of the Tanzanian population.

A study on the performance comparison of metaheuristic algorithms on the learning of neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2017-08-01

The learning or training process of neural networks entails the task of finding the most optimal set of parameters, which includes translation vectors, dilation parameter, synaptic weights, and bias terms. Apart from the traditional gradient descent-based methods, metaheuristic methods can also be used for this learning purpose. Since the inception of genetic algorithm half a century ago, the last decade witnessed the explosion of a variety of novel metaheuristic algorithms, such as harmony search algorithm, bat algorithm, and whale optimization algorithm. Despite the proof of the no free lunch theorem in the discipline of optimization, a survey in the literature of machine learning gives contrasting results. Some researchers report that certain metaheuristic algorithms are superior to the others, whereas some others argue that different metaheuristic algorithms give comparable performance. As such, this paper aims to investigate if a certain metaheuristic algorithm will outperform the other algorithms. In this work, three metaheuristic algorithms, namely genetic algorithms, particle swarm optimization, and harmony search algorithm are considered. The algorithms are incorporated in the learning of neural networks and their classification results on the benchmark UCI machine learning data sets are compared. It is found that all three metaheuristic algorithms give similar and comparable performance, as captured in the average overall classification accuracy. The results corroborate the findings reported in the works done by previous researchers. Several recommendations are given, which include the need of statistical analysis to verify the results and further theoretical works to support the obtained empirical results.

Foreign Language Circles of Knowledge.

ERIC Educational Resources Information Center

Schiffer, Deana

1981-01-01

Describes use of Circles of Knowledge designed to generate excitement about foreign language learning as technique for individualized instruction. Includes guidelines for using, organizing, and implementing this method. (BK)

Deep Hashing for Scalable Image Search.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Jie

2017-05-01

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods, which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the non-linear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) and multi-label SDH by including a discriminative term into the objective function of DH, which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes with the single-label and multi-label settings, respectively. Extensive experimental results on eight widely used image search data sets show that our proposed methods achieve very competitive results with the state-of-the-arts.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

PubMed

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Active Learning in PhysicsTechnology and Research-based Techniques Emphasizing Interactive Lecture Demonstrations

NASA Astrophysics Data System (ADS)

Thornton, Ronald

2010-10-01

Physics education research has shown that learning environments that engage students and allow them to take an active part in their learning can lead to large conceptual gains compared to traditional instruction. Examples of successful curricula and methods include Peer Instruction, Just in Time Teaching, RealTime Physics, Workshop Physics, Scale-Up, and Interactive Lecture Demonstrations (ILDs). An active learning environment is often difficult to achieve in lecture sessions. This presentation will demonstrate the use of sequences of Interactive Lecture Demonstrations (ILDs) that use real experiments often involving real-time data collection and display combined with student interaction to create an active learning environment in large or small lecture classes. Interactive lecture demonstrations will be done in the area of mechanics using real-time motion probes and the Visualizer. A video tape of students involved in interactive lecture demonstrations will be shown. The results of a number of research studies at various institutions (including international) to measure the effectiveness of ILDs and guided inquiry conceptual laboratories will be presented.

The evaluation of simulation market in nursing education and the determination of learning style of students

PubMed Central

Çelik, Yasemin; Ceylantekin, Yeşim; Kiliç, İbrahim

2017-01-01

Objective: The aim of this study is to detect the overall evaluation of nursing students toward simulation markets throughout the practice education and to reveal their learning styles in relation to certain individual features. Materials and Methods: The data were collected via questionnaires including students’ evaluation toward simulation markets and “Kolb learning styles inventory.” Participants included 103 male and female nursing students in Turkey. For the analysis, percentage, means, standard deviation, t-test, and ANOVA were utilized. Results: 71% of the students stated that the laboratory was suitable for the skill education but 53.4% uttered the duration of the practice was not enough. Students were found to have different learning styles (28.2% assimilating, 27.2% convergent, 26.2% accommodating, and 18.4% divergent). Conclusion: The results demonstrated that the duration of the laboratory practice and the number of the markets should be increased during the education of students with different learning styles. PMID:28293150

The evaluation of simulation maket in nursing education and the determination of learning style of students

PubMed Central

Çelik, Yasemin; Ceylantekin, Yeşim; Kiliç, İbrahim

2017-01-01

Objective: The aim of this study is to detect the overall evaluation of nursing students toward simulation makets throughout the practice education and to reveal their learning styles in relation to certain individual features. Materials and Methods: The data were collected via questionnaires including students’ evaluation toward simulation makets and “Kolb learning styles inventory.” Participants included 103 male and female nursing students in Turkey. For the analysis, percentage, means, standard deviation, t-test, and ANOVA were utilized. Results: 71% of the students stated that the laboratory was suitable for the skill education but 53.4% uttered the duration of the practice was not enough. Students were found to have different learning styles (28.2% assimilating, 27.2% convergent, 26.2% accommodating, and 18.4% divergent). Conclusion: The results demonstrated that the duration of the laboratory practice and the number of the makets should be increased during the education of students with different learning styles. PMID:28936157

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

PubMed

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S. M.; Graff, T. G.; Morris, R. V.; Laura, J.; Gaddis, L. R.

2017-12-01

Machine learning is a powerful but underutilized approach that can enable planetary scientists to derive meaningful results from the rapidly-growing quantity of available spectral data. For example, regression methods such as Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO), can be used to determine chemical concentrations from ChemCam and SuperCam Laser-Induced Breakdown Spectroscopy (LIBS) data [1]. Many scientists are interested in testing different spectral data processing and machine learning methods, but few have the time or expertise to write their own software to do so. We are therefore developing a free open-source library of software called the Python Spectral Analysis Tool (PySAT) along with a flexible, user-friendly graphical interface to enable scientists to process and analyze point spectral data without requiring significant programming or machine-learning expertise. A related but separately-funded effort is working to develop a graphical interface for orbital data [2]. The PySAT point-spectra tool includes common preprocessing steps (e.g. interpolation, normalization, masking, continuum removal, dimensionality reduction), plotting capabilities, and capabilities to prepare data for machine learning such as creating stratified folds for cross validation, defining training and test sets, and applying calibration transfer so that data collected on different instruments or under different conditions can be used together. The tool leverages the scikit-learn library [3] to enable users to train and compare the results from a variety of multivariate regression methods. It also includes the ability to combine multiple "sub-models" into an overall model, a method that has been shown to improve results and is currently used for ChemCam data [4]. Although development of the PySAT point-spectra tool has focused primarily on the analysis of LIBS spectra, the relevant steps and methods are applicable to any spectral data. The tool is available at https://github.com/USGS-Astrogeology/PySAT_Point_Spectra_GUI. [1] Clegg, S.M., et al. (2017) Spectrochim Acta B. 129, 64-85. [2] Gaddis, L. et al. (2017) 3rd Planetary Data Workshop, #1986. [3] http://scikit-learn.org/ [4] Anderson, R.B., et al. (2017) Spectrochim. Acta B. 129, 49-57.

The diversity of Iranian nursing students' clinical learning styles: a qualitative study.

PubMed

Baraz, Shahram; Memarian, Robabeh; Vanaki, Zohreh

2014-09-01

Numerous factors, including learning styles, affect the learning process of nursing students. Having insights about students' learning styles helps promoting the quality of education. The aim of this study was to explore the Iranian baccalaureate nursing students' learning styles in clinical settings. A qualitative design using a content analysis approach was used to collect and analyze data. Semi-structured interviews were conducted with fifteen Iranian baccalaureate nursing students selected using a purposive sample method. During data analysis, it was found that nursing students employed different clinical learning styles such as 'thoughtful observation,' 'learning by thinking,' and 'learning by doing'. Students adopt different learning strategies in clinical practice. Designing teaching strategies based on students' learning styles can promote students' learning and maximize their academic and clinical practice success. Nursing educators, curriculum designers, and students can use the findings of this study to improve the quality of nursing education in both the classroom and clinical settings. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Assessment of Service-Learning in 34 US Schools of Pharmacy Follow Up on the 2001 Professional Affairs Committee Report

PubMed Central

Schlesselman, Lauren; Borrego, Matthew; Mehta, Bella; Drobitch, Robert K.; Smith, Thomas

2015-01-01

Objective. To determine if the service-learning components used at a convenience sample of schools and colleges of pharmacy meet the intent of the 2001 AACP Professional Affairs Committee (PAC) report. Methods. An online questionnaire was used to survey faculty members or staff involved with service-learning education at their school of pharmacy. Questions addressed aspects of service-learning including types of activities used, duration of student involvement with community partners, and association of learning objectives with service-learning activities. Results. The majority (85.3%) of respondents reported their institution used service-learning. Activities reported as part of service-learning ranged from working at health fairs to involvement with pharmacy school recruitment. More than half (64.3%) of service-learning activities involved long-term interactions with one community partner, and 74.1% of respondents indicated there was always an opportunity for student reflection on the service-learning activity. Conclusion. There is increasing though inconsistent application of PAC guidelines regarding service-learning. PMID:26688584

Virtual Reality Rehabilitation from Social Cognitive and Motor Learning Theoretical Perspectives in Stroke Population

PubMed Central

Imam, Bita; Jarus, Tal

2014-01-01

Objectives. To identify the virtual reality (VR) interventions used for the lower extremity rehabilitation in stroke population and to explain their underlying training mechanisms using Social Cognitive (SCT) and Motor Learning (MLT) theoretical frameworks. Methods. Medline, Embase, Cinahl, and Cochrane databases were searched up to July 11, 2013. Randomized controlled trials that included a VR intervention for lower extremity rehabilitation in stroke population were included. The Physiotherapy Evidence Database (PEDro) scale was used to assess the quality of the included studies. The underlying training mechanisms involved in each VR intervention were explained according to the principles of SCT (vicarious learning, performance accomplishment, and verbal persuasion) and MLT (focus of attention, order and predictability of practice, augmented feedback, and feedback fading). Results. Eleven studies were included. PEDro scores varied from 3 to 7/10. All studies but one showed significant improvement in outcomes in favour of the VR group (P < 0.05). Ten VR interventions followed the principle of performance accomplishment. All the eleven VR interventions directed subject's attention externally, whereas nine provided training in an unpredictable and variable fashion. Conclusions. The results of this review suggest that VR applications used for lower extremity rehabilitation in stroke population predominantly mediate learning through providing a task-oriented and graduated learning under a variable and unpredictable practice. PMID:24523967

Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation.

PubMed

Xu, Zhe; Huang, Shaoli; Zhang, Ya; Tao, Dacheng

2018-05-01

Learning visual representations from web data has recently attracted attention for object recognition. Previous studies have mainly focused on overcoming label noise and data bias and have shown promising results by learning directly from web data. However, we argue that it might be better to transfer knowledge from existing human labeling resources to improve performance at nearly no additional cost. In this paper, we propose a new semi-supervised method for learning via web data. Our method has the unique design of exploiting strong supervision, i.e., in addition to standard image-level labels, our method also utilizes detailed annotations including object bounding boxes and part landmarks. By transferring as much knowledge as possible from existing strongly supervised datasets to weakly supervised web images, our method can benefit from sophisticated object recognition algorithms and overcome several typical problems found in webly-supervised learning. We consider the problem of fine-grained visual categorization, in which existing training resources are scarce, as our main research objective. Comprehensive experimentation and extensive analysis demonstrate encouraging performance of the proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is likely to be highly effective for real-world applications.

Closed-loop and robust control of quantum systems.

PubMed

Chen, Chunlin; Wang, Lin-Cheng; Wang, Yuanlong

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H(∞) control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention.

An exploration for research-oriented teaching model in biology teaching.

PubMed

Xing, Wanjin; Mo, Morigen; Su, Huimin

2014-07-01

Training innovative talents, as one of the major aims for Chinese universities, needs to reform the traditional teaching methods. The research-oriented teaching method has been introduced and its connotation and significance for Chinese university teaching have been discussed for years. However, few practical teaching methods for routine class teaching were proposed. In this paper, a comprehensive and concrete research-oriented teaching model with contents of reference value and evaluation method for class teaching was proposed based on the current teacher-guiding teaching model in China. We proposed that the research-oriented teaching model should include at least seven aspects on: (1) telling the scientific history for the skills to find out scientific questions; (2) replaying the experiments for the skills to solve scientific problems; (3) analyzing experimental data for learning how to draw a conclusion; (4) designing virtual experiments for learning how to construct a proposal; (5) teaching the lesson as the detectives solve the crime for learning the logic in scientific exploration; (6) guiding students how to read and consult the relative references; (7) teaching students differently according to their aptitude and learning ability. In addition, we also discussed how to evaluate the effects of the research-oriented teaching model in examination.

Support for learning goes beyond academic support: Voices of students with Asperger's disorder and attention deficit hyperactivity disorder.

PubMed

Bolic Baric, Vedrana; Hellberg, Kristina; Kjellberg, Anette; Hemmingsson, Helena

2016-02-01

The purpose of this study was to describe and explore the experiences of support at school among young adults with Asperger's disorder and attention deficit hyperactivity disorder and also to examine what support they, in retrospect, described as influencing learning. Purposive sampling was used to enroll participants. Data were collected through semi-structured interviews with 13 young adults aged between 20 and 29 years. A qualitative analysis, based on interpreting people's experiences, was conducted by grouping and searching for patterns in data. The findings indicate that the participants experienced difficulties at school that included academic, social, and emotional conditions, all of which could influence learning. Support for learning included small groups, individualized teaching methods, teachers who cared, and practical and emotional support. These clusters together confirm the overall understanding that support for learning aligns academic and psychosocial support. In conclusion, academic support combined with psychosocial support at school seems to be crucial for learning among students with Asperger's disorder and attention deficit hyperactivity disorder. © The Author(s) 2015.

Deep learning aided decision support for pulmonary nodules diagnosing: a review.

PubMed

Yang, Yixin; Feng, Xiaoyi; Chi, Wenhao; Li, Zhengyang; Duan, Wenzhe; Liu, Haiping; Liang, Wenhua; Wang, Wei; Chen, Ping; He, Jianxing; Liu, Bo

2018-04-01

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

Comparison of the Operative Outcomes and Learning Curves between Laparoscopic and Robotic Gastrectomy for Gastric Cancer

PubMed Central

Huang, Kuo-Hung; Lan, Yuan-Tzu; Fang, Wen-Liang; Chen, Jen-Hao; Lo, Su-Shun; Li, Anna Fen-Yau; Chiou, Shih-Hwa; Wu, Chew-Wun; Shyr, Yi-Ming

2014-01-01

Background Minimally invasive surgery, including laparoscopic and robotic gastrectomy, has become more popular in the treatment of gastric cancer. However, few studies have compared the learning curves between laparoscopic and robotic gastrectomy for gastric cancer. Methods Data were prospectively collected between July 2008 and Aug 2014. A total of 145 patients underwent minimally invasive gastrectomy for gastric cancer by a single surgeon, including 73 laparoscopic and 72 robotic gastrectomies. The clinicopathologic characteristics, operative outcomes and learning curves were compared between the two groups. Results Compared with the laparoscopic group, the robotic group was associated with less blood loss and longer operative time. After the surgeon learning curves were overcome for each technique, the operative outcomes became similar between the two groups except longer operative time in the robotic group. After accumulating more cases of robotic gastrectomy, the operative time in the laparoscopic group decreased dramatically. Conclusions After overcoming the learning curves, the operative outcomes became similar between laparoscopic and robotic gastrectomy. The experience of robotic gastrectomy could affect the learning process of laparoscopic gastrectomy. PMID:25360767

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

PubMed Central

Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

2016-01-01

Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. PMID:27802228

Effective pedagogies for teaching math to nursing students: a literature review.

PubMed

Hunter Revell, Susan M; McCurry, Mary K

2013-11-01

Improving mathematical competency and problem-solving skills in undergraduate nursing students has been an enduring challenge for nurse educators. A number of teaching strategies have been used to address this problem with varying degrees of success. This paper discusses a literature review which examined undergraduate nursing student challenges to learning math, methods used to teach math and problem-solving skills, and the use of innovative pedagogies for teaching. The literature was searched using the Cumulative Index of Nursing and Allied Health Literature and Education Resource Information Center databases. Key search terms included: math*, nurs*, nursing student, calculation, technology, medication administration, challenges, problem-solving, personal response system, clickers, computer and multi-media. Studies included in the review were published in English from 1990 to 2011. Results support four major themes which include: student challenges to learning, traditional pedagogies, curriculum strategies, and technology and integrative methods as pedagogy. The review concludes that there is a need for more innovative pedagogical strategies for teaching math to student nurses. Nurse educators in particular play a central role in helping students learn the conceptual basis, as well as practical hands-on methods, to problem solving and math competency. It is recommended that an integrated approach inclusive of technology will benefit students through better performance, increased understanding, and improved student satisfaction. Copyright © 2012 Elsevier Ltd. All rights reserved.

Designing a model for critical thinking development in AJA University of Medical Sciences

PubMed Central

MAFAKHERI LALEH, MAHYAR; MOHAMMADIMEHR, MOJGAN; ZARGAR BALAYE JAME, SANAZ

2016-01-01

Introduction: In the new concept of medical education, creativity development is an important goal. The aim of this research was to identify a model for developing critical thinking among students with the special focus on learning environment and learning style. Methods: This applied and cross-sectional study was conducted among all students studying in undergraduate and professional doctorate programs in Fall Semester 2013-2014 in AJA University of Medical Sciences (N=777). The sample consisted of 257 students selected based on the proportional stratified random sampling method. To collect data, three questionnaires including Critical Thinking, Perception of Learning Environment and Learning Style were employed. The data were analyzed using Pearson's correlation statistical test, and one-sample t-test. The Structural Equation Model (SEM) was used to test the research model. SPSS software, version 14 and the LISREL software were used for data analysis. Results: The results showed that students had significantly assessed the teaching-learning environment and two components of "perception of teachers" and "perception of emotional-psychological climate" at the desirable level (p<0.05). Also learning style and two components of "the study method" and "motivation for studying" were considered significantly desirable (p<0.05). The level of critical thinking among students in terms of components of "commitment", "creativity" and "cognitive maturity" was at the relatively desirable level (p<0.05). In addition, perception of the learning environment can impact the critical thinking through learning style. Conclusion: One of the factors which can significantly impact the quality improvement of the teaching and learning process in AJA University of Medical Sciences is to develop critical thinking among learners. This issue requires providing the proper situation for teaching and learning critical thinking in the educational environment. PMID:27795968

Associations of learning style with cultural values and demographics in nursing students in Iran and Malaysia

PubMed Central

Abdollahimohammad, Abdolghani; Ja’afar, Rogayah

2015-01-01

Purpose: The goal of the current study was to identify associations between the learning style of nursing students and their cultural values and demographic characteristics. Methods: A non-probability purposive sampling method was used to gather data from two populations. All 156 participants were female, Muslim, and full-time degree students. Data were collected from April to June 2010 using two reliable and validated questionnaires: the Learning Style Scales and the Values Survey Module 2008 (VSM 08). A simple linear regression was run for each predictor before conducting multiple linear regression analysis. The forward selection method was used for variable selection. P-values ≤0.05 and ≤0.1 were considered to indicate significance and marginal significance, respectively. Moreover, multi-group confirmatory factor analysis was performed to determine the invariance of the Farsi and English versions of the VSM 08. Results: The perceptive learning style was found to have a significant negative relationship with the power distance and monumentalism indices of the VSM 08. Moreover, a significant negative association was observed between the solitary learning style and the power distance index. However, no significant association was found between the analytic, competitive, and imaginative learning styles and cultural values (P>0.05). Likewise, no significant associations were observed between learning style, including the perceptive, solitary, analytic, competitive, and imaginative learning styles, and year of study or age (P>0.05). Conclusion: Students who reported low values on the power distance and monumentalism indices are more likely to prefer perceptive and solitary learning styles. Within each group of students in our study sample from the same school the year of study and age did not show any significant associations with learning style. PMID:26268831

Ethical experiential learning in medical, nursing and allied health education: A narrative review.

PubMed

Grace, Sandra; Innes, Ev; Patton, Narelle; Stockhausen, Lynette

2017-04-01

Students enrolled in medical, nursing and health science programs often participate in experiential learning in their practical classes. Experiential learning includes peer physical examination and peer-assisted learning where students practise clinical skills on each other. To identify effective strategies that enable ethical experiential learning for health students during practical classes. A narrative review of the literature. Pubmed, Cinahl and Scopus databases were searched because they include most of the health education journals where relevant articles would be published. A data extraction framework was developed to extract information from the included papers. Data were entered into a fillable form in Google Docs. Findings from identified studies were extracted to a series of tables (e.g. strategies for fostering ethical conduct; facilitators and barriers to peer-assisted learning). Themes were identified from these findings through a process of line by line coding and organisation of codes into descriptive themes using a constant comparative method. Finally understandings and hypotheses of relevance to our research question were generated from the descriptive themes. A total of 35 articles were retrieved that met the inclusion criteria. A total of 13 strategies for ethical experiential learning were identified and one evaluation was reported. The most frequently reported strategies were gaining written informed consent from students, providing information about the benefits of experiential learning and what to expect in practical classes, and facilitating discussions in class about potential issues. Contexts that facilitated participation in experiential learning included allowing students to choose their own groups, making participation voluntary, and providing adequate supervision, feedback and encouragement. A total of 13 strategies for ethical experiential learning were identified in the literature. A formal process for written consent was evaluated as effective; the effectiveness of other strategies remains to be determined. A comprehensive framework that integrates all recommendations from the literature is needed to guide future research and practise of ethical experiential learning in health courses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Analysis of live cell images: Methods, tools and opportunities.

PubMed

Nketia, Thomas A; Sailem, Heba; Rohde, Gustavo; Machiraju, Raghu; Rittscher, Jens

2017-02-15

Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits. Copyright © 2017. Published by Elsevier Inc.

METAPHOR: Probability density estimation for machine learning based photometric redshifts

NASA Astrophysics Data System (ADS)

Amaro, V.; Cavuoti, S.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.

2017-06-01

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).