Paradigm Shift or Business as Usual: The Reception and Implementation of the BYU-Idaho Learning Model by Faculty Members--A Mixed Methods Study

ERIC Educational Resources Information Center

Thurgood, Larry L.

2010-01-01

A mixed methods study examined how a newly developed campus-wide framework for learning and teaching, called the Learning Model, was accepted and embraced by faculty members at Brigham Young University-Idaho from September 2007 to January 2009. Data from two administrations of the Approaches to Teaching Inventory showed that (a) faculty members…

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.

The Role of Service-Learning to Promote Early Childhood Physical Education while Examining Its Influence upon the Vocational Call to Teach

ERIC Educational Resources Information Center

Miller, Marybeth

2012-01-01

Background: The implementation of service-learning as a teaching and learning method has been well grounded in education, yet the discipline of physical education teacher education (PETE) has been slow to establish itself in this experiential learning paradigm. This study examined the role that service-learning plays in teacher candidates'…

A Machine Learning Method for Power Prediction on the Mobile Devices.

PubMed

Chen, Da-Ren; Chen, You-Shyang; Chen, Lin-Chih; Hsu, Ming-Yang; Chiang, Kai-Feng

2015-10-01

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method.

PubMed

Bhaya, Amit; Kaszkurewicz, Eugenius

2004-01-01

It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system.

Mobile Learning in Higher Education: An Empirical Assessment of a New Educational Tool

ERIC Educational Resources Information Center

McConatha, Douglas; Praul, Matt; Lynch, Michael J.

2008-01-01

Mobile Learning, or M-learning as it is often called, is a relatively new tool in the pedagogical arsenal to assist students and teachers as they navigate the options available in the expanding distance learning world. This article assesses some of the possible methods, challenges and future potential of using this approach in a college classroom…

Storyboarding: A Method for Bootstrapping the Design of Computer-Based Educational Tasks

ERIC Educational Resources Information Center

Jones, Ian

2008-01-01

There has been a recent call for the use of more systematic thought experiments when investigating learning. This paper presents a storyboarding method for capturing and sharing initial ideas and their evolution in the design of a mathematics learning task. The storyboards produced can be considered as "virtual data" created by thought experiments…

Block Play: Practical Suggestions for Common Dilemmas

ERIC Educational Resources Information Center

Tunks, Karyn Wellhousen

2009-01-01

Learning materials and teaching methods used in early childhood classrooms have fluctuated greatly over the past century. However, one learning tool has stood the test of time: Wood building blocks, often called unit blocks, continue to be a source of pleasure and learning for young children at play. Wood blocks have the unique capacity to engage…

Implementing a Project-Based Learning Model in a Pre-Service Leadership Program

ERIC Educational Resources Information Center

Albritton, Shelly; Stacks, Jamie

2016-01-01

This paper describes two instructors' efforts to more authentically engage students in a preservice leadership program's course called Program Planning and Evaluation by using a project-based learning approach. Markham, Larmer, and Ravitz (2003) describe project-based learning (PjBL) as "a systematic teaching method that engages students in…

Three Reading Comprehension Strategies: TELLS, Story Mapping, and QARs.

ERIC Educational Resources Information Center

Sorrell, Adrian L.

1990-01-01

Three reading comprehension strategies are presented to assist learning-disabled students: an advance organizer technique called "TELLS Fact or Fiction" used before reading a passage, a schema-based technique called "Story Mapping" used while reading, and a postreading method of categorizing questions called…

A strategy for quantum algorithm design assisted by machine learning

NASA Astrophysics Data System (ADS)

Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

2014-07-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

The Implementation of Lesson Study to Strengthen Students: Understanding Participation and Application Capabilities in History Education Research Method on Topic Research and Development

ERIC Educational Resources Information Center

Towaf, Siti Malikhah

2016-01-01

Learning can be observed from three-dimensions called: effectiveness, efficiency, and attractiveness of learning. Careful study carried out by analyzing the learning elements of the system are: input, process, and output. Lesson study is an activity designed and implemented as an effort to improve learning in a variety of dimensions. "Lesson…

Story Retelling Used with Average and Learning Disabled Readers as a Measure of Reading Comprehension.

ERIC Educational Resources Information Center

Hansen, Cheryl L.

1978-01-01

A method for quantifying story retells, called proposition analysis, was used to study the reading comprehension performances of 34 learning disabled and normal fifth and sixth graders. Journal availability: see EC 112 927. (DLS) 927

Development of Speaking Skills through Activity Based Learning at the Elementary Level

ERIC Educational Resources Information Center

Ul-Haq, Zahoor; Khurram, Bushra Ahmed; Bangash, Arshad Khan

2017-01-01

Purpose: This paper discusses an effective instructional method called "activity based learning" that can be used to develop the speaking skills of students in the elementary school level. The present study was conducted to determine the effect of activity based learning on the development of the speaking skills of low and high achievers…

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…

Employer Involvement in Work-Based Learning Programs.

ERIC Educational Resources Information Center

Bailey, Thomas; Hughes, Katherine

A 3-year research project focused on whether sufficient numbers of employers could be recruited to create a national school-to-work system with a substantial work-based learning component as called for by the 1994 School-to-Work Opportunities Act. Research methods were as follows: case studies of 12 work-based learning programs at 9 sites located…

Label Information Guided Graph Construction for Semi-Supervised Learning.

PubMed

Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

2017-09-01

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

Changes in Instructional System Design (ISD): Improving Training Product Delivery to United States Army Soldiers

DTIC Science & Technology

2005-03-18

simulation. This model is a basis of what is called discovery learning. Discovery learning is constructionist method of instruction, which is a concept in...2005 PAGES: 48 CLASSIFICATION: Unclassified The purpose of this study is to identify methods that could speed up the instructional system design...became obvious as the enemy attacked using asymmetric means and methods . For instance: during the war, a mine identification-training product was

Assessing the Impact of a Multi-Disciplinary Peer-Led-Team Learning Program on Undergraduate STEM Education

ERIC Educational Resources Information Center

Carlson, Kerri; Celotta, Dayius Turvold; Curran, Erin; Marcus, Mithra; Loe, Melissa

2016-01-01

There has been a national call to transition away from the traditional, passive, lecture-based model of STEM education towards one that facilitates learning through active engagement and problem solving. This mixed-methods research study examines the impact of a supplemental Peer-Led Team Learning (PLTL) program on knowledge and skill acquisition…

Qualitative Insights from a Canadian Multi-Institutional Research Study: In Search of Meaningful E-Learning

ERIC Educational Resources Information Center

Carter, Lorraine M.; Salyers, Vince; Myers, Sue; Hipfner, Carol; Hoffart, Caroline; MacLean, Christa; White, Kathy; Matus, Theresa; Forssman, Vivian; Barrett, Penelope

2014-01-01

This paper reports the qualitative findings of a mixed methods research study conducted at three Canadian post-secondary institutions. Called the Meaningful E-learning or MEL project, the study was an exploration of the teaching and learning experiences of faculty and students as well as their perceptions of the benefits and challenges of…

Developing Student Social Skills Using Restorative Practices: A New Framework Called H.E.A.R.T

ERIC Educational Resources Information Center

Kehoe, Michelle; Bourke-Taylor, Helen; Broderick, David

2018-01-01

Students attending schools today not only learn about formal academic subjects, they also learn social and emotional skills. Whole-school restorative practices (RP) is an approach which can be used to address student misbehaviour when it occurs, and as a holistic method to increase social and emotional learning in students. The aim of this study…

Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection

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

Chen, Pin-Yu; Choudhury, Sutanay; Hero, Alfred

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles ofmore » graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.« less

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

PubMed

Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

2015-12-01

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

AAC menu interface: effectiveness of active versus passive learning to master abbreviation-expansion codes.

PubMed

Gregory, Ellyn; Soderman, Melinda; Ward, Christy; Beukelman, David R; Hux, Karen

2006-06-01

This study investigated the accuracy with which 30 young adults without disabilities learned abbreviation expansion codes associated with specific vocabulary items that were stored in an AAC device with two accessing methods: mouse access and keyboard access. Both accessing methods utilized a specialized computer application, called AAC Menu, which allowed for errorless practice. Mouse access prompted passive learning, whereas keyboard access prompted active learning. Results revealed that participants who accessed words via a keyboard demonstrated significantly higher mastery of abbreviation-expansion codes than those who accessed words via a computer mouse.

New Blueprints for K-12 Schools

ERIC Educational Resources Information Center

Kearns, Larry

2017-01-01

Blended Learning uses school time in a unique way, combining online instruction with traditional methods and giving students more agency over how, when, and where they learn. That third variable, the "where," calls for some serious rethinking of how school space is organized and deployed. Design either supports or frustrates a school's…

Recent Research on Human Learning Challenges Conventional Instructional Strategies

ERIC Educational Resources Information Center

Rohrer, Doug; Pashler, Harold

2010-01-01

There has been a recent upsurge of interest in exploring how choices of methods and timing of instruction affect the rate and persistence of learning. The authors review three lines of experimentation--all conducted using educationally relevant materials and time intervals--that call into question important aspects of common instructional…

The Adaptation Study of the Questionnaires of the Attitude towards CALL (A-CALL), the Attitude towards CAL (A-CAL), the Attitude towards Foreign Language Learning (A-FLL) to Turkish Language

ERIC Educational Resources Information Center

Erdem, Cahit; Saykili, Abdullah; Kocyigit, Mehmet

2018-01-01

This study primarily aims to adapt the Foreign Language Learning (FLL), Computer assisted Learning (CAL) and Computer assisted Language Learning (CALL) scales developed by Vandewaetere and Desmet into Turkish context. The instrument consists of three scales which are "the attitude towards CALL questionnaire" ("A-CALL")…

Evolutionary neural networks for anomaly detection based on the behavior of a program.

PubMed

Han, Sang-Jun; Cho, Sung-Bae

2006-06-01

The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.

Prediction and Validation of Disease Genes Using HeteSim Scores.

PubMed

Zeng, Xiangxiang; Liao, Yuanlu; Liu, Yuansheng; Zou, Quan

2017-01-01

Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp.

Aspects of a Theory of Simplification, Debugging, and Coaching.

ERIC Educational Resources Information Center

Fischer, Gerhard; And Others

This paper analyses new methods of teaching skiing in terms of a computational paradigm for learning called increasingly complex microworlds (ICM). Examining the factors that underlie the dramatic enhancement of the learning of skiing led to the focus on the processes of simplification, debugging, and coaching. These three processes are studied in…

The Development of Prosocial Behavior in Adolescents: A Mixed Methods Study from NOLS

ERIC Educational Resources Information Center

Furman, Nate; Sibthorp, Jim

2014-01-01

Learning transfer and prosocial behavior (PSB) are critical components of many outdoor education programs for adolescents. This study examined the effects of a theoretically grounded treatment curriculum designed to foster the transfer of learning of general and contextual PSB (also called expedition behavior) among adolescents enrolled on 14-day…

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…

Let's Be PALS: An Evidence-Based Approach to Professional Development

ERIC Educational Resources Information Center

Dunst, Carl J.; Trivette, Carol M.

2009-01-01

An evidence-based approach to professional development is described on the basis of the findings from a series of research syntheses and meta-analyses of adult learning methods and strategies. The approach, called PALS (Participatory Adult Learning Strategy), places major emphasis on both active learner involvement in all aspects of training…

Twenty Golden Opportunities To Enhance Student Learning: Use Them or Lose Them.

ERIC Educational Resources Information Center

Sponder, Barry

In an average classroom period, a teacher has twenty or more opportunities to interact with students and thereby influence learning outcomes. As such, teachers should use these opportunities to reinforce instruction or give positive corrective feedback. Typical methods used in schools emphasize error correction at the expense of calling attention…

The Effects of an Experiential Approach to Learning on Student Motivation

ERIC Educational Resources Information Center

Baker, Marshall A.; Robinson, J. Shane

2017-01-01

Student motivation is often an overlooked product of classroom instruction. Researchers have repeatedly called for broader measures to adequately assess and understand the effects of various instructional methods. This study sought to determine the effects of an experiential approach to learning on student motivation, as defined by Keller's (1987)…

The Impact of Cooperative Learning on Student Engagement: Results from an Intervention

ERIC Educational Resources Information Center

Herrmann, Kim J.

2013-01-01

With an increasing awareness that many undergraduates are passive during teaching sessions, calls for instructional methods that allow students to become actively engaged have increased. Cooperative learning has long been popular at the primary and secondary level and, within recent years, higher education. However, empirical evidence of the…

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.

Preparing Future Practitioners to Engage in the Scholarship of Teaching and Learning: A Collective Case Study Approach to Understanding the Formal Student Affairs Master's Curriculum

ERIC Educational Resources Information Center

Ribera, Tony

2012-01-01

Student affairs professionals have been called to apply pedagogical methods to promote student learning in the out-of-class setting and show evidence of their contributions to student learning. To fulfill their professional responsibilities, practitioners should enter the student affairs profession with a basic understanding of ways to gather,…

The Effects of a Modified Cover, Copy, Compare on Spelling Tests and in Written Compositions for Three Students with Specific Learning Disabilities

ERIC Educational Resources Information Center

Goodman, Ashley; McLaughlin, T. F.; Derby, K. Mark; Everson, Mary

2015-01-01

Spelling skills are vital in teaching students to read and write effectively. One method to help students learn to spell words correctly is called cover, copy, and compare (CCC). This study was designed to evaluate the effects of using CCC on the spelling and writing skills of three students with learning disabilities. These skills were measured…

Automatic face naming by learning discriminative affinity matrices from weakly labeled images.

PubMed

Xiao, Shijie; Xu, Dong; Wu, Jianxin

2015-10-01

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

Social calls provide novel insights into the evolution of vocal learning

PubMed Central

Sewall, Kendra B.; Young, Anna M.; Wright, Timothy F.

2016-01-01

Learned song is among the best-studied models of animal communication. In oscine songbirds, where learned song is most prevalent, it is used primarily for intrasexual selection and mate attraction. Learning of a different class of vocal signals, known as contact calls, is found in a diverse array of species, where they are used to mediate social interactions among individuals. We argue that call learning provides a taxonomically rich system for studying testable hypotheses for the evolutionary origins of vocal learning. We describe and critically evaluate four nonmutually exclusive hypotheses for the origin and current function of vocal learning of calls, which propose that call learning (1) improves auditory detection and recognition, (2) signals local knowledge, (3) signals group membership, or (4) allows for the encoding of more complex social information. We propose approaches to testing these four hypotheses but emphasize that all of them share the idea that social living, not sexual selection, is a central driver of vocal learning. Finally, we identify future areas for research on call learning that could provide new perspectives on the origins and mechanisms of vocal learning in both animals and humans. PMID:28163325

Active semi-supervised learning method with hybrid deep belief networks.

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

Visual texture perception via graph-based semi-supervised learning

NASA Astrophysics Data System (ADS)

Zhang, Qin; Dong, Junyu; Zhong, Guoqiang

2018-04-01

Perceptual features, for example direction, contrast and repetitiveness, are important visual factors for human to perceive a texture. However, it needs to perform psychophysical experiment to quantify these perceptual features' scale, which requires a large amount of human labor and time. This paper focuses on the task of obtaining perceptual features' scale of textures by small number of textures with perceptual scales through a rating psychophysical experiment (what we call labeled textures) and a mass of unlabeled textures. This is the scenario that the semi-supervised learning is naturally suitable for. This is meaningful for texture perception research, and really helpful for the perceptual texture database expansion. A graph-based semi-supervised learning method called random multi-graphs, RMG for short, is proposed to deal with this task. We evaluate different kinds of features including LBP, Gabor, and a kind of unsupervised deep features extracted by a PCA-based deep network. The experimental results show that our method can achieve satisfactory effects no matter what kind of texture features are used.

Exploration of Textual Interactions in CALL Learning Communities: Emerging Research and Opportunities

ERIC Educational Resources Information Center

White, Jonathan R.

2017-01-01

Computer-assisted language learning (CALL) has greatly enhanced the realm of online social interaction and behavior. In language classrooms, it allows the opportunity for students to enhance their learning experiences. "Exploration of Textual Interactions in CALL Learning Communities: Emerging Research and Opportunities" is an ideal…

Pedagogy and Related Criteria: The Selection of Software for Computer Assisted Language Learning

ERIC Educational Resources Information Center

Samuels, Jeffrey D.

2013-01-01

Computer-Assisted Language Learning (CALL) is an established field of academic inquiry with distinct applications for second language teaching and learning. Many CALL professionals direct language labs or language resource centers (LRCs) in which CALL software applications and generic software applications support language learning programs and…

Exercise, character strengths, well-being, and learning climate in the prediction of performance over a 6-month period at a call center

PubMed Central

Moradi, Saleh; Nima, Ali A.; Rapp Ricciardi, Max; Archer, Trevor; Garcia, Danilo

2014-01-01

Background: Performance monitoring might have an adverse influence on call center agents' well-being. We investigate how performance, over a 6-month period, is related to agents' perceptions of their learning climate, character strengths, well-being (subjective and psychological), and physical activity. Method: Agents (N = 135) self-reported perception of the learning climate (Learning Climate Questionnaire), character strengths (Values In Action Inventory Short Version), well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version), and how often/intensively they engaged in physical activity. Performance, “time on the phone,” was monitored for 6 consecutive months by the same system handling the calls. Results: Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective) and Temperance (e.g., having self-control, being prudent, humble, and modest), and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation) and Justice (e.g., affiliation, fairness, leadership), positive affect, life satisfaction and exercise Intensity. Conclusion: Call centers may need to create opportunities to develop to increase agents' performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes. PMID:25002853

Instructional Scaffolds for Learning from Formative Peer Assessment: Effects of Core Task, Peer Feedback, and Dialogue

ERIC Educational Resources Information Center

Deiglmayr, Anne

2018-01-01

Formative peer assessment is an instructional method that offers many opportunities to foster students' learning with respect to both the domain of the core task and students' assessment skills. The contributions to this special issue effectively address earlier calls for more research into instructional scaffolds and the implementation of…

A Cross-Cultural Study on the Attitudes of English Language Students towards Computer-Assisted Language Learning

ERIC Educational Resources Information Center

Tafazoli, Dara; Gómez Parra, Mª Elena; Huertas Abril, Cristina A.

2018-01-01

The purpose of this study was to compare the attitude of Iranian and non-Iranian English language students' attitudes towards Computer-Assisted Language Learning (CALL). Furthermore, the relations of gender, education level, and age to their attitude are investigated. A convergent mixed methods design was used for analyzing both quantitative and…

EFL Students' Experiences in Learning "CALL" through Project Based Instructions

ERIC Educational Resources Information Center

Mali, Yustinus Calvin Gai

2017-01-01

Various initiatives led by Ministries of Education and related entities in many countries around the world have encouraged teachers not only to integrate technology in their teaching practices but also to employ various sound teaching methods that allow learners to be actively involved in the teaching and learning process. As a response to these…

A Comparative Analysis of Student Learning with a Collaborative Computer Simulation of the Cardiopulmonary System

ERIC Educational Resources Information Center

Keyser, Diane

2010-01-01

To design a series of assessments that could be used to compare the learning gains of high school students studying the cardiopulmonary system using traditional methods to those who used a collaborative computer simulation, called "Mr. Vetro". Five teachers and 264 HS biology students participated in the study. The students were in…

Adapting and Evaluating a Tree of Life Group for Women with Learning Disabilities

ERIC Educational Resources Information Center

Randle-Phillips, Cathy; Farquhar, Sarah; Thomas, Sally

2016-01-01

Background: This study describes how a specific narrative therapy approach called 'the tree of life' was adapted to run a group for women with learning disabilities. The group consisted of four participants and ran for five consecutive weeks. Materials and Methods: Participants each constructed a tree to represent their lives and presented their…

Teaching Law and Theory through Context: Contract Clauses in Legal Studies Education

ERIC Educational Resources Information Center

DiMatteo, Larry A.; Anenson, T. Leigh

2007-01-01

Business professors in the twenty-first century have been engaging in another form of problem-based pedagogy to unite business school and business practice. This teaching methodology, called "active learning," has become the new case method in college courses. Like the case-based approach, active learning bridges the gap between theory and…

Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Lu, Zhixin; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2017-12-01

We use recent advances in the machine learning area known as "reservoir computing" to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measurements as input to a high-dimensional dynamical system called a "reservoir." After the reservoir's response to the data is recorded, linear regression is used to learn a large set of parameters, called the "output weights." The learned output weights are then used to form a modified autonomous reservoir designed to be capable of producing an arbitrarily long time series whose ergodic properties approximate those of the input signal. When successful, we say that the autonomous reservoir reproduces the attractor's "climate." Since the reservoir equations and output weights are known, we can compute the derivatives needed to determine the Lyapunov exponents of the autonomous reservoir, which we then use as estimates of the Lyapunov exponents for the original input generating system. We illustrate the effectiveness of our technique with two examples, the Lorenz system and the Kuramoto-Sivashinsky (KS) equation. In the case of the KS equation, we note that the high dimensional nature of the system and the large number of Lyapunov exponents yield a challenging test of our method, which we find the method successfully passes.

Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

NASA Astrophysics Data System (ADS)

Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

2011-08-01

This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

PubMed

Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

2012-09-01

It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

Sparse alignment for robust tensor learning.

PubMed

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

Pre-Service Teachers' Uses of and Barriers from Adopting Computer-Assisted Language Learning (CALL) Programs

ERIC Educational Resources Information Center

Samani, Ebrahim; Baki, Roselan; Razali, Abu Bakar

2014-01-01

Success in implementation of computer-assisted language learning (CALL) programs depends on the teachers' understanding of the roles of CALL programs in education. Consequently, it is also important to understand the barriers teachers face in the use of computer-assisted language learning (CALL) programs. The current study was conducted on 14…

The Ghost in the Machine: Are "Teacherless" CALL Programs Really Possible?

ERIC Educational Resources Information Center

Davies, Ted; Williamson, Rodney

1998-01-01

Reflects critically on pedagogical issues in the production of computer-assisted language learning (CALL) courseware and ways CALL has affected the practice of language learning. Concludes that if CALL is to reach full potential, it must be more than a simple medium of information; it should provide a teaching/learning process, with the real…

Evolution of learning strategies in temporally and spatially variable environments: A review of theory

PubMed Central

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

PubMed

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

Design and Effects of Scenario Educational Software.

ERIC Educational Resources Information Center

Keegan, Mark

1993-01-01

Describes the development of educational computer software called scenario software that was designed to incorporate advances in cognitive, affective, and physiological research. Instructional methods are outlined; the need to change from didactic methods to discovery learning is explained; and scenario software design features are discussed. (24…

Group-sparse representation with dictionary learning for medical image denoising and fusion.

PubMed

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer-Assisted Language Learning (CALL) in Support of (Re)-Learning Native Languages: The Case of Runyakitara

ERIC Educational Resources Information Center

Katushemererwe, Fridah; Nerbonne, John

2015-01-01

This study presents the results from a computer-assisted language learning (CALL) system of Runyakitara (RU_CALL). The major objective was to provide an electronic language learning environment that can enable learners with mother tongue deficiencies to enhance their knowledge of grammar and acquire writing skills in Runyakitara. The system…

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

PubMed

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Artificial Intelligence in Cardiology.

PubMed

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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.

Critically Evaluating Prensky in a Language Learning Context: The "Digital Natives/Immigrants Debate" and Its Implications for CALL

ERIC Educational Resources Information Center

Benini, Silvia; Murray, Liam

2013-01-01

More than 10 years have passed since the first introduction of the term "digital natives" in Prensky's (2001a, 2001b) two seminal articles. Prensky argues that students today, having grown up in the Digital Age, learn differently from their predecessors, or "digital immigrants". As such, the pedagogical tools and methods used…

The "P2P" Educational Model Providing Innovative Learning by Linking Technology, Business and Research

ERIC Educational Resources Information Center

Dickinson, Paul Gordon

2017-01-01

This paper evaluates the effect and potential of a new educational learning model called Peer to Peer (P2P). The study was focused on Laurea, Hyvinkaa's Finland campus and its response to bridging the gap between traditional educational methods and working reality, where modern technology plays an important role. The study describes and evaluates…

Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

NASA Astrophysics Data System (ADS)

Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

2018-02-01

The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

Evaluating Blended and Flipped Instruction in Numerical Methods at Multiple Engineering Schools

ERIC Educational Resources Information Center

Clark, Renee; Kaw, Autar; Lou, Yingyan; Scott, Andrew; Besterfield-Sacre, Mary

2018-01-01

With the literature calling for comparisons among technology-enhanced or active-learning pedagogies, a blended versus flipped instructional comparison was made for numerical methods coursework using three engineering schools with diverse student demographics. This study contributes to needed comparisons of enhanced instructional approaches in STEM…

Persuasive Conversational Agent with Persuasion Tactics

NASA Astrophysics Data System (ADS)

Narita, Tatsuya; Kitamura, Yasuhiko

Persuasive conversational agents persuade people to change their attitudes or behaviors through conversation, and are expected to be applied as virtual sales clerks in e-shopping sites. As an approach to create such an agent, we have developed a learning agent with the Wizard of Oz method in which a person called Wizard talks to the user pretending to be the agent. The agent observes the conversations between the Wizard and the user, and learns how to persuade people. In this method, the Wizard has to reply to most of the user's inputs at the beginning, but the burden gradually falls because the agent learns how to reply as the conversation model grows.

Effects-Driven Participatory Design: Learning from Sampling Interruptions.

PubMed

Brandrup, Morten; Østergaard, Kija Lin; Hertzum, Morten; Karasti, Helena; Simonsen, Jesper

2017-01-01

Participatory design (PD) can play an important role in obtaining benefits from healthcare information technologies, but we contend that to fulfil this role PD must incorporate feedback from real use of the technologies. In this paper we describe an effects-driven PD approach that revolves around a sustained focus on pursued effects and uses the experience sampling method (ESM) to collect real-use feedback. To illustrate the use of the method we analyze a case that involves the organizational implementation of electronic whiteboards at a Danish hospital to support the clinicians' intra- and interdepartmental coordination. The hospital aimed to reduce the number of phone calls involved in coordinating work because many phone calls were seen as unnecessary interruptions. To learn about the interruptions we introduced an app for capturing quantitative data and qualitative feedback about the phone calls. The investigation showed that the electronic whiteboards had little potential for reducing the number of phone calls at the operating ward. The combination of quantitative data and qualitative feedback worked both as a basis for aligning assumptions to data and showed ESM as an instrument for triggering in-situ reflection. The participant-driven design and redesign of the way data were captured by means of ESM is a central contribution to the understanding of how to conduct effects-driven PD.

Dynamic Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality

PubMed Central

Mena, Luis J.; Orozco, Eber E.; Felix, Vanessa G.; Ostos, Rodolfo; Melgarejo, Jesus; Maestre, Gladys E.

2012-01-01

Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research. In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED. We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases. Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy. The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses. PMID:22924062

Visual question answering using hierarchical dynamic memory networks

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Duan, Zhikui; Huang, Junwei

2018-04-01

Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What's more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.

DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.

PubMed

Kalsi, Shruti; Kaur, Harleen; Chang, Victor

2017-12-05

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

Methods of Conceptual Clustering and their Relation to Numerical Taxonomy.

DTIC Science & Technology

1985-07-22

the conceptual clustering problem is to first solve theaggregation problem, and then the characterization problem. In machine learning, the...cluster- ings by first generating some number of possible clusterings. For each clustering generated, one calls a learning from examples subroutine, which...class 1 from class 2, and vice versa, only the first combination implies a partition over the set of theoretically possible objects. The first

Knowledge, Attitudes and Behaviors of Older Adults and College Students Participating in Recycling Mentors, a Service-Learning, Environmental Health Education Program

ERIC Educational Resources Information Center

D'Abundo, Michelle Lee; Fugate-Whitlock, Elizabeth; Fiala, Kelly Ann; Covan, Eleanor Krassen

2013-01-01

Purpose: The purpose of this research was to assess the knowledge, attitudes and practices of both students and older adults that participated in a service-learning, environmental health education program called Recycling Mentors (RM). Methods: Surveys were conducted before and after participation in RM. Quantitative data were analyzed using SPSS.…

Innovation in Sales Training

ERIC Educational Resources Information Center

Spencer, R. W.

1974-01-01

The British Gas Corporation has formulated and refined the incident process of training into their own method, which they call developing case study. Sales trainees learn indoor and outdoor sales techniques for selling central heating through self-taught case studies. (DS)

An agent-based model of dialect evolution in killer whales.

PubMed

Filatova, Olga A; Miller, Patrick J O

2015-05-21

The killer whale is one of the few animal species with vocal dialects that arise from socially learned group-specific call repertoires. We describe a new agent-based model of killer whale populations and test a set of vocal-learning rules to assess which mechanisms may lead to the formation of dialect groupings observed in the wild. We tested a null model with genetic transmission and no learning, and ten models with learning rules that differ by template source (mother or matriline), variation type (random errors or innovations) and type of call change (no divergence from kin vs. divergence from kin). The null model without vocal learning did not produce the pattern of group-specific call repertoires we observe in nature. Learning from either mother alone or the entire matriline with calls changing by random errors produced a graded distribution of the call phenotype, without the discrete call types observed in nature. Introducing occasional innovation or random error proportional to matriline variance yielded more or less discrete and stable call types. A tendency to diverge from the calls of related matrilines provided fast divergence of loose call clusters. A pattern resembling the dialect diversity observed in the wild arose only when rules were applied in combinations and similar outputs could arise from different learning rules and their combinations. Our results emphasize the lack of information on quantitative features of wild killer whale dialects and reveal a set of testable questions that can draw insights into the cultural evolution of killer whale dialects. Copyright © 2015 Elsevier Ltd. All rights reserved.

Is CALL Obsolete? Language Acquisition and Language Learning Revisited in a Digital Age

ERIC Educational Resources Information Center

Jarvis, Huw; Krashen, Stephen

2014-01-01

In this article, Huw Jarvis and Stephen Krashen ask "Is CALL Obsolete?" When the term CALL (Computer-Assisted Language Learning) was introduced in the 1960s, the language education profession knew only about language learning, not language acquisition, and assumed the computer's primary contribution to second language acquisition…

Rona's Story and the Theory of Symbolic Interactionism

ERIC Educational Resources Information Center

Naveh, Nissan

2010-01-01

This article presents a method for teaching the theory of symbolic interactionism in a high-school course--Introduction to Sociology. The role-playing game used as a method for teaching the theory is grounded on a philosophy of education whose principles call for meaningful and relevant learning, based on experiential activity and investigation of…

Phase transitions between lower and higher level management learning in times of crisis: an experimental study based on synergetics.

PubMed

Liening, Andreas; Strunk, Guido; Mittelstadt, Ewald

2013-10-01

Much has been written about the differences between single- and double-loop learning, or more general between lower level and higher level learning. Especially in times of a fundamental crisis, a transition between lower and higher level learning would be an appropriate reaction to a challenge coming entirely out of the dark. However, so far there is no quantitative method to monitor such a transition. Therefore we introduce theory and methods of synergetics and present results from an experimental study based on the simulation of a crisis within a business simulation game. Hypothesized critical fluctuations - as a marker for so-called phase transitions - have been assessed with permutation entropy. Results show evidence for a phase transition during the crisis, which can be interpreted as a transition between lower and higher level learning.

Computer-based learning: interleaving whole and sectional representation of neuroanatomy.

PubMed

Pani, John R; Chariker, Julia H; Naaz, Farah

2013-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). Copyright © 2012 American Association of Anatomists.

Computer-Based Learning: Interleaving Whole and Sectional Representation of Neuroanatomy

PubMed Central

Pani, John R.; Chariker, Julia H.; Naaz, Farah

2015-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). PMID:22761001

Developing CALL to Meet the Needs of Language Teaching and Learning

ERIC Educational Resources Information Center

Jiang, Zhaofeng

2008-01-01

This paper illustrates the advantages and disadvantages of CALL. It points out that CALL is influenced by traditional language teaching and learning approaches to some extent. It concludes that what is important in our university system is that CALL design and implementation should match the users' needs, since CALL is not always better than…

Linking CALL and SLA: Using the IRIS Database to Locate Research Instruments

ERIC Educational Resources Information Center

Handley, Zöe; Marsden, Emma

2014-01-01

To establish an evidence base for future computer-assisted language learning (CALL) design, CALL research needs to move away from CALL versus non-CALL comparisons, and focus on investigating the differential impact of individual coding elements, that is, specific features of a technology which might have an impact on learning (Pederson, 1987).…

Discrete Dynamical Modeling.

ERIC Educational Resources Information Center

Sandefur, James T.

1991-01-01

Discussed is the process of translating situations involving changing quantities into mathematical relationships. This process, called dynamical modeling, allows students to learn new mathematics while sharpening their algebraic skills. A description of dynamical systems, problem-solving methods, a graphical analysis, and available classroom…

Convex formulation of multiple instance learning from positive and unlabeled bags.

PubMed

Bao, Han; Sakai, Tomoya; Sato, Issei; Sugiyama, Masashi

2018-05-24

Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available. MIL has a variety of applications such as content-based image retrieval, text categorization, and medical diagnosis. Most of the previous work for MIL assume that training bags are fully labeled. However, it is often difficult to obtain an enough number of labeled bags in practical situations, while many unlabeled bags are available. A learning framework called PU classification (positive and unlabeled classification) can address this problem. In this paper, we propose a convex PU classification method to solve an MIL problem. We experimentally show that the proposed method achieves better performance with significantly lower computation costs than an existing method for PU-MIL. Copyright © 2018 Elsevier Ltd. All rights reserved.

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Huang, H.; Liu, J.; Pan, Y.

2012-07-01

The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task demonstrate that this method outperforms current state-of-the-art HSI-classification methods.

Manifold Regularized Experimental Design for Active Learning.

PubMed

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

Direct Importance Estimation with Gaussian Mixture Models

NASA Astrophysics Data System (ADS)

Yamada, Makoto; Sugiyama, Masashi

The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.

Using Computers in Relation to Learning Climate in CLIL Method

ERIC Educational Resources Information Center

Binterová, Helena; Komínková, Olga

2013-01-01

The main purpose of the work is to present a successful implementation of CLIL method in Mathematics lessons in elementary schools. Nowadays at all types of schools (elementary schools, high schools and universities) all over the world every school subject tends to be taught in a foreign language. In 2003, a document called Action plan for…

Learning as Researchers and Teachers: The Development of a Pedagogical Culture for Social Science Research Methods?

ERIC Educational Resources Information Center

Kilburn, Daniel; Nind, Melanie; Wiles, Rose

2014-01-01

In light of calls to improve the capacity for social science research within UK higher education, this article explores the possibilities for an emerging pedagogy for research methods. A lack of pedagogical culture in this field has been identified by previous studies. In response, we examine pedagogical literature surrounding approaches for…

How to Help Students Conceptualize the Rigorous Definition of the Limit of a Sequence

ERIC Educational Resources Information Center

Roh, Kyeong Hah

2010-01-01

This article suggests an activity, called the epsilon-strip activity, as an instructional method for conceptualization of the rigorous definition of the limit of a sequence via visualization. The article also describes the learning objectives of each instructional step of the activity, and then provides detailed instructional methods to guide…

D'Nealian Manuscript--An Aid to Reading Development.

ERIC Educational Resources Information Center

Thurber, Donald N.

A new method of continuous stroke manuscript print called D'Nealian Manuscript is challenging the traditional circle-stick method of teaching children how to write. The circle-stick uses component or splinter parts to form whole letters. Children are forced to form all writing with verticle lines and to learn a manuscript print that goes nowhere.…

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

Jamieson, Kevin; Davis, IV, Warren L.

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building themore » system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.« less

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.

Automated discovery systems and the inductivist controversy

NASA Astrophysics Data System (ADS)

Giza, Piotr

2017-09-01

The paper explores possible influences that some developments in the field of branches of AI, called automated discovery and machine learning systems, might have upon some aspects of the old debate between Francis Bacon's inductivism and Karl Popper's falsificationism. Donald Gillies facetiously calls this controversy 'the duel of two English knights', and claims, after some analysis of historical cases of discovery, that Baconian induction had been used in science very rarely, or not at all, although he argues that the situation has changed with the advent of machine learning systems. (Some clarification of terms machine learning and automated discovery is required here. The key idea of machine learning is that, given data with associated outcomes, software can be trained to make those associations in future cases which typically amounts to inducing some rules from individual cases classified by the experts. Automated discovery (also called machine discovery) deals with uncovering new knowledge that is valuable for human beings, and its key idea is that discovery is like other intellectual tasks and that the general idea of heuristic search in problem spaces applies also to discovery tasks. However, since machine learning systems discover (very low-level) regularities in data, throughout this paper I use the generic term automated discovery for both kinds of systems. I will elaborate on this later on). Gillies's line of argument can be generalised: thanks to automated discovery systems, philosophers of science have at their disposal a new tool for empirically testing their philosophical hypotheses. Accordingly, in the paper, I will address the question, which of the two philosophical conceptions of scientific method is better vindicated in view of the successes and failures of systems developed within three major research programmes in the field: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group and the programme called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett and P. Thagard.

Diverse expected gradient active learning for relative attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-07-01

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called diverse expected gradient active learning. This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multiclass distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Diverse Expected Gradient Active Learning for Relative Attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-06-02

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Learning to improve iterative repair scheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene

1992-01-01

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

Enhancing the Design and Analysis of Flipped Learning Strategies

ERIC Educational Resources Information Center

Jenkins, Martin; Bokosmaty, Rena; Brown, Melanie; Browne, Chris; Gao, Qi; Hanson, Julie; Kupatadze, Ketevan

2017-01-01

There are numerous calls in the literature for research into the flipped learning approach to match the flood of popular media articles praising its impact on student learning and educational outcomes. This paper addresses those calls by proposing pedagogical strategies that promote active learning in "flipped" approaches and improved…

Facts and fiction of learning systems. [decision making intelligent control

NASA Technical Reports Server (NTRS)

Saridis, G. N.

1975-01-01

The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

Social Networking and the Affective Domain of Learning

ERIC Educational Resources Information Center

Carrigan, Robert L.

2013-01-01

In 2006, the U.S. Department of Education commissioned a report called, "Charting the Future of U.S. Higher Education", asking educators to, "...test new teaching methods, content deliveries, and innovative pedagogies using technology-based collaborative applications" (p. 6). Fittingly, technology-based collaborative…

Employer-Led Quality Assurance

ERIC Educational Resources Information Center

Tyszko, Jason A.

2017-01-01

Recent criticism of higher education accreditation has prompted calls for reform and sparked interest in piloting alternative quality assurance methods that better address student learning and employment outcomes. Although this debate has brought much needed attention to improving the outcomes of graduates and safeguarding federal investment in…

A CALL-Based Lesson Plan for Teaching Reading Comprehension to Iranian Intermediate EFL Learners

ERIC Educational Resources Information Center

Khoshsima, Hooshang; Khosravani, Mahboobeh

2014-01-01

The main purpose of this descriptive research is to provide a CALL (Computer-Assisted Language Learning)-based lesson plan for teaching reading comprehension to Iranian intermediate EFL learners. CALL is a new way of learning and teaching language. It is proved that CALL mainly has positive effects on educational contexts. Although teachers…

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

PubMed

Skoraczyński, G; Dittwald, P; Miasojedow, B; Szymkuć, S; Gajewska, E P; Grzybowski, B A; Gambin, A

2017-06-15

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.

Implementation of a Learning Design Run-Time Environment for the .LRN Learning Management System

ERIC Educational Resources Information Center

del Cid, Jose Pablo Escobedo; de la Fuente Valentin, Luis; Gutierrez, Sergio; Pardo, Abelardo; Kloos, Carlos Delgado

2007-01-01

The IMS Learning Design specification aims at capturing the complete learning flow of courses, without being restricted to a particular pedagogical model. Such flow description for a course, called a Unit of Learning, must be able to be reproduced in different systems using a so called run-time environment. In the last few years there has been…

Biomimetic molecular design tools that learn, evolve, and adapt.

PubMed

Winkler, David A

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

Biomimetic molecular design tools that learn, evolve, and adapt

PubMed Central

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

PubMed

Sun, Shiliang; Xie, Xijiong

2016-09-01

Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Speech Recognition Software for Language Learning: Toward an Evaluation of Validity and Student Perceptions

ERIC Educational Resources Information Center

Cordier, Deborah

2009-01-01

A renewed focus on foreign language (FL) learning and speech for communication has resulted in computer-assisted language learning (CALL) software developed with Automatic Speech Recognition (ASR). ASR features for FL pronunciation (Lafford, 2004) are functional components of CALL designs used for FL teaching and learning. The ASR features…

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

The Montessori Method and the Kindergarten. Bulletin, 1914, No. 28. Whole Number 602

ERIC Educational Resources Information Center

Harrison, Elizabeth

1914-01-01

Recently an earnest, brilliant, and learned Italian woman, Dr. Maria Montessori, has become famous, probably beyond her desire, for her contribution to the knowledge of little children and for the embodiment of her own and the discoveries of others in what she likes to call "a method of a new science of education." Her scientific investigations as…

The Viability of Portraiture for Science Education Research: Learning from Portraits of Two Science Classrooms

ERIC Educational Resources Information Center

Quigley, Cassie; Trauth-Nare, Amy; Beeman-Cadwallader, Nicole

2015-01-01

The purpose of this paper is to describe the relevance of a qualitative methodology called portraiture for science education. Portraiture is a method of inquiry that blends art and science by combining the empirical aspects of inquiry with beauty and aesthetic properties. This method encompasses all aspects of a research study, including protocol,…

The Role of Methods Textbooks in Providing Early Training for Teaching with Technology in the Language Classroom

ERIC Educational Resources Information Center

Arnold, Nike

2013-01-01

The ability to make effective use of technology is becoming increasingly important for prospective language teachers. As a result, many teacher preparation programs include some form of training in computer assisted language learning (CALL). This study focuses on one component of such training, the textbooks used in methods courses, and employs…

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

PubMed

Kamimura, Ryotaro

2011-05-01

In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

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

Ma, Xiaoyao; Hall, Randall W.; Löffler, Frank

The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initio ground state energies for multiple geometries of the H2O, N2, and F2 molecules. The method is based on Feynman’s path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the linear combinations of Slater determinants which are used in the second stage, a conventional QMC simulation. The method is tested using different vector spaces and compared to the results of other quantum chemical methodsmore » and to exact diagonalization. Our findings demonstrate that the SiLK method is accurate and reduces or eliminates the minus sign problem.« less

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.

The Internet, Language Learning, and International Dialogue: Constructing Online Foreign Language Learning Websites

ERIC Educational Resources Information Center

Kartal, Erdogan; Uzun, Levent

2010-01-01

In the present study we call attention to the close connection between languages and globalization, and we also emphasize the importance of the Internet and online websites in foreign language teaching and learning as unavoidable elements of computer assisted language learning (CALL). We prepared a checklist by which we investigated 28 foreign…

CALL Vocabulary Learning in Japanese: Does Romaji Help Beginners Learn More Words?

ERIC Educational Resources Information Center

Okuyama, Yoshiko

2007-01-01

This study investigated the effects of using Romanized spellings on beginner-level Japanese vocabulary learning. Sixty-one first-semester students at two universities in Arizona were both taught and tested on 40 Japanese content words in a computer-assisted language learning (CALL) program. The primary goal of the study was to examine whether the…

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

Recursive heuristic classification

NASA Technical Reports Server (NTRS)

Wilkins, David C.

1994-01-01

The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

Multistrategy Self-Organizing Map Learning for Classification Problems

PubMed Central

Hasan, S.; Shamsuddin, S. M.

2011-01-01

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test. PMID:21876686

Autonomous learning in gesture recognition by using lobe component analysis

NASA Astrophysics Data System (ADS)

Lu, Jian; Weng, Juyang

2007-02-01

Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

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

Ma, Xiaoyao; Hall, Randall W.; Department of Chemistry, Louisiana State University, Baton Rouge, Louisiana 70803

The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initio ground state energies for multiple geometries of the H{sub 2}O, N{sub 2}, and F{sub 2} molecules. The method is based on Feynman’s path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the linear combinations of Slater determinants which are used in the second stage, a conventional QMC simulation. The method is tested using different vector spaces and compared to the results of othermore » quantum chemical methods and to exact diagonalization. Our findings demonstrate that the SiLK method is accurate and reduces or eliminates the minus sign problem.« less

The Case of the "Open Secrets": Increasing the Effectiveness of Instructional Supervision.

ERIC Educational Resources Information Center

Duffy, Francis M.

Conditions in schools that reduce the effectiveness and perceived value of instructional supervision can be diagnosed and corrected through a cyclical process called "organizational learning." Rather than merely responding to symptoms, this method focuses on eliminating or mitigating the underlying causes of "organizational…

Social learning: medical student perceptions of geriatric house calls.

PubMed

Abbey, Linda; Willett, Rita; Selby-Penczak, Rachel; McKnight, Roberta

2010-01-01

Bandura's social learning theory provides a useful conceptual framework to understand medical students' perceptions of a house calls experience at Virginia Commonwealth University School of Medicine. Social learning and role modeling reflect Liaison Committee on Medical Education guidelines for "Medical schools (to) ensure that the learning environment for medical students promotes the development of explicit and appropriate professional attributes (attitudes, behaviors, and identity) in their medical students." This qualitative study reports findings from open-ended survey questions from 123 medical students who observed a preceptor during house calls to elderly homebound patients. Their comments included reflections on the medical treatment as well as interactions with family and professional care providers. Student insights about the social learning process they experienced during house calls to geriatric patients characterized physician role models as dedicated, compassionate, and communicative. They also described patient care in the home environment as comprehensive, personalized, more relaxed, and comfortable. Student perceptions reflect an appreciation of the richness and complexity of details learned from home visits and social interaction with patients, families, and caregivers.

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

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

Integrated Language Skills CALL Course Design

ERIC Educational Resources Information Center

Watson, Kevin; Agawa, Grant

2013-01-01

The importance of a structured learning framework or interrelated frameworks is the cornerstone of a solid English as a foreign language (EFL) computer-assisted language learning (CALL) curriculum. While the benefits of CALL are widely promoted in the literature, there is often an endemic discord separating theory and practice. Oftentimes the…

Integrating Computer-Assisted Language Learning in Saudi Schools: A Change Model

ERIC Educational Resources Information Center

Alresheed, Saleh; Leask, Marilyn; Raiker, Andrea

2015-01-01

Computer-assisted language learning (CALL) technology and pedagogy have gained recognition globally for their success in supporting second language acquisition (SLA). In Saudi Arabia, the government aims to provide most educational institutions with computers and networking for integrating CALL into classrooms. However, the recognition of CALL's…

A Study of Multimedia Application-Based Vocabulary Acquisition

ERIC Educational Resources Information Center

Shao, Jing

2012-01-01

The development of computer-assisted language learning (CALL) has created the opportunity for exploring the effects of the multimedia application on foreign language vocabulary acquisition in recent years. This study provides an overview the computer-assisted language learning (CALL) and detailed a developing result of CALL--multimedia. With the…

Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

PubMed Central

Hauschild, Anne-Christin; Kopczynski, Dominik; D’Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-01-01

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME). We manually generated a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications. PMID:24957992

Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches.

PubMed

Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-04-16

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

Combining advanced networked technology and pedagogical methods to improve collaborative distance learning.

PubMed

Staccini, Pascal; Dufour, Jean-Charles; Raps, Hervé; Fieschi, Marius

2005-01-01

Making educational material be available on a network cannot be reduced to merely implementing hypermedia and interactive resources on a server. A pedagogical schema has to be defined to guide students for learning and to provide teachers with guidelines to prepare valuable and upgradeable resources. Components of a learning environment, as well as interactions between students and other roles such as author, tutor and manager, can be deduced from cognitive foundations of learning, such as the constructivist approach. Scripting the way a student will to navigate among information nodes and interact with tools to build his/her own knowledge can be a good way of deducing the features of the graphic interface related to the management of the objects. We defined a typology of pedagogical resources, their data model and their logic of use. We implemented a generic and web-based authoring and publishing platform (called J@LON for Join And Learn On the Net) within an object-oriented and open-source programming environment (called Zope) embedding a content management system (called Plone). Workflow features have been used to mark the progress of students and to trace the life cycle of resources shared by the teaching staff. The platform integrated advanced on line authoring features to create interactive exercises and support live courses diffusion. The platform engine has been generalized to the whole curriculum of medical studies in our faculty; it also supports an international master of risk management in health care and will be extent to all other continuous training diploma.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Application of adobe flash media to optimize jigsaw learning model on geometry material

NASA Astrophysics Data System (ADS)

Imam, P.; Imam, S.; Ikrar, P.

2018-05-01

This study aims to determine and describe the effectiveness of the application of adobe flash media for jigsaw learning model on geometry material. In this study, the modified jigsaw learning with adobe flash media is called jigsaw-flash model. This research was conducted in Surakarta. The research method used is mix method research with exploratory sequential strategy. The results of this study indicate that students feel more comfortable and interested in studying geometry material taught by jigsaw-flash model. In addition, students taught using the jigsaw-flash model are more active and motivated than the students who were taught using ordinary jigsaw models. This shows that the use of the jigsaw-flash model can increase student participation and motivation. It can be concluded that the adobe flash media can be used as a solution to reduce the level of student abstraction in learning mathematics.

User-Centered Computer Aided Language Learning

ERIC Educational Resources Information Center

Zaphiris, Panayiotis, Ed.; Zacharia, Giorgos, Ed.

2006-01-01

In the field of computer aided language learning (CALL), there is a need for emphasizing the importance of the user. "User-Centered Computer Aided Language Learning" presents methodologies, strategies, and design approaches for building interfaces for a user-centered CALL environment, creating a deeper understanding of the opportunities and…

Social Learning: Medical Student Perceptions of Geriatric House Calls

ERIC Educational Resources Information Center

Abbey, Linda; Willett, Rita; Selby-Penczak, Rachel; McKnight, Roberta

2010-01-01

Bandura's social learning theory provides a useful conceptual framework to understand medical students' perceptions of a house calls experience at Virginia Commonwealth University School of Medicine. Social learning and role modeling reflect Liaison Committee on Medical Education guidelines for "Medical schools (to) ensure that the learning…

Modeling Learning Processes in Lexical CALL.

ERIC Educational Resources Information Center

Goodfellow, Robin; Laurillard, Diana

1994-01-01

Studies the performance of a novice Spanish student using a Computer-assisted language learning (CALL) system designed for vocabulary enlargement. Results indicate that introspective evidence may be used to validate performance data within a theoretical framework that characterizes the learning approach as "surface" or "deep." (25 references)…

Professional Learning in Canada: Learning Forward Releases a Landmark Study and Call to Action

ERIC Educational Resources Information Center

Learning Professional, 2017

2017-01-01

Learning Forward recently released findings from a new study that fills a long-standing gap in existing Pan-Canadian research, identifying key components of effective professional learning based on findings from educators' experiences in Canada. Accompanying the study is a call to action by Michael Fullan and Andy Hargreaves making the case for a…

Task Based Language Teaching: Development of CALL

ERIC Educational Resources Information Center

Anwar, Khoirul; Arifani, Yudhi

2016-01-01

The dominant complexities of English teaching in Indonesia are about limited development of teaching methods and materials which still cannot optimally reflect students' needs (in particular of how to acquire knowledge and select the most effective learning models). This research is to develop materials with complete task-based activities by using…

The Project Method as Practice of Study Activation

ERIC Educational Resources Information Center

Fazlyeva, Zulfiya Kh.; Sheinina, Dina P.; Deputatova, Natalia A.

2016-01-01

Relevance of the problem stated in the article is determined by new teaching approach uniting the traditional teaching experience with that of the modern information technologies, all being merged into a new course of the computer lingua-didactics (the international term of which is "Computer Assisted Language Learning" (CALL) or…

Methods and Strategies: Science Notebooks as Learning Tools

ERIC Educational Resources Information Center

Fulton, Lori

2017-01-01

Writing in science is a natural way to integrate science and literacy and meet the goals set by the "Next Generation Science Standards" ("NGSS") and the "Common Core State Standards" ("CCSS"), which call for learners to be engaged with the language of science. This means that students should record…

Intimate Debate Technique: Medicinal Use of Marijuana

ERIC Educational Resources Information Center

Herreid, Clyde Freeman; DeRei, Kristie

2007-01-01

Classroom debates used to be familiar exercises to students schooled in past generations. In this article, the authors describe the technique called "intimate debate". To cooperative learning specialists, the technique is known as "structured debate" or "constructive debate". It is a powerful method for dealing with case topics that involve…

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning.

PubMed

Honkela, Antti; Valpola, Harri

2004-07-01

The bits-back coding first introduced by Wallace in 1990 and later by Hinton and van Camp in 1993 provides an interesting link between Bayesian learning and information-theoretic minimum-description-length (MDL) learning approaches. The bits-back coding allows interpreting the cost function used in the variational Bayesian method called ensemble learning as a code length in addition to the Bayesian view of misfit of the posterior approximation and a lower bound of model evidence. Combining these two viewpoints provides interesting insights to the learning process and the functions of different parts of the model. In this paper, the problem of variational Bayesian learning of hierarchical latent variable models is used to demonstrate the benefits of the two views. The code-length interpretation provides new views to many parts of the problem such as model comparison and pruning and helps explain many phenomena occurring in learning.

Integrating Collaborative and Decentralized Models to Support Ubiquitous Learning

ERIC Educational Resources Information Center

Barbosa, Jorge Luis Victória; Barbosa, Débora Nice Ferrari; Rigo, Sandro José; de Oliveira, Jezer Machado; Rabello, Solon Andrade, Jr.

2014-01-01

The application of ubiquitous technologies in the improvement of education strategies is called Ubiquitous Learning. This article proposes the integration between two models dedicated to support ubiquitous learning environments, called Global and CoolEdu. CoolEdu is a generic collaboration model for decentralized environments. Global is an…

Acceleration of saddle-point searches with machine learning.

PubMed

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning

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

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the numbermore » of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.« less

Sustainability in CALL Learning Environments: A Systemic Functional Grammar Approach

ERIC Educational Resources Information Center

McDonald, Peter

2014-01-01

This research aims to define a sustainable resource in Computer-Assisted Language Learning (CALL). In order for a CALL resource to be sustainable it must work within existing educational curricula. This feature is a necessary prerequisite of sustainability because, despite the potential for educational change that digitalization has offered since…

Factors Affecting the Normalization of CALL in Chinese Senior High Schools

ERIC Educational Resources Information Center

He, Bi; Puakpong, Nattaya; Lian, Andrew

2015-01-01

With the development of Information Technology, increasing attention has been paid to Computer Assisted Language Learning (CALL). Meanwhile, increasing enthusiasm is seen for English learning and teaching in China. Yet, few research studies have focused on the normalization of CALL in ethnically diverse areas. In response to this research gap,…

English Language Teachers' Perceptions of Computer-Assisted Language Learning

ERIC Educational Resources Information Center

Feng, Yu Lin

2012-01-01

A growing number of studies have reported the potential use of computer-assisted language learning (CALL) and other types of technology for ESL and EFL students. So far, most studies on CALL have focused on CALL-classroom comparisons (Chenoweth & Murday, 2003; Chenoweth, Ushida, & Murday, 2007; Fitze, 2006; Neri, Mich, Gerosa, &…

Impact of Using CALL on Iranian EFL Learners' Vocabulary Knowledge

ERIC Educational Resources Information Center

Yunus, Melor Md; Salehi, Hadi; Amini, Mahdi

2016-01-01

Computer Assisted Language Learning (CALL) integration in EFL contexts has intensified noticeably in recent years. This integration might be in different ways and for different purposes such as vocabulary acquisition, grammar learning, phonology, writing skills, etc. More explicitly, this study is an attempt to explore the effect of using CALL on…

CALL and Less Commonly Taught Languages--Still a Way to Go

ERIC Educational Resources Information Center

Ward, Monica

2016-01-01

Many Computer Assisted Language Learning (CALL) innovations mainly apply to the Most Commonly Taught Languages (MCTLs), especially English. Recent manifestations of CALL for MCTLs such as corpora, Mobile Assisted Language Learning (MALL) and Massively Open Online Courses (MOOCs) are found less frequently in the world of Less Commonly Taught…

GP and pharmacist inter-professional learning - a grounded theory study.

PubMed

Cunningham, David E; Ferguson, Julie; Wakeling, Judy; Zlotos, Leon; Power, Ailsa

2016-05-01

Practice Based Small Group Learning (PBSGL) is an established learning resource for primary care clinicians in Scotland and is used by one-third of general practitioners (GPs). Scottish Government and UK professional bodies have called for GPs and pharmacists to work more closely together to improve care. To gain GPs' and pharmacists' perceptions and experiences of learning together in an inter-professional PBSGL pilot. Qualitative research methods involving established GP PBSGL groups in NHS Scotland recruiting one or two pharmacists to join them. A grounded theory method was used. GPs were interviewed in focus groups by a fellow GP, and pharmacists were interviewed individually by two researchers, neither being a GP or a pharmacist. Interviews were audio-recorded, transcribed and analysed using grounded theory methods. Data saturation was achieved and confirmed. Three themes were identified: GPs' and pharmacists' perceptions and experiences of inter-professional learning; Inter-professional relationships and team-working; Group identity and purpose of existing GP groups. Pharmacists were welcomed into GP groups and both professions valued inter-professional PBSGL learning. Participants learned from each other and both professions gained a wider perspective of the NHS and of each others' roles in the organisation. Inter-professional relationships, communication and team-working were strengthened and professionals regarded each other as peers and friends.

Prediction task guided representation learning of medical codes in EHR.

PubMed

Cui, Liwen; Xie, Xiaolei; Shen, Zuojun

2018-06-18

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples. Copyright © 2018. Published by Elsevier Inc.

Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification.

PubMed

Jowkar, Gholam-Hossein; Mansoori, Eghbal G

2016-10-01

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Pan, Y.; Wu, J.; Huang, H.; Liu, J.

2012-08-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

Attitudes of Jordanian Undergraduate Students towards Using Computer Assisted Language Learning (CALL)

ERIC Educational Resources Information Center

Saeed, Farah Jamal Abed Alrazeq; Al-Zayed, Norma Nawaf

2018-01-01

The study aimed at investigating the attitudes of Jordanian undergraduate students towards using computer assisted-language learning (CALL) and its effectiveness in the process of learning the English language. In order to fulfill the study's objective, the researchers used a questionnaire to collect data, followed-up with semi-structured…

Learning Design: Reflections upon the Current Landscape

ERIC Educational Resources Information Center

Mor, Yishay; Craft, Brock

2012-01-01

The mounting wealth of open and readily available information and the accelerated evolution of social, mobile and creative technologies call for a re-conceptualisation of the role of educators: from providers of knowledge to designers of learning. This call is reverberated by the rising trend of research in learning design (LD). Addressing this,…

Studying Language Learning Opportunities Afforded by a Collaborative CALL Task

ERIC Educational Resources Information Center

Leahy, Christine

2016-01-01

This research study explores the learning potential of a computer-assisted language learning (CALL) activity. Research suggests that the dual emphasis on content development and language accuracy, as well as the complexity of L2 production in natural settings, can potentially create cognitive overload. This study poses the question whether, and…

Student Teachers and CALL: Personal and Pedagogical Uses and Beliefs

ERIC Educational Resources Information Center

Hlas, Anne Cummings; Conroy, Kelly; Hildebrandt, Susan A.

2017-01-01

The student teaching semester affords teacher candidates the chance to apply what they have learned during their teacher preparation coursework. Therefore, it can be a prime opportunity for student teachers to use technology for their own language learning and to implement computer assisted language learning (CALL) in their instruction. This study…

Interdisciplinary Research at the Intersection of CALL, NLP, and SLA: Methodological Implications from an Input Enhancement Project

ERIC Educational Resources Information Center

Ziegler, Nicole; Meurers, Detmar; Rebuschat, Patrick; Ruiz, Simón; Moreno-Vega, José L.; Chinkina, Maria; Li, Wenjing; Grey, Sarah

2017-01-01

Despite the promise of research conducted at the intersection of computer-assisted language learning (CALL), natural language processing, and second language acquisition, few studies have explored the potential benefits of using intelligent CALL systems to deepen our understanding of the process and products of second language (L2) learning. The…

A Comparison of Authoring Software for Developing Mathematics Self-Learning Software Packages.

ERIC Educational Resources Information Center

Suen, Che-yin; Pok, Yang-ming

Four years ago, the authors started to develop a self-paced mathematics learning software called NPMaths by using an authoring package called Tencore. However, NPMaths had some weak points. A development team was hence formed to develop similar software called Mathematics On Line. This time the team used another development language called…

CALL in the Year 2000: A Look Back from 2016

ERIC Educational Resources Information Center

Chapelle, Carol A.

2016-01-01

This commentary offers a brief reflection on the state of CALL in 1997, when "Language Learning & Technology" was launched with my paper entitled "CALL in the year 2000: Still in search of research paradigms?" The point of my 1997 paper was to suggest the potential value of research on second language learning for the study…

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.

Student anxiety in introductory biology classrooms: Perceptions about active learning and persistence in the major.

PubMed

England, Benjamin J; Brigati, Jennifer R; Schussler, Elisabeth E

2017-01-01

Many researchers have called for implementation of active learning practices in undergraduate science classrooms as one method to increase retention and persistence in STEM, yet there has been little research on the potential increases in student anxiety that may accompany these practices. This is of concern because excessive anxiety can decrease student performance. Levels and sources of student anxiety in three introductory biology lecture classes were investigated via an online survey and student interviews. The survey (n = 327) data revealed that 16% of students had moderately high classroom anxiety, which differed among the three classes. All five active learning classroom practices that were investigated caused student anxiety, with students voluntarily answering a question or being called on to answer a question causing higher anxiety than working in groups, completing worksheets, or answering clicker questions. Interviews revealed that student anxiety seemed to align with communication apprehension, social anxiety, and test anxiety. Additionally, students with higher general anxiety were more likely to self-report lower course grade and the intention to leave the major. These data suggest that a subset of students in introductory biology experience anxiety in response to active learning, and its potential impacts should be investigated.

Student anxiety in introductory biology classrooms: Perceptions about active learning and persistence in the major

PubMed Central

2017-01-01

Many researchers have called for implementation of active learning practices in undergraduate science classrooms as one method to increase retention and persistence in STEM, yet there has been little research on the potential increases in student anxiety that may accompany these practices. This is of concern because excessive anxiety can decrease student performance. Levels and sources of student anxiety in three introductory biology lecture classes were investigated via an online survey and student interviews. The survey (n = 327) data revealed that 16% of students had moderately high classroom anxiety, which differed among the three classes. All five active learning classroom practices that were investigated caused student anxiety, with students voluntarily answering a question or being called on to answer a question causing higher anxiety than working in groups, completing worksheets, or answering clicker questions. Interviews revealed that student anxiety seemed to align with communication apprehension, social anxiety, and test anxiety. Additionally, students with higher general anxiety were more likely to self-report lower course grade and the intention to leave the major. These data suggest that a subset of students in introductory biology experience anxiety in response to active learning, and its potential impacts should be investigated. PMID:28771564

Measurement of Employability Skills on Teaching Factory Learning

NASA Astrophysics Data System (ADS)

Subekti, S.; Ana, A.

2018-02-01

Vocational High Schools as one of the educational institutions that has the responsibility in preparing skilled labors has a challenge to improve the quality of human resources as a candidate for skilled labors, to compete and survive in a changing climate of work. BPS noted an increase in the number of non-worker population (BAK) in 2015-2017 on vocational graduates as many as 564,272 people. The ability to adapt and maintain jobs in a variety of conditions is called employability skills. This study purpose to measure the development of employability skills of communication skills, problem-solving skills and teamwork skills on the implementation of teaching factory learning in SMK Negeri 1 Cibadak, THPH Skills Program on bakery competency. This research uses mixed method, with concurrent triangulation mix methods research design. Data collection techniques used interviews and questionnaires. The result shows that there are increasing students’ employability skills in communication skills, problem solving skills, and teamwork skills in teaching factory learning. Principles of learning that apply learning by doing student centering and learning arrangements such as situations and conditions in the workplace have an impact on improving student employability skills.

Algorithms that Defy the Gravity of Learning Curve

DTIC Science & Technology

2017-04-28

three nearest neighbour-based anomaly detectors, i.e., an ensemble of nearest neigh- bours, a recent nearest neighbour-based ensemble method called iNNE...streams. Note that the change in sample size does not alter the geometrical data characteristics discussed here. 3.1 Experimental Methodology ...need to be answered. 3.6 Comparison with conventional ensemble methods Given the theoretical results, the third aim of this project (i.e., identify the

Invention Versus Direct Instruction: For Some Content, It's a Tie

NASA Astrophysics Data System (ADS)

Chase, Catherine C.; Klahr, David

2017-12-01

An important, but as yet unresolved pedagogical question is whether discovery-oriented or direct instruction methods lead to greater learning and transfer. We address this issue in a study with 101 fourth and fifth grade students that contrasts two distinct instructional methods. One is a blend of discovery and direct instruction called Invent-then-Tell (IT), and the other is a version of direct instruction called Tell-then-Practice (TP). The relative effectiveness of these methods is compared in the context of learning a critical inquiry skill—the control-of-variables strategy. Previous research has demonstrated the success of IT over TP for teaching deep domain structures, while other research has demonstrated the superiority of direct instruction for teaching simple experimental design, a domain-general inquiry skill. In the present study, students in both conditions made equally large gains on an immediate assessment of their application and conceptual understanding of experimental design, and they also performed similarly on a test of far transfer. These results were fairly consistent across school populations with various levels of prior achievement and socioeconomic status. Findings suggest that broad claims about the relative effectiveness of these two distinct methods should be conditionalized by particular instructional contexts, such as the type of knowledge being taught.

Context-dependent vocal mimicry in a passerine bird.

PubMed

Goodale, Eben; Kotagama, Sarath W

2006-04-07

How do birds select the sounds they mimic, and in what contexts do they use vocal mimicry? Some birds show a preference for mimicking other species' alarm notes, especially in situations when they appear to be alarmed. Yet no study has demonstrated that birds change the call types they mimic with changing contexts. We found that greater racket-tailed drongos (Dicrurus paradiseus) in the rainforest of Sri Lanka mimic the calls of predators and the alarm-associated calls of other species more often than would be expected from the frequency of these sounds in the acoustic environment. Drongos include this alarm-associated mimicry in their own alarm vocalizations, while incorporating other species' songs and contact calls in their own songs. Drongos show an additional level of context specificity by mimicking other species' ground predator-specific call types when mobbing. We suggest that drongos learn other species' calls and their contexts while interacting with these species in mixed flocks. The drongos' behaviour demonstrates that alarm-associated calls can have learned components, and that birds can learn the appropriate usage of calls that encode different types of information.

Context-dependent vocal mimicry in a passerine bird

PubMed Central

Goodale, Eben; Kotagama, Sarath W

2005-01-01

How do birds select the sounds they mimic, and in what contexts do they use vocal mimicry? Some birds show a preference for mimicking other species' alarm notes, especially in situations when they appear to be alarmed. Yet no study has demonstrated that birds change the call types they mimic with changing contexts. We found that greater racket-tailed drongos (Dicrurus paradiseus) in the rainforest of Sri Lanka mimic the calls of predators and the alarm-associated calls of other species more often than would be expected from the frequency of these sounds in the acoustic environment. Drongos include this alarm-associated mimicry in their own alarm vocalizations, while incorporating other species' songs and contact calls in their own songs. Drongos show an additional level of context specificity by mimicking other species' ground predator-specific call types when mobbing. We suggest that drongos learn other species' calls and their contexts while interacting with these species in mixed flocks. The drongos' behaviour demonstrates that alarm-associated calls can have learned components, and that birds can learn the appropriate usage of calls that encode different types of information. PMID:16618682

A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

ERIC Educational Resources Information Center

Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

2015-01-01

This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

Learning Robust and Discriminative Subspace With Low-Rank Constraints.

PubMed

Li, Sheng; Fu, Yun

2016-11-01

In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization-based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The experimental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.

Zebra finch mates use their forebrain song system in unlearned call communication.

PubMed

Ter Maat, Andries; Trost, Lisa; Sagunsky, Hannes; Seltmann, Susanne; Gahr, Manfred

2014-01-01

Unlearned calls are produced by all birds whereas learned songs are only found in three avian taxa, most notably in songbirds. The neural basis for song learning and production is formed by interconnected song nuclei: the song control system. In addition to song, zebra finches produce large numbers of soft, unlearned calls, among which "stack" calls are uttered frequently. To determine unequivocally the calls produced by each member of a group, we mounted miniature wireless microphones on each zebra finch. We find that group living paired males and females communicate using bilateral stack calling. To investigate the role of the song control system in call-based male female communication, we recorded the electrical activity in a premotor nucleus of the song control system in freely behaving male birds. The unique combination of acoustic monitoring together with wireless brain recording of individual zebra finches in groups shows that the neuronal activity of the song system correlates with the production of unlearned stack calls. The results suggest that the song system evolved from a brain circuit controlling simple unlearned calls to a system capable of producing acoustically rich, learned vocalizations.

Zebra Finch Mates Use Their Forebrain Song System in Unlearned Call Communication

PubMed Central

Ter Maat, Andries; Trost, Lisa; Sagunsky, Hannes; Seltmann, Susanne; Gahr, Manfred

2014-01-01

Unlearned calls are produced by all birds whereas learned songs are only found in three avian taxa, most notably in songbirds. The neural basis for song learning and production is formed by interconnected song nuclei: the song control system. In addition to song, zebra finches produce large numbers of soft, unlearned calls, among which “stack” calls are uttered frequently. To determine unequivocally the calls produced by each member of a group, we mounted miniature wireless microphones on each zebra finch. We find that group living paired males and females communicate using bilateral stack calling. To investigate the role of the song control system in call-based male female communication, we recorded the electrical activity in a premotor nucleus of the song control system in freely behaving male birds. The unique combination of acoustic monitoring together with wireless brain recording of individual zebra finches in groups shows that the neuronal activity of the song system correlates with the production of unlearned stack calls. The results suggest that the song system evolved from a brain circuit controlling simple unlearned calls to a system capable of producing acoustically rich, learned vocalizations. PMID:25313846

Using Guided Notes to Enhance Instruction for All Students

ERIC Educational Resources Information Center

Konrad, Moira; Joseph, Laurice M.; Itoi, Madoka

2011-01-01

Taking notes from lectures or reading material can be challenging, especially for those who have learning disabilities. An alternative to traditional note-taking is a method called "guided notes," which has been found to improve the accuracy of students' notes, increase the frequency of student responses, and improve students' quiz and test…

From Taylor to Tyler to "No Child Left Behind": Legitimating Educational Standards

ERIC Educational Resources Information Center

Waldow, Florian

2015-01-01

In the early 20th century, proponents of the so-called "social efficiency movement" in the United States tried to apply methods and concepts for enhancing efficiency in industrial production to the organization of teaching and learning processes. This included the formulation of "educational standards" analogous to industrial…

Cross-Age Reading Buddies and Cultural-Sensitive Literature: Student-Centered English Language Instruction in an Ethiopian Budget School

ERIC Educational Resources Information Center

Cianca, Sherri

2012-01-01

The Ethiopian government has called for educational improvement, emphasizing the employment of active, student-centered pedagogy. One way of maximizing an interactive learning approach involves blending a cross-age reading buddies program with high-quality, culturally relevant children's literature. Employing descriptive, mixed-method research,…

Too Important to Quit: A Call for Teacher Support of Art

ERIC Educational Resources Information Center

Patterson, Jodi A.

2017-01-01

The author argues that general education teacher candidates must learn to "­re-start" art to empower them with the skills needed to realize art's promise within their future classrooms. Entry/exit surveys completed by candidates ­revealed that an art methods course corrected misconceptions about the nature of creativity and improved…

Getting Students to Read before Class: Innovation in a University in Chile

ERIC Educational Resources Information Center

McGinn, Noel F.; Schiefelbein, Ernesto

2015-01-01

Reading before class has been demonstrated to improve student learning. This article describes the installation and effectiveness of a strategy to encourage student class preparation. The strategy, called the Class-to-Class Method, has been implemented in a large private university in Chile. The university hopes that this innovation will reduce…

The Babushka Concept--An Instructional Sequence to Enhance Laboratory Learning in Science Education

ERIC Educational Resources Information Center

Gårdebjer, Sofie; Larsson, Anette; Adawi, Tom

2017-01-01

This paper deals with a novel method for improving the traditional "verification" laboratory in science education. Drawing on the idea of integrated instructional units, we describe an instructional sequence which we call the Babushka concept. This concept consists of three integrated instructional units: a start-up lecture, a laboratory…

The Evolution of Student Engagement: Writing Improves Teaching in Introductory Biology Courses

ERIC Educational Resources Information Center

Camfield, Eileen Kogl; Land, Kirkwood M.

2017-01-01

In response to calls for pedagogical reforms in undergraduate biology courses to decrease student attrition rates and increase active learning, this article describes one faculty member's conversion from traditional teaching methods to more engaging forms of practice. Partially told as a narrative, this article illustrates a.) the way many faculty…

Documenting Evidence of Practice: The Power of Formative Assessment

ERIC Educational Resources Information Center

Stefl-Mabry, Joette

2018-01-01

The field of school librarianship has long called for stronger evidence related to school libraries and student achievement (Stefl-Mabry and Raddick 2017; Stefl-Mabry et al. 2016; Morris and Cahill 2017). This article outlines a systematic method for school librarians to document student learning and provide tangible confirmation of their…

Copying Helps Novice Learners Build Orthographic Knowledge: Methods for Teaching Devanagari Akshara

ERIC Educational Resources Information Center

Bhide, Adeetee

2018-01-01

Hindi graphs, called akshara, are difficult to learn because of their visual complexity and large set of graphs. Akshara containing multiple consonants (complex akshara) are particularly difficult. In Hindi, complex akshara are formed by fusing individual consonantal graphs. Some complex akshara look similar to their component parts (transparent),…

The Assayer's Scale: Was Intelligence the Ultimate Currency of the Information Age?

ERIC Educational Resources Information Center

Fuller, Renee

1992-01-01

This article considers the role of the basic cognitive unit, called the "story engram," in young children's learning to read, including children ranging in ability from severe mental retardation to giftedness. It illustrates how the "Ball-Stick-Bird" method of beginning reading can facilitate this process because of the…

Portfolio of Evidence: An Assessment Tool in Promoting Geometry Achievement among Teacher Education College Students

ERIC Educational Resources Information Center

Weldeana, Hailu Nigus; Sbhatu, Desta Berhe

2017-01-01

Background: This article reports contributions of an assessment tool called Portfolio of Evidence (PE) in learning college geometry. Material and methods: Two classes of second-year students from one Ethiopian teacher education college, assigned into Treatment and Comparison classes, were participated. The assessment tools used in the Treatment…

Quality in E-Learning: A Framework for Promoting and Assuring Quality in Virtual Institutions

ERIC Educational Resources Information Center

Masoumi, D.; Lindstrom, B.

2012-01-01

With the growing demand for e-learning along with striving for excellence associated with globalization, there are worldwide calls for enhancing and assuring quality in e-learning, specifically in the context of the developing countries. Such calls for quality enhancement, accountability, added value, value for money, self-evaluation, and role…

Advancing Civic Learning and Engagement in Democracy: A Road Map and Call to Action

ERIC Educational Resources Information Center

US Department of Education, 2012

2012-01-01

Today, the U.S. Department of Education joins the National Task Force on Civic Learning and Democratic Engagement, the American Commonwealth Partnership, and the Campaign for the Civic Mission of Schools in a new national call to action to infuse and enhance civic learning and democratic engagement for all students throughout the American…

The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

ERIC Educational Resources Information Center

Blitz, Mark H.; Modeste, Marsha

2015-01-01

The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

Use of media for recruiting clinical research volunteers in Ecuador.

PubMed

Peñaherrera, Carlos Andrés; Palacios, Michael; Duarte, María Carolina; Santibáñez, Rocío; Tamariz, Leonardo; Palacio, Ana

2015-12-10

Up to this date, there are no reports made about the use of media for recruiting research volunteers in Latin American populations. Given the emergence of clinical research in Ecuador, a study of this kind in the local population will be beneficial for future research, and is probably applicable to other countries in the region. Two public calls were made for a cross-sectional study on cognitive function and diabetes. We only included people between 55 and 65 years of age without previous neurocognitive conditions. We invited individuals through interviews on the radio, television broadcasts and local newspapers, along with social media ads. Each individual was asked about the method by which they learned of the project. We calculated the frequency in which each method was reported and a chi-square test was used to assess gender differences in the results. A total of 274 patients were enrolled in the study, 64.2% are women and 35.8% men. We found that 29.93% learned of it from third persons, 20.8% through radio, 8.76% through social media, 8.39% by newspaper, and 5.11% by television, while a remaining 27.01% had not previously heard of the recruitment call. Methods reported varied significantly between men and women (p = 0.03). Traditional media were the most common method of recruitment, with radio interviews being the most frequently reported. Individually, none of them surpassed the frequency of people learning of the project from other people (snowball effect). Social networks play an important role, exceeding certain traditional media. We have described for the first time in Latin America the use of media as methods to recruit volunteers for research, and the importance of project dissemination by the participants to reach more people.

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

PubMed Central

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Relationship between learning styles and interpersonal communication skills of nursing student in Medical Sciences Tehran University in 2012

PubMed Central

Azari, S; Mokhtari, S; Mousavi, H; Mohammadi, M; Aliyari, A; Salimi, M; Azari, GH

2015-01-01

Introduction: interpersonal communication skills are required for training and represent one of the most significant parts concerning the character of student learning. In another idea, learning is a constant method and learners favor a position of knowledge forms according to their character and individual practices. Evaluate the correlation between the learning methods and interpersonal conversation abilities of the nursing undergraduate in Medical Sciences Tehran University in 2012 was the purpose of this research. Methods: In this regular detailed cross-sectional analysis, 361 students from the School of Nursing and Midwifery were chosen during a census method. The information collection instruments were regulated, giving a questionnaire called Interpersonal Communication Skills Standards exam and VARK Learning Styles questionnaire. Data was examined by SPSS application (18th edition) by using Mann-Whitney and Kruskal-Wallis test. Results: 320 questionnaires were finished. 60.6% of the members were females. The average number of the students’ conversation abilities level was 101.91 ± 10.35. More than half of the samples (58.8%) preferred multi-modal learning styles (Bi-Tri and Quad Modals) and 41.2% of the students preferred single modal learning styles. There were no significant differences between the Interpersonal Communication Skills and the learning styles (P= 0.46). Conclusion: According to no significant relationship between the communication skills of students with learning style and Demographic changeable and Lack of proper form of communication skills, we were ready to build different systems and courses related to improving the skills’ level. PMID:28316687

Advanced Steel Microstructural Classification by Deep Learning Methods.

PubMed

Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

2018-02-01

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Beyond adaptive-critic creative learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.

Learning coefficient of generalization error in Bayesian estimation and vandermonde matrix-type singularity.

PubMed

Aoyagi, Miki; Nagata, Kenji

2012-06-01

The term algebraic statistics arises from the study of probabilistic models and techniques for statistical inference using methods from algebra and geometry (Sturmfels, 2009 ). The purpose of our study is to consider the generalization error and stochastic complexity in learning theory by using the log-canonical threshold in algebraic geometry. Such thresholds correspond to the main term of the generalization error in Bayesian estimation, which is called a learning coefficient (Watanabe, 2001a , 2001b ). The learning coefficient serves to measure the learning efficiencies in hierarchical learning models. In this letter, we consider learning coefficients for Vandermonde matrix-type singularities, by using a new approach: focusing on the generators of the ideal, which defines singularities. We give tight new bound values of learning coefficients for the Vandermonde matrix-type singularities and the explicit values with certain conditions. By applying our results, we can show the learning coefficients of three-layered neural networks and normal mixture models.

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Non-Traditional Learners in Higher Education: Comparison of a Traditional MCQ Examination with Concept Mapping to Assess Learning in a Dental Radiological Science Course

ERIC Educational Resources Information Center

Hay, David B.; Tan, Po Li; Whaites, Eric

2010-01-01

The aim of this study is to argue for alternative assessment methods (i.e. concept map) considering the changes in demography in higher education. In the case of school of dentistry, for example, there is an urgent call for a catalyst for new assessment methods in dental education in view of the drive to comprehensively assess professional…

Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

PubMed

Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

2015-05-01

This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

NASA Astrophysics Data System (ADS)

Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu

2018-03-01

Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.

Authentic Oral Language Production and Interaction in CALL: An Evolving Conceptual Framework for the Use of Learning Analytics within the SpeakApps Project

ERIC Educational Resources Information Center

Nic Giolla Mhichíl, Mairéad; van Engen, Jeroen; Ó Ciardúbháin, Colm; Ó Cléircín, Gearóid; Appel, Christine

2014-01-01

This paper sets out to construct and present the evolving conceptual framework of the SpeakApps projects to consider the application of learning analytics to facilitate synchronous and asynchronous oral language skills within this CALL context. Drawing from both the CALL and wider theoretical and empirical literature of learner analytics, the…

Convergence of calls as animals form social bonds, active compensation for noisy communication channels, and the evolution of vocal learning in mammals.

PubMed

Tyack, Peter L

2008-08-01

The classic evidence for vocal production learning involves imitation of novel, often anthropogenic sounds. Among mammals, this has been reported for dolphins, elephants, harbor seals, and humans. A broader taxonomic distribution has been reported for vocal convergence, where the acoustic properties of calls from different individuals converge when they are housed together in captivity or form social bonds in the wild. Vocal convergence has been demonstrated for animals as diverse as songbirds, parakeets, hummingbirds, bats, elephants, cetaceans, and primates. For most species, call convergence is thought to reflect a group-distinctive identifier, with shared calls reflecting and strengthening social bonds. A ubiquitous function for vocal production learning that is starting to receive attention involves modifying signals to improve communication in a noisy channel. Pooling data on vocal imitation, vocal convergence, and compensation for noise suggests a wider taxonomic distribution of vocal production learning among mammals than has been generally appreciated. The wide taxonomic distribution of this evidence for vocal production learning suggests that perhaps more of the neural underpinnings for vocal production learning are in place in mammals than is usually recognized. (c) 2008 APA, all rights reserved

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

PubMed

Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

2014-03-01

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Make the call, don't miss a beat: Heart Attack Information for Women

MedlinePlus

... Other resources Learn more about heart disease and heart attacks. Make the Call, Don't Miss a Beat ... symptoms Learn the 7 most common signs of heart attack in men and women. Chest pain or discomfort " ...

Developing computer-based training programs for basic mammalian histology: Didactic versus discovery-based design

NASA Astrophysics Data System (ADS)

Fabian, Henry Joel

Educators have long tried to understand what stimulates students to learn. The Swiss psychologist and zoologist, Jean Claude Piaget, suggested that students are stimulated to learn when they attempt to resolve confusion. He reasoned that students try to explain the world with the knowledge they have acquired in life. When they find their own explanations to be inadequate to explain phenomena, students find themselves in a temporary state of confusion. This prompts students to seek more plausible explanations. At this point, students are primed for learning (Piaget 1964). The Piagetian approach described above is called learning by discovery. To promote discovery learning, a teacher must first allow the student to recognize his misconception and then provide a plausible explanation to replace that misconception (Chinn and Brewer 1993). One application of this method is found in the various learning cycles, which have been demonstrated to be effective means for teaching science (Renner and Lawson 1973, Lawson 1986, Marek and Methven 1991, and Glasson & Lalik 1993). In contrast to the learning cycle, tutorial computer programs are generally not designed to correct student misconceptions, but rather follow a passive, didactic method of teaching. In the didactic or expositional method, the student is told about a phenomenon, but is neither encouraged to explore it, nor explain it in his own terms (Schneider and Renner 1980).

Bare-Bones Teaching-Learning-Based Optimization

PubMed Central

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. PMID:25013844

Bare-bones teaching-learning-based optimization.

PubMed

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Evaluation and assessment of the online postgraduate critical care nursing course.

PubMed

Patel, Reena

2007-01-01

During challenging times facing the health service, strategies for sustaining further education for nurses in highly specialised areas call for alternate means for learning. Nurses, who were accustomed to traditional methods of learning and had no formal computer training as part of their curriculum, are now being confronted with new methods of learning. Evaluation of the effectiveness of a newly developed postgraduate critical care course delivered online for nurses was examined. A pre test and post test of 16 participants were conducted. Participants found coursework intellectually stimulating and their preference to learn from websites demonstrated the effect size (tau-b=.677) to be significant at the 0.01 level. The pre and post test results from the knowledge assessment tool indicated an advancement of mean test scores and at a significant difference value of p=.055. Ninety four percent of the participants agreed that they were able to integrate their learning from the coursework towards their clinical practice. Improvement in nurses critical care knowledge impacts positively on delivery of safe and effective health care.

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.

Problem-Based Composition: The Practical Side

ERIC Educational Resources Information Center

Beckelhimer, Lisa; Hundemer, Ronald; Sharp, Judith; Zipfel, William

2007-01-01

For several years a number of instructors at the University of Cincinnati have experimented with the concept of problem-based learning (PBL) in their composition courses. The concept, rooted as it is in Socratic method and the hands-on problem-solving advocated by John Dewey, is not new, and though some of its applications may call for adjustments…

Visual Thinking Routines: A Mixed Methods Approach Applied to Student Teachers at the American University in Dubai

ERIC Educational Resources Information Center

Gholam, Alain

2017-01-01

Visual thinking routines are principles based on several theories, approaches, and strategies. Such routines promote thinking skills, call for collaboration and sharing of ideas, and above all, make thinking and learning visible. Visual thinking routines were implemented in the teaching methodology graduate course at the American University in…

The NAEP Guide: A Description of the Content and Methods of the 1997 and 1998 Assessments. Revised Edition.

ERIC Educational Resources Information Center

Calderone, John, Ed.; King, Laura Mitchell, Ed.; Horkay, Nancy, Ed.

The National Assessment of Educational Progress (NAEP), often called the "Nation's Report Card," is the only nationally representative, continuing assessment of what U.S. students know and can do in various subject areas. NAEP provides a comprehensive measure of students' learning at critical junctures in their school experience. The…

Second Language Acquisition: Implications of Web 2.0 and Beyond

ERIC Educational Resources Information Center

Chang, Ching-Wen; Pearman, Cathy; Farha, Nicholas

2012-01-01

Language laboratories, developed in the 1970s under the influence of the Audiolingual Method, were superseded several decades later by computer-assisted language learning (CALL) work stations (Gündüz, 2005). The World Wide Web was developed shortly thereafter. From this introduction and the well-documented and staggering growth of the Internet and…

Prioritizing preferable locations for increasing urban tree canopy in New York City

Treesearch

Dexter Locke; J. Morgan Grove; Jacqueline W.T. Lu; Austin Troy; Jarlath P.M. O' Neil-Dunne; Brian Beck

2010-01-01

This paper presents a set of Geographic Information System (GIS) methods for identifying and prioritizing tree planting sites in urban environments. It uses an analytical approach created by a University of Vermont service-learning class called "GIS Analysis of New York City's Ecology" that was designed to provide research support to the MillionTreesNYC...

A Mixed-Methods Evaluation of Social Work Learning Outcomes in Interprofessional Training with Medicine and Pharmacy Students

ERIC Educational Resources Information Center

Wharton, Tracy; Burg, Mary Ann

2017-01-01

Social work has moved firmly into a need for partnership training models, as our newest Educational Policy and Accreditation Standards explicitly call for interprofessional education (IPE). Although IPE is not a new model, we have not been consistently involved in training partnerships. Three professional schools formed partnerships to provide IPE…

Perceived Difficulty of a Motor-Skill Task as a Function of Training.

ERIC Educational Resources Information Center

Bratfisch, Oswald; And Others

A simple device called a "wire labyrinth" was used in an experiment involving learning of a two-hand motor task. The Ss were asked, after completing each of 7 successive trails, to give their estimates of perceived (subjective) difficulty of the task. For this purpose, the psychophysical method of magnitude estimation was used. Time was…

An STS Approach to Organizing a Secondary Science Methods Course: Preliminary Findings.

ERIC Educational Resources Information Center

Dass, Pradeep M.

The current agenda in science education calls for science instruction that enhances student understanding of the nature of scientific enterprise, enables students to critically analyze scientific information as well as apply it in real-life situations, and sets them on a path of lifelong learning in science. In order to prepare teachers who can…

Ballet as Somatic Practice: A Case Study Exploring the Integration of Somatic Practices in Ballet Pedagogy

ERIC Educational Resources Information Center

Berg, Tanya

2017-01-01

This case study explores one teacher's integration of Alexander Technique and the work of neuromuscular retrainer Irene Dowd in ballet pedagogy to establish a somatic approach to teaching, learning, and performing ballet technique. This case study highlights the teacher's unique teaching method called IMAGE TECH for dancers (ITD) and offers…

Scenario Planning: A Phenomenological Examination of Influence on Organizational Learning and Decision-Making in a K-12 Public Education System

ERIC Educational Resources Information Center

Deklotz, Patricia F.

2013-01-01

Organizations commonly engage in long range planning to direct decisions. Scenario planning, one method of private sector planning, is recognized as useful when organizations are facing uncertainty. Scenario planning engages the organization in a process that produces plausible stories, called scenarios, describing the organization in several…

The Use of Probeware to Improve Learning Outcomes in Middle School Science: A Mixed Methods Case Study

ERIC Educational Resources Information Center

Price, Elizabeth Lamond

2017-01-01

The Next Generation Science Standards (NGSS) call upon K-12 science teachers to provide authentic science and engineering practices which deepen understanding of core ideas and crosscutting concepts (NGSS Lead States, 2013). Probeware technology provides exposure to these scientific practices; however, there is a disconnect between the frequency…

Forum: The Lecture and Student Learning. The Lost Art of Lecturing: Cultivating Student Listening and Notetaking

ERIC Educational Resources Information Center

Meyer, Kevin R.; Hunt, Stephen K.

2017-01-01

As this forum's call for papers notes, lecture represents one of the more "controversial forms of instructional communication," yet remains a predominant instructional method in academia. Ironically, instructors face increasing pressure to abandon lecture at a time when these classes are popular and students readily enroll in lecture…

Enhancing Practice through Clinically Rich Methods Courses in Physical Education: Perceptions of Preservice Teachers and Their Cooperating Teachers

ERIC Educational Resources Information Center

Flory, Sara Barnard; Burns, Rebecca West

2017-01-01

Similar to other teacher education disciplines, Physical Education Teacher Education (PETE) must adjust to calls for clinically rich teacher preparation because knowledge learned in PETE does not easily transfer to cultures of schools, classrooms, and gymnasia. Opportunity exists to understand more about clinically rich PETE courses, particularly…

Semi-Supervised Tripled Dictionary Learning for Standard-dose PET Image Prediction using Low-dose PET and Multimodal MRI

PubMed Central

Wang, Yan; Ma, Guangkai; An, Le; Shi, Feng; Zhang, Pei; Lalush, David S.; Wu, Xi; Pu, Yifei; Zhou, Jiliu; Shen, Dinggang

2017-01-01

Objective To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semi-supervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion This work proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients. PMID:27187939

Addition of fraction in swimming context

NASA Astrophysics Data System (ADS)

Putri, R. I. I.; Gunawan, M. S.; Zulkardi

2017-12-01

This study aimed to produce learning trajectory that can help students in learning fractions by using swimming context. The study involved 37 fourth grade students with different capabilities in Elementary School IBA, South Sumatra, Indonesia. This study used an instructional theory called Indonesian version of Realistic Mathematics Education (PMRI). This research used design research method with three stages: preliminary design, the design experiment, and retrospective analysis. Several techniques used for collecting data including a video recording of students interaction in the group discussion, students’ work, and interviewing the students. To conclude, the swimming context could stimulate students’ informal knowledge about the meaning of fractions in which it can be used in the additional learning either the same denominator or different denominator.

"I write to know what I think": a four-year writing curriculum.

PubMed

Lister, Elena; Kravis, Nathan; Sandberg, Larry; Halpern, Jeffrey K; Cabaniss, Deborah L; Singer, Meriamne B

2008-12-01

The four-year writing curriculum of the Columbia Center for Psychoanalytic Training and Research has as its main objective to teach candidates to learn about analysis through writing. Learning to write about analyses ultimately entails learning to clarify and then express how one thinks and functions as an analyst. Since its inception ten years ago, the program has evolved into its current structure, a stepwise approach through the years of candidate training based on a didactic method called "layering." For each level of the course, candidates' typical writing difficulties are examined, and examples given of write-ups and how they were used in teaching. The essential role of the faculty experience is also described.

Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

NASA Astrophysics Data System (ADS)

Kyo, Koki

Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

Incremental Learning of Context Free Grammars by Parsing-Based Rule Generation and Rule Set Search

NASA Astrophysics Data System (ADS)

Nakamura, Katsuhiko; Hoshina, Akemi

This paper discusses recent improvements and extensions in Synapse system for inductive inference of context free grammars (CFGs) from sample strings. Synapse uses incremental learning, rule generation based on bottom-up parsing, and the search for rule sets. The form of production rules in the previous system is extended from Revised Chomsky Normal Form A→βγ to Extended Chomsky Normal Form, which also includes A→B, where each of β and γ is either a terminal or nonterminal symbol. From the result of bottom-up parsing, a rule generation mechanism synthesizes minimum production rules required for parsing positive samples. Instead of inductive CYK algorithm in the previous version of Synapse, the improved version uses a novel rule generation method, called ``bridging,'' which bridges the lacked part of the derivation tree for the positive string. The improved version also employs a novel search strategy, called serial search in addition to minimum rule set search. The synthesis of grammars by the serial search is faster than the minimum set search in most cases. On the other hand, the size of the generated CFGs is generally larger than that by the minimum set search, and the system can find no appropriate grammar for some CFL by the serial search. The paper shows experimental results of incremental learning of several fundamental CFGs and compares the methods of rule generation and search strategies.

Investigating the Effectiveness of Computer-Assisted Language Learning (CALL) Using Google Documents in Enhancing Writing--A Study on Senior 1 Students in a Chinese Independent High School

ERIC Educational Resources Information Center

Ambrose, Regina Maria; Palpanathan, Shanthini

2017-01-01

Computer-assisted language learning (CALL) has evolved through various stages in both technology as well as the pedagogical use of technology (Warschauer & Healey, 1998). Studies show that the CALL trend has facilitated students in their English language writing with useful tools such as computer based activities and word processing. Students…

At the Crossroads of Learning and Culture: Identifying a Construct for Effective Computer-Assisted Language Learning for English Language Learners

ERIC Educational Resources Information Center

Shaw, Yun

2010-01-01

Many of the commercial Computer-Assisted Language Learning (CALL) programs available today typically take a generic approach. This approach standardizes the program so that it can be used to teach any language merely by translating the content from one language to another. These CALL programs rarely consider the cultural background or preferred…

An Assessment for Learning System Called ACED: Designing for Learning Effectiveness and Accessibility. Research Report. ETS RR-07-26

ERIC Educational Resources Information Center

Shute, Valerie J.; Hansen, Eric G.; Almond, Russell G.

2007-01-01

This paper reports on a 3-year, NSF-funded research and development project called ACED: Adaptive Content with Evidence-based Diagnosis. The purpose of the project was to design, develop, and evaluate an assessment for learning (AfL) system for diverse students, using Algebra I content related to geometric sequences (i.e., successive numbers…

QuickAssist: Reading and Learning Vocabulary Independently with the Help of CALL and NLP Technologies

ERIC Educational Resources Information Center

Wood, Peter

2011-01-01

Independent learning is a buzz word that is often used in connection with computer technologies applied to the area of foreign language instruction. This chapter takes a critical look at some of the stereotypes that exist with regard to computer-assisted language learning (CALL) as a money saver and an easy way to create an "independent"…

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Online gaming for learning optimal team strategies in real time

NASA Astrophysics Data System (ADS)

Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

2010-04-01

This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

Selecting a restoration technique to minimize OCR error.

PubMed

Cannon, M; Fugate, M; Hush, D R; Scovel, C

2003-01-01

This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.

BCR CDR3 length distributions differ between blood and spleen and between old and young patients, and TCR distributions can be used to detect myelodysplastic syndrome

NASA Astrophysics Data System (ADS)

Pickman, Yishai; Dunn-Walters, Deborah; Mehr, Ramit

2013-10-01

Complementarity-determining region 3 (CDR3) is the most hyper-variable region in B cell receptor (BCR) and T cell receptor (TCR) genes, and the most critical structure in antigen recognition and thereby in determining the fates of developing and responding lymphocytes. There are millions of different TCR Vβ chain or BCR heavy chain CDR3 sequences in human blood. Even now, when high-throughput sequencing becomes widely used, CDR3 length distributions (also called spectratypes) are still a much quicker and cheaper method of assessing repertoire diversity. However, distribution complexity and the large amount of information per sample (e.g. 32 distributions of the TCRα chain, and 24 of TCRβ) calls for the use of machine learning tools for full exploration. We have examined the ability of supervised machine learning, which uses computational models to find hidden patterns in predefined biological groups, to analyze CDR3 length distributions from various sources, and distinguish between experimental groups. We found that (a) splenic BCR CDR3 length distributions are characterized by low standard deviations and few local maxima, compared to peripheral blood distributions; (b) healthy elderly people's BCR CDR3 length distributions can be distinguished from those of the young; and (c) a machine learning model based on TCR CDR3 distribution features can detect myelodysplastic syndrome with approximately 93% accuracy. Overall, we demonstrate that using supervised machine learning methods can contribute to our understanding of lymphocyte repertoire diversity.

Margined winner-take-all: New learning rule for pattern recognition.

PubMed

Fukushima, Kunihiko

2018-01-01

The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Problem-based learning on quantitative analytical chemistry course

NASA Astrophysics Data System (ADS)

Fitri, Noor

2017-12-01

This research applies problem-based learning method on chemical quantitative analytical chemistry, so called as "Analytical Chemistry II" course, especially related to essential oil analysis. The learning outcomes of this course include aspects of understanding of lectures, the skills of applying course materials, and the ability to identify, formulate and solve chemical analysis problems. The role of study groups is quite important in improving students' learning ability and in completing independent tasks and group tasks. Thus, students are not only aware of the basic concepts of Analytical Chemistry II, but also able to understand and apply analytical concepts that have been studied to solve given analytical chemistry problems, and have the attitude and ability to work together to solve the problems. Based on the learning outcome, it can be concluded that the problem-based learning method in Analytical Chemistry II course has been proven to improve students' knowledge, skill, ability and attitude. Students are not only skilled at solving problems in analytical chemistry especially in essential oil analysis in accordance with local genius of Chemistry Department, Universitas Islam Indonesia, but also have skilled work with computer program and able to understand material and problem in English.

Lessons learned by (from?) an economist working in medical decision making.

PubMed

Wakker, Peter P

2008-01-01

This article is a personal account of the author's experiences as an economist working in medical decision making. He discusses the differences between economic decision theory and medical decision making and gives examples of the mutual benefits resulting from interactions. In particular, he discusses the pros and cons of different methods for measuring quality of life (or, as economists would call it, utility), including the standard gamble, the time tradeoff, and the healthy-years equivalent methods.

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

PubMed Central

Gao, Junfeng; Wang, Zhao; Yang, Yong; Zhang, Wenjia; Tao, Chunyi; Guan, Jinan; Rao, Nini

2013-01-01

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time. PMID:23755136

Teaching communication skills: using action methods to enhance role-play in problem-based learning.

PubMed

Baile, Walter F; Blatner, Adam

2014-08-01

Role-play is a method of simulation used commonly to teach communication skills. Role-play methods can be enhanced by techniques that are not widely used in medical teaching, including warm-ups, role-creation, doubling, and role reversal. The purposes of these techniques are to prepare learners to take on the role of others in a role-play; to develop an insight into unspoken attitudes, thoughts, and feelings, which often determine the behavior of others; and to enhance communication skills through the participation of learners in enactments of communication challenges generated by them. In this article, we describe a hypothetical teaching session in which an instructor applies each of these techniques in teaching medical students how to break bad news using a method called SPIKES [Setting, Perception, Invitation, Knowledge, Emotions, Strategy, and Summary]. We illustrate how these techniques track contemporary adult learning theory through a learner-centered, case-based, experiential approach to selecting challenging scenarios in giving bad news, by attending to underlying emotion and by using reflection to anchor new learning.

Self-Taught Low-Rank Coding for Visual Learning.

PubMed

Li, Sheng; Li, Kang; Fu, Yun

2018-03-01

The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choice of auxiliary data. It has shown promising performance in visual learning. However, existing self-taught learning methods usually ignore the structure information in data. In this paper, we focus on building a self-taught coding framework, which can effectively utilize the rich low-level pattern information abstracted from the auxiliary domain, in order to characterize the high-level structural information in the target domain. By leveraging a high quality dictionary learned across auxiliary and target domains, the proposed approach learns expressive codings for the samples in the target domain. Since many types of visual data have been proven to contain subspace structures, a low-rank constraint is introduced into the coding objective to better characterize the structure of the given target set. The proposed representation learning framework is called self-taught low-rank (S-Low) coding, which can be formulated as a nonconvex rank-minimization and dictionary learning problem. We devise an efficient majorization-minimization augmented Lagrange multiplier algorithm to solve it. Based on the proposed S-Low coding mechanism, both unsupervised and supervised visual learning algorithms are derived. Extensive experiments on five benchmark data sets demonstrate the effectiveness of our approach.

Developing an effective corrective action process : lessons learned from operating a confidential close call reporting system

DOT National Transportation Integrated Search

2013-03-05

In 2007, the Federal Railroad Administration (FRA) launched : C3RS, the Confidential Close Call Reporting System, as a : demonstration project to learn how to facilitate the effective : reporting and implementation of corrective actions, and assess t...

Video based object representation and classification using multiple covariance matrices.

PubMed

Zhang, Yurong; Liu, Quan

2017-01-01

Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

Bat detective-Deep learning tools for bat acoustic signal detection.

PubMed

Mac Aodha, Oisin; Gibb, Rory; Barlow, Kate E; Browning, Ella; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R; Newson, Stuart E; Pandourski, Ivan; Parsons, Stuart; Russ, Jon; Szodoray-Paradi, Abigel; Szodoray-Paradi, Farkas; Tilova, Elena; Girolami, Mark; Brostow, Gabriel; Jones, Kate E

2018-03-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.

Bat detective—Deep learning tools for bat acoustic signal detection

PubMed Central

Barlow, Kate E.; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R.; Newson, Stuart E.; Pandourski, Ivan; Russ, Jon; Szodoray-Paradi, Abigel; Tilova, Elena; Girolami, Mark; Jones, Kate E.

2018-01-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio. PMID:29518076

Wavefront cellular learning automata.

PubMed

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

Wavefront cellular learning automata

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

A Can of Coke Leads to a Piece of Pi: A Professional Development Exercise for Educators Is an Adaptable Math Lesson for Many Grades

ERIC Educational Resources Information Center

Burns, Marilyn

2004-01-01

Teaching teachers to become observers and inquirers into mathematics will help change how they teach math to students. Essential to all professional development in mathematics is the idea that making sense of mathematics is key to learning. Just as learning to read calls for bringing meaning to the printed page, learning math calls for bringing…

Criteria for Evaluating a Game-Based CALL Platform

ERIC Educational Resources Information Center

Ní Chiaráin, Neasa; Ní Chasaide, Ailbhe

2017-01-01

Game-based Computer-Assisted Language Learning (CALL) is an area that currently warrants attention, as task-based, interactive, multimodal games increasingly show promise for language learning. This area is inherently multidisciplinary--theories from second language acquisition, games, and psychology must be explored and relevant concepts from…

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

PubMed

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

Learning object correspondences with the observed transport shape measure.

PubMed

Pitiot, Alain; Delingette, Hervé; Toga, Arthur W; Thompson, Paul M

2003-07-01

We propose a learning method which introduces explicit knowledge to the object correspondence problem. Our approach uses an a priori learning set to compute a dense correspondence field between two objects, where the characteristics of the field bear close resemblance to those in the learning set. We introduce a new local shape measure we call the "observed transport measure", whose properties make it particularly amenable to the matching problem. From the values of our measure obtained at every point of the objects to be matched, we compute a distance matrix which embeds the correspondence problem in a highly expressive and redundant construct and facilitates its manipulation. We present two learning strategies that rely on the distance matrix and discuss their applications to the matching of a variety of 1-D, 2-D and 3-D objects, including the corpus callosum and ventricular surfaces.

Overtaking method based on sand-sifter mechanism: Why do optimistic value functions find optimal solutions in multi-armed bandit problems?

PubMed

Ochi, Kento; Kamiura, Moto

2015-09-01

A multi-armed bandit problem is a search problem on which a learning agent must select the optimal arm among multiple slot machines generating random rewards. UCB algorithm is one of the most popular methods to solve multi-armed bandit problems. It achieves logarithmic regret performance by coordinating balance between exploration and exploitation. Since UCB algorithms, researchers have empirically known that optimistic value functions exhibit good performance in multi-armed bandit problems. The terms optimistic or optimism might suggest that the value function is sufficiently larger than the sample mean of rewards. The first definition of UCB algorithm is focused on the optimization of regret, and it is not directly based on the optimism of a value function. We need to think the reason why the optimism derives good performance in multi-armed bandit problems. In the present article, we propose a new method, which is called Overtaking method, to solve multi-armed bandit problems. The value function of the proposed method is defined as an upper bound of a confidence interval with respect to an estimator of expected value of reward: the value function asymptotically approaches to the expected value of reward from the upper bound. If the value function is larger than the expected value under the asymptote, then the learning agent is almost sure to be able to obtain the optimal arm. This structure is called sand-sifter mechanism, which has no regrowth of value function of suboptimal arms. It means that the learning agent can play only the current best arm in each time step. Consequently the proposed method achieves high accuracy rate and low regret and some value functions of it can outperform UCB algorithms. This study suggests the advantage of optimism of agents in uncertain environment by one of the simplest frameworks. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Talking Stories on CD-ROM--How Do They Benefit Their Users?

ERIC Educational Resources Information Center

Donnelly, Michelle

2005-01-01

In recent years, the DfES, 2003 has pushed schools to try and integrate more interactive teaching methods into everyday teaching and learning. This push in technology has lead to an increased call for the use of interactive software to support reading. This study looks at whether talking stories benefit the children who use them. It is a…

Evidence of Two Theoretical Models Observed in Young Children with Disabilities Who Are Beginning to Learn to Write

ERIC Educational Resources Information Center

Staples, Amy; Edmister, Evette

2012-01-01

This study examined the composing process and communication of students aged 5-8 identified with intellectual disabilities. An open-ended writing activity called Big Paper was implemented at least once every 2 weeks for a 6-month period. Qualitative methods were utilized to analyze writing samples, videotapes of writing sessions, and transcripts…

Statistical, Graphical, and Learning Methods for Sensing, Surveillance, and Navigation Systems

DTIC Science & Technology

2016-06-28

harsh propagation environments. Conventional filtering techniques fail to provide satisfactory performance in many important nonlinear or non...Gaussian scenarios. In addition, there is a lack of a unified methodology for the design and analysis of different filtering techniques. To address...these problems, we have proposed a new filtering methodology called belief condensation (BC) DISTRIBUTION A: Distribution approved for public release

Music and Physical Play: What Can We Learn from Early Childhood Teachers in Kenya?

ERIC Educational Resources Information Center

Freshwater, Amy; Sherwood, Elizabeth; Mbugua, Esther

2008-01-01

Sharing classroom practices across international borders can add new dimensions to teaching methods, no matter where one calls home. With this idea in mind, the authors (two U.S. early childhood teacher educators and a Kenyan-born U.S. early childhood teacher) have corresponded for several years through e-mail with a small group of early childhood…

How Much Professional Development Is Enough? Meeting the Needs of Independent Music Teachers Learning to Use a Digital Tool

ERIC Educational Resources Information Center

Upitis, Rena; Brook, Julia

2017-01-01

Even though there are demonstrated benefits of using online tools to support student musicians, there is a persistent challenge of providing sufficient and effective professional development for independent music teachers to use such tools successfully. This paper describes several methods for helping teachers use an online tool called iSCORE,…

My Entirely Plausible Fantasy: Early Mathematics Education in the Age of the Touchscreen Computer

ERIC Educational Resources Information Center

Ginsburg, Herbert P.

2014-01-01

This paper offers an account of what early mathematics education could look like in an age of young digital natives. Each "Tubby," as the tablets are called, presents Nicole (our generic little child) with stimulating mathematics microworlds, from which, beginning at age 3, she can learn basic math concepts, as well as methods of…

Impact of Cold-Calling on Student Voluntary Participation

ERIC Educational Resources Information Center

Dallimore, Elise J.; Hertenstein, Julie H.; Platt, Marjorie B.

2013-01-01

Classroom discussion is perhaps the most frequently used "active learning" strategy. However, instructors are often concerned about students who are less inclined to participate voluntarily. They worry that students not involved in the discussion might have lower quality learning experiences. Although instructors might consider whether to call on…

Rethinking Transfer: Learning from CALL Teacher Education as Consequential Transition

ERIC Educational Resources Information Center

Chao, Chin-chi

2015-01-01

Behind CALL teacher education (CTE) there is an unproblematized consensus of transfer, which suggests a positivist and tool-centered view of learning gains that differs from the sociocultural focus of recent teacher education research. Drawing on Beach's (2003) conceptualization of transfer as "consequential transition," this qualitative…

Integration of Computers into an EFL Reading Classroom

ERIC Educational Resources Information Center

Lim, Kang-Mi; Shen, Hui Zhong

2006-01-01

This study examined the impact of Computer Assisted Language Learning (CALL) on Korean TAFE (Technical and Further Education) college students in an English as a Foreign Language (EFL) reading classroom in terms of their perceptions of learning environment and their reading performance. The study compared CALL and traditional reading classes over…

A New Engine for Schools: The Flexible Scheduling Paradigm

ERIC Educational Resources Information Center

Snyder, Yaakov; Herer, Yale T.; Moore, Michael

2012-01-01

We present a new approach for the organization of schools, which we call the flexible scheduling paradigm (FSP). FSP improves student learning by dynamically redeploying teachers and other pedagogical resources to provide students with customized learning conditions over shorter time periods called "mini-terms" instead of semesters or years. By…

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Max-margin weight learning for medical knowledge network.

PubMed

Jiang, Jingchi; Xie, Jing; Zhao, Chao; Su, Jia; Guan, Yi; Yu, Qiubin

2018-03-01

The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The purpose of this study is to investigate a discriminative weight-learning method based on a medical knowledge network (MKN). We propose a training model called the maximum margin medical knowledge network (M 3 KN), which is strictly derived for calculating the weight of medical knowledge. Using the definition of a reasonable margin, the weight learning can be transformed into a margin optimization problem. To solve the optimization problem, we adopt a sequential minimal optimization (SMO) algorithm and the clique property of a Markov network. Ultimately, M 3 KN not only incorporates the inference ability of PGMs but also deals with high-dimensional logic knowledge. The experimental results indicate that M 3 KN obtains a higher F-measure score than the maximum likelihood learning algorithm of MKN for both Chinese Electronic Medical Records (CEMRs) and Blood Examination Records (BERs). Furthermore, the proposed approach is obviously superior to some classical machine learning algorithms for medical diagnosis. To adequately manifest the importance of domain knowledge, we numerically verify that the diagnostic accuracy of M 3 KN is gradually improved as the number of learned CEMRs increase, which contain important medical knowledge. Our experimental results show that the proposed method performs reliably for learning the weights of medical knowledge. M 3 KN outperforms other existing methods by achieving an F-measure of 0.731 for CEMRs and 0.4538 for BERs. This further illustrates that M 3 KN can facilitate the investigations of intelligent healthcare. Copyright © 2018 Elsevier B.V. All rights reserved.

Online Object Tracking, Learning and Parsing with And-Or Graphs.

PubMed

Wu, Tianfu; Lu, Yang; Zhu, Song-Chun

2017-12-01

This paper presents a method, called AOGTracker, for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM [1] to account for appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of a tracked object, as well as distractors (e.g., similar objects) in background. Three key issues in online inference and learning are addressed: (i) maintaining purity of positive and negative examples collected online, (ii) controling model complexity in latent structure learning, and (iii) identifying critical moments to re-learn the structure of AOG based on its intrackability. The intrackability measures uncertainty of an AOG based on its score maps in a frame. In experiments, our AOGTracker is tested on two popular tracking benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks  , [3] , and the VOT benchmarks [4] -VOT 2013, 2014, 2015 and TIR2015 (thermal imagery tracking). In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network   [5] , [6] . In the latter, our AOGTracker outperforms all other trackers in VOT2013 and is comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.

Active Learning Using Hint Information.

PubMed

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

The Necessity of Machine Learning and Epistemology in the Development of Categorization Theories: A Case Study in Prototype-Exemplar Debate

NASA Astrophysics Data System (ADS)

Gagliardi, Francesco

In the present paper we discuss some aspects of the development of categorization theories concerning cognitive psychology and machine learning. We consider the thirty-year debate between prototype-theory and exemplar-theory in the studies of cognitive psychology regarding the categorization processes. We propose this debate is ill-posed, because it neglects some theoretical and empirical results of machine learning about the bias-variance theorem and the existence of some instance-based classifiers which can embed models subsuming both prototype and exemplar theories. Moreover this debate lies on a epistemological error of pursuing a, so called, experimentum crucis. Then we present how an interdisciplinary approach, based on synthetic method for cognitive modelling, can be useful to progress both the fields of cognitive psychology and machine learning.

Automatic MeSH term assignment and quality assessment.

PubMed Central

Kim, W.; Aronson, A. R.; Wilbur, W. J.

2001-01-01

For computational purposes documents or other objects are most often represented by a collection of individual attributes that may be strings or numbers. Such attributes are often called features and success in solving a given problem can depend critically on the nature of the features selected to represent documents. Feature selection has received considerable attention in the machine learning literature. In the area of document retrieval we refer to feature selection as indexing. Indexing has not traditionally been evaluated by the same methods used in machine learning feature selection. Here we show how indexing quality may be evaluated in a machine learning setting and apply this methodology to results of the Indexing Initiative at the National Library of Medicine. PMID:11825203

Nurse faculty experiences in problem-based learning: an interpretive phenomenologic analysis.

PubMed

Paige, Jane B; Smith, Regina O

2013-01-01

This study explored the nurse faculty experience of participating in a problem-based learning (PBL) faculty development program. Utilizing PBL as a pedagogical method requires a paradigm shift in the way faculty think about teaching, learning, and the teacher-student relationship. An interpretive phenomenological analysis approach was used to explore the faculty experience in a PBL development program. Four themes emerged: change in perception of the teacher-student relationship, struggle in letting go, uncertainty, and valuing PBL as a developmental process. Epistemic doubt happens when action and intent toward the PBL teaching perspective do not match underlying beliefs. Findings from this study call for ongoing administrative support for education on PBL while faculty take time to uncover hidden epistemological beliefs.

A new method for enhancer prediction based on deep belief network.

PubMed

Bu, Hongda; Gan, Yanglan; Wang, Yang; Zhou, Shuigeng; Guan, Jihong

2017-10-16

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area. Here, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction. Deep learning is effective in boosting the performance of enhancer prediction.

Foreign Language Tutoring in Oral Conversations Using Spoken Dialog Systems

NASA Astrophysics Data System (ADS)

Lee, Sungjin; Noh, Hyungjong; Lee, Jonghoon; Lee, Kyusong; Lee, Gary Geunbae

Although there have been enormous investments into English education all around the world, not many differences have been made to change the English instruction style. Considering the shortcomings for the current teaching-learning methodology, we have been investigating advanced computer-assisted language learning (CALL) systems. This paper aims at summarizing a set of POSTECH approaches including theories, technologies, systems, and field studies and providing relevant pointers. On top of the state-of-the-art technologies of spoken dialog system, a variety of adaptations have been applied to overcome some problems caused by numerous errors and variations naturally produced by non-native speakers. Furthermore, a number of methods have been developed for generating educational feedback that help learners develop to be proficient. Integrating these efforts resulted in intelligent educational robots — Mero and Engkey — and virtual 3D language learning games, Pomy. To verify the effects of our approaches on students' communicative abilities, we have conducted a field study at an elementary school in Korea. The results showed that our CALL approaches can be enjoyable and fruitful activities for students. Although the results of this study bring us a step closer to understanding computer-based education, more studies are needed to consolidate the findings.

Classify epithelium-stroma in histopathological images based on deep transferable network.

PubMed

Yu, X; Zheng, H; Liu, C; Huang, Y; Ding, X

2018-04-20

Recently, the deep learning methods have received more attention in histopathological image analysis. However, the traditional deep learning methods assume that training data and test data have the same distributions, which causes certain limitations in real-world histopathological applications. However, it is costly to recollect a large amount of labeled histology data to train a new neural network for each specified image acquisition procedure even for similar tasks. In this paper, an unsupervised domain adaptation is introduced into a typical deep convolutional neural network (CNN) model to mitigate the repeating of the labels. The unsupervised domain adaptation is implemented by adding two regularisation terms, namely the feature-based adaptation and entropy minimisation, to the object function of a widely used CNN model called the AlexNet. Three independent public epithelium-stroma datasets were used to verify the proposed method. The experimental results have demonstrated that in the epithelium-stroma classification, the proposed method can achieve better performance than the commonly used deep learning methods and some existing deep domain adaptation methods. Therefore, the proposed method can be considered as a better option for the real-world applications of histopathological image analysis because there is no requirement for recollection of large-scale labeled data for every specified domain. © 2018 The Authors Journal of Microscopy © 2018 Royal Microscopical Society.

WE-G-BRA-01: Patient Safety and Treatment Quality Improvement Through Incident Learning: Experience of a Non-Academic Proton Therapy Center

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

Zheng, Y; Johnson, R; Zhao, L

2015-06-15

Purpose: Incident learning has been proven to improve patient safety and treatment quality in conventional radiation therapy. However, its application in proton therapy has not been reported yet to our knowledge. In this study, we report our experience in developing and implementation of an in-house incident learning system. Methods: An incident learning system was developed based on published principles and tailored for our clinical practice and available resource about 18 months ago. The system includes four layers of error detection and report: 1) dosimetry peer review; 2) physicist plan quality assurance (QA); 3) treatment delivery issue on call and record;more » and 4) other incident report. The first two layers of QA and report were mandatory for each treatment plan through easy-to-use spreadsheets that are only accessible by the dosimetry and physicist departments. The treatment delivery issues were recorded case by case by the on call physicist. All other incidents were reported through an online incident report system, which can be anonymous. The incident report includes near misses on planning and delivery, process deviation, machine issues, work flow and documentation. Periodic incident reviews were performed. Results: In total, about 116 errors were reported through dosimetry review, 137 errors through plan QA, 83 treatment issues through physics on call record, and 30 through the online incident report. Only 8 incidents (2.2%) were considered to have a clinical impact to patients, and the rest of errors were either detected before reaching patients or had negligible dosimetric impact (<5% dose variance). Personnel training & process improvements were implemented upon periodic incident review. Conclusion: An incident learning system can be helpful in personnel training, error reduction, and patient safety and treatment quality improvement. The system needs to be catered for each clinic’s practice and available resources. Incident and knowledge sharing among proton centers are encouraged.« less

Decomposition-based transfer distance metric learning for image classification.

PubMed

Luo, Yong; Liu, Tongliang; Tao, Dacheng; Xu, Chao

2014-09-01

Distance metric learning (DML) is a critical factor for image analysis and pattern recognition. To learn a robust distance metric for a target task, we need abundant side information (i.e., the similarity/dissimilarity pairwise constraints over the labeled data), which is usually unavailable in practice due to the high labeling cost. This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information). The state-of-the-art metric learning algorithms usually fail in this setting because the data distributions of the source task and target task are often quite different. We address this problem by assuming that the target distance metric lies in the space spanned by the eigenvectors of the source metrics (or other randomly generated bases). The target metric is represented as a combination of the base metrics, which are computed using the decomposed components of the source metrics (or simply a set of random bases); we call the proposed method, decomposition-based transfer DML (DTDML). In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics. The main advantage of the proposed method compared with existing transfer metric learning approaches is that we directly learn the base metric coefficients instead of the target metric. To this end, far fewer variables need to be learned. We therefore obtain more reliable solutions given the limited side information and the optimization tends to be faster. Experiments on the popular handwritten image (digit, letter) classification and challenge natural image annotation tasks demonstrate the effectiveness of the proposed method.

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi-view datasets clearly demonstrate that our proposed co-labeling approach achieves state-of-the-art performance for various multi-view weakly labeled learning problems including multi-view SSL, multi-view MIL and multi-view ROD.

Post-boosting of classification boundary for imbalanced data using geometric mean.

PubMed

Du, Jie; Vong, Chi-Man; Pun, Chi-Man; Wong, Pak-Kin; Ip, Weng-Fai

2017-12-01

In this paper, a novel imbalance learning method for binary classes is proposed, named as Post-Boosting of classification boundary for Imbalanced data (PBI), which can significantly improve the performance of any trained neural networks (NN) classification boundary. The procedure of PBI simply consists of two steps: an (imbalanced) NN learning method is first applied to produce a classification boundary, which is then adjusted by PBI under the geometric mean (G-mean). For data imbalance, the geometric mean of the accuracies of both minority and majority classes is considered, that is statistically more suitable than the common metric accuracy. PBI also has the following advantages over traditional imbalance methods: (i) PBI can significantly improve the classification accuracy on minority class while improving or keeping that on majority class as well; (ii) PBI is suitable for large data even with high imbalance ratio (up to 0.001). For evaluation of (i), a new metric called Majority loss/Minority advance ratio (MMR) is proposed that evaluates the loss ratio of majority class to minority class. Experiments have been conducted for PBI and several imbalance learning methods over benchmark datasets of different sizes, different imbalance ratios, and different dimensionalities. By analyzing the experimental results, PBI is shown to outperform other imbalance learning methods on almost all datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Contextual Learning Issues.

ERIC Educational Resources Information Center

1999

This document contains four symposium papers on contextual learning issues. "Learning to Learn Strategies of Successful Real Estate Professionals: Implications for Learning in the Workplace" (Margot B. Weinstein) describes a multicase study in which a model called the Individual Learning System was used to identify the strategies and…

Multimodal Research: Addressing the Complexity of Multimodal Environments and the Challenges for CALL

ERIC Educational Resources Information Center

Tan, Sabine; O'Halloran, Kay L.; Wignell, Peter

2016-01-01

Multimodality, the study of the interaction of language with other semiotic resources such as images and sound resources, has significant implications for computer assisted language learning (CALL) with regards to understanding the impact of digital environments on language teaching and learning. In this paper, we explore recent manifestations of…

Celebrating the Story of My First Contribution to CALL

ERIC Educational Resources Information Center

Al-Seghayer, Khalid

2016-01-01

In the realm of second language acquisition, investigations of the efficacy of multimedia annotations for learning unknown lexical items has attracted considerable interest during the past decade. This commentary discusses the story of my first contribution to the field of computer-assisted language learning (CALL) 14 years ago. In particular, it…

Using a Value-Added Approach to Assess the Sociology Major

ERIC Educational Resources Information Center

Pedersen, Daphne E.; White, Frank

2011-01-01

Universities across the nation have been called upon to provide evidence of student learning through direct means of assessment. Value-added assessment, which aims to document the development of student learning from the beginning of the university experience to the end, has been called "accountability's new frontier" by the American…

Teaching Business Management to Engineers: The Impact of Interactive Lectures

ERIC Educational Resources Information Center

Rambocas, Meena; Sastry, Musti K. S.

2017-01-01

Some education specialists are challenging the use of traditional strategies in classrooms and are calling for the use of contemporary teaching and learning techniques. In response to these calls, many field experiments that compare different teaching and learning strategies have been conducted. However, to date, little is known on the outcomes of…

Cloud Computing and Validated Learning for Accelerating Innovation in IoT

ERIC Educational Resources Information Center

Suciu, George; Todoran, Gyorgy; Vulpe, Alexandru; Suciu, Victor; Bulca, Cristina; Cheveresan, Romulus

2015-01-01

Innovation in Internet of Things (IoT) requires more than just creation of technology and use of cloud computing or big data platforms. It requires accelerated commercialization or aptly called go-to-market processes. To successfully accelerate, companies need a new type of product development, the so-called validated learning process.…

A Chatbot for a Dialogue-Based Second Language Learning System

ERIC Educational Resources Information Center

Huang, Jin-Xia; Lee, Kyung-Soon; Kwon, Oh-Woog; Kim, Young-Kil

2017-01-01

This paper presents a chatbot for a Dialogue-Based Computer-Assisted second Language Learning (DB-CALL) system. A DB-CALL system normally leads dialogues by asking questions according to given scenarios. User utterances outside the scenarios are normally considered as semantically improper and simply rejected. In this paper, we assume that raising…

Multiple "Curriculum" Meanings Heighten Debate over Standards

ERIC Educational Resources Information Center

Gewertz, Catherine

2011-01-01

Calls for shared curricula for the common standards have triggered renewed debates about who decides what students learn, and even about varied meanings of the word "curriculum," adding layers of complexity to the job of translating the broad learning goals into classroom teaching. The most recent calls for common curricula came from the American…

The Flipped Classroom: Implementing Technology to Aid in College Mathematics Student's Success

ERIC Educational Resources Information Center

Buch, George R.; Warren, Carryn B.

2017-01-01

August 2016 there was a call (Braun, Bremser, Duval, Lockwood & White, 2017) for post-secondary instructors to use active learning in their classrooms. Once such example of active learning is what is called the "flipped" classroom. This paper presents the need for, and the methodology of the flipped classroom, results of…

Integrating CALL into an Iranian EAP Course: Constraints and Affordances

ERIC Educational Resources Information Center

Mehran, Parisa; Alizadeh, Mehrasa

2015-01-01

Iranian universities have recently displayed a growing interest in integrating Computer-Assisted Language Learning (CALL) into teaching/learning English. The English for Academic Purposes (EAP) context, however, is not keeping pace with the current changes since EAP courses are strictly text-based and exam-oriented, and little research has thus…

Future Language Teachers Learning to Become CALL Designers--Methodological Perspectives in Studying Complexity

ERIC Educational Resources Information Center

Keisanen, Tiina; Kuure, Leena

2015-01-01

Language teachers of the future, our current students, live in an increasingly technology-rich world. However, language students do not necessarily see their own digital practices as having relevance for guiding language learning. Research in the fields of CALL and language education more generally indicates that teaching practices change slowly…

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stacked Multilayer Self-Organizing Map for Background Modeling.

PubMed

Zhao, Zhenjie; Zhang, Xuebo; Fang, Yongchun

2015-09-01

In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

Feature Discovery by Competitive Learning.

ERIC Educational Resources Information Center

Rumelhart, David E.; Zipser, David

1985-01-01

Reports results of studies with an unsupervised learning paradigm called competitive learning which is examined using computer simulation and formal analysis. When competitive learning is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. (Author)

Quantum Chemical Topology: Knowledgeable atoms in peptides

NASA Astrophysics Data System (ADS)

Popelier, Paul L. A.

2012-06-01

The need to improve atomistic biomolecular force fields remains acute. Fortunately, the abundance of contemporary computing power enables an overhaul of the architecture of current force fields, which typically base their electrostatics on fixed atomic partial charges. We discuss the principles behind the electrostatics of a more realistic force field under construction, called QCTFF. At the heart of QCTFF lies the so-called topological atom, which is a malleable box, whose shape and electrostatics changes in response to a changing environment. This response is captured by a machine learning method called Kriging. Kriging directly predicts each multipole moment of a given atom (i.e. the output) from the coordinates of the nuclei surrounding this atom (i.e. the input). This procedure yields accurate interatomic electrostatic energies, which form the basis for future-proof progress in force field design.

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions.

PubMed

Phinyomark, Angkoon; Petri, Giovanni; Ibáñez-Marcelo, Esther; Osis, Sean T; Ferber, Reed

2018-01-01

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V's definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called "topological data analysis" and directions for future research are outlined and discussed.

Opinion mining on book review using CNN-L2-SVM algorithm

NASA Astrophysics Data System (ADS)

Rozi, M. F.; Mukhlash, I.; Soetrisno; Kimura, M.

2018-03-01

Review of a product can represent quality of a product itself. An extraction to that review can be used to know sentiment of that opinion. Process to extract useful information of user review is called Opinion Mining. Review extraction model that is enhancing nowadays is Deep Learning model. This Model has been used by many researchers to obtain excellent performance on Natural Language Processing. In this research, one of deep learning model, Convolutional Neural Network (CNN) is used for feature extraction and L2 Support Vector Machine (SVM) as classifier. These methods are implemented to know the sentiment of book review data. The result of this method shows state-of-the art performance in 83.23% for training phase and 64.6% for testing phase.

The influence of the application of personal response systems on the effects of teaching and learning physics at the high school level

NASA Astrophysics Data System (ADS)

Binek, Sławomir; Kimla, Damian; Jarosz, Jerzy

2017-01-01

We report on the effectiveness of using interactive personal response systems in teaching physics in secondary schools. Our research were conducted over the period of 2013-2016 using the system called clickers. The idea is based on a reciprocal interaction allowing one to ask questions and receive immediate responses from all the students simultaneously. Our investigation has confirmed this method to be highly effective and powerful. In particular, students’ ability to acquire knowledge increased with the time spent using clickers. We have successfully applied the system also to entire physics courses. As a result, a positive feedback from students has been observed: not only did they learn more but also the teachers were able to improve their own methods.

Screen-Capturing System with Two-Layer Display for PowerPoint Presentation to Enhance Classroom Education

ERIC Educational Resources Information Center

Lai, Yen-Shou; Tsai, Hung-Hsu; Yu, Pao-Ta

2011-01-01

This paper proposes a new presentation system integrating a Microsoft PowerPoint presentation in a two-layer method, called the TL system, to promote learning in a physical classroom. With the TL system, teachers can readily control hints or annotations as a way of making them visible or invisible to students so as to reduce information load. In…

The Variable of the Self in Classroom Research: A Brief and Incomplete History of My Work as a Teacher Researcher

ERIC Educational Resources Information Center

Gallas, Karen

2010-01-01

This article traces Karen Gallas' experience as a teacher engaged in teacher research beginning in September of 1989 when she joined a weekly seminar in which teachers looked together at children's talk and while learning about methods of conducting classroom research on language. Gallas became very involved in what she now calls "Science…

Educational Game as Supplemental Learning Tool: Benefits, Challenges, and Tensions Arising from Use in an Elementary School Classroom

ERIC Educational Resources Information Center

Warren, Scott; Dondlinger, Mary Jo; Stein, Richard; Barab, Sasha

2009-01-01

This article examines the qualitative findings from a mixed-methods comparison study of the use of an online multi-user virtual environment called Anytown which supplemented face-to-face writing instruction in a fourth grade classroom to determine implications for the design of such environments and the reported impact of this design on students…

Strategic Decision Games: Improving Strategic Intuition

DTIC Science & Technology

2007-04-23

this is useful and enlightening , the truly powerful method of learning mathematical techniques is to work through many and various problems using...about, and approaches to, that new world. - General Tony Zinni, The Battle for Peace In 1972, evolutionary scientists Stephen Jay Gould and Niles... neurology and cognitive science, has identified a phenomenon similar to intuition that he calls “Intelligent Memory.” Dr. Gordon describes

District-Charter Partnerships: Collaboration to Advance Teaching and Learning in New York City. Year 1 Evaluation Results (2016-17)

ERIC Educational Resources Information Center

Litt, J.; Fishel, G.

2017-01-01

The Office of School Design and Charter Partnerships (OSDCP) at the New York City Department of Education (DOE) developed and executed a plan for district-charter collaboration, which they called the District-Charter Partnerships (DCP) initiative. This document describes the results of a mixed-method study of DCP conducted during the 2016-17…

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts.

PubMed

Fernandes, Henrique; Zhang, Hai; Figueiredo, Alisson; Malheiros, Fernando; Ignacio, Luis Henrique; Sfarra, Stefano; Ibarra-Castanedo, Clemente; Guimaraes, Gilmar; Maldague, Xavier

2018-01-19

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts

PubMed Central

Maldague, Xavier

2018-01-01

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed. PMID:29351240

Teaching Theory Construction With Initial Grounded Theory Tools: A Reflection on Lessons and Learning.

PubMed

Charmaz, Kathy

2015-12-01

This article addresses criticisms of qualitative research for spawning studies that lack analytic development and theoretical import. It focuses on teaching initial grounded theory tools while interviewing, coding, and writing memos for the purpose of scaling up the analytic level of students' research and advancing theory construction. Adopting these tools can improve teaching qualitative methods at all levels although doctoral education is emphasized here. What teachers cover in qualitative methods courses matters. The pedagogy presented here requires a supportive environment and relies on demonstration, collective participation, measured tasks, progressive analytic complexity, and accountability. Lessons learned from using initial grounded theory tools are exemplified in a doctoral student's coding and memo-writing excerpts that demonstrate progressive analytic development. The conclusion calls for increasing the number and depth of qualitative methods courses and for creating a cadre of expert qualitative methodologists. © The Author(s) 2015.

Relabeling exchange method (REM) for learning in neural networks

NASA Astrophysics Data System (ADS)

Wu, Wen; Mammone, Richard J.

1994-02-01

The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

The structure of stereotyped calls reflects kinship and social affiliation in resident killer whales (Orcinus orca).

PubMed

Deecke, Volker B; Barrett-Lennard, Lance G; Spong, Paul; Ford, John K B

2010-05-01

A few species of mammals produce group-specific vocalisations that are passed on by learning, but the function of learned vocal variation remains poorly understood. Resident killer whales live in stable matrilineal groups with repertoires of seven to 17 stereotyped call types. Some types are shared among matrilines, but their structure typically shows matriline-specific differences. Our objective was to analyse calls of nine killer whale matrilines in British Columbia to test whether call similarity primarily reflects social or genetic relationships. Recordings were made in 1985-1995 in the presence of focal matrilines that were either alone or with groups with non-overlapping repertoires. We used neural network discrimination performance to measure the similarity of call types produced by different matrilines and determined matriline association rates from 757 encounters with one or more focal matrilines. Relatedness was measured by comparing variation at 11 microsatellite loci for the oldest female in each group. Call similarity was positively correlated with association rates for two of the three call types analysed. Similarity of the N4 call type was also correlated with matriarch relatedness. No relationship between relatedness and association frequency was detected. These results show that call structure reflects relatedness and social affiliation, but not because related groups spend more time together. Instead, call structure appears to play a role in kin recognition and shapes the association behaviour of killer whale groups. Our results therefore support the hypothesis that increasing social complexity plays a role in the evolution of learned vocalisations in some mammalian species.

The structure of stereotyped calls reflects kinship and social affiliation in resident killer whales ( Orcinus orca)

NASA Astrophysics Data System (ADS)

Deecke, Volker B.; Barrett-Lennard, Lance G.; Spong, Paul; Ford, John K. B.

2010-05-01

A few species of mammals produce group-specific vocalisations that are passed on by learning, but the function of learned vocal variation remains poorly understood. Resident killer whales live in stable matrilineal groups with repertoires of seven to 17 stereotyped call types. Some types are shared among matrilines, but their structure typically shows matriline-specific differences. Our objective was to analyse calls of nine killer whale matrilines in British Columbia to test whether call similarity primarily reflects social or genetic relationships. Recordings were made in 1985-1995 in the presence of focal matrilines that were either alone or with groups with non-overlapping repertoires. We used neural network discrimination performance to measure the similarity of call types produced by different matrilines and determined matriline association rates from 757 encounters with one or more focal matrilines. Relatedness was measured by comparing variation at 11 microsatellite loci for the oldest female in each group. Call similarity was positively correlated with association rates for two of the three call types analysed. Similarity of the N4 call type was also correlated with matriarch relatedness. No relationship between relatedness and association frequency was detected. These results show that call structure reflects relatedness and social affiliation, but not because related groups spend more time together. Instead, call structure appears to play a role in kin recognition and shapes the association behaviour of killer whale groups. Our results therefore support the hypothesis that increasing social complexity plays a role in the evolution of learned vocalisations in some mammalian species.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

2017-02-01

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Evidence for vocal learning in juvenile male killer whales, Orcinus orca, from an adventitious cross-socializing experiment.

PubMed

Crance, Jessica L; Bowles, Ann E; Garver, Alan

2014-04-15

Killer whales (Orcinus orca) are thought to learn their vocal dialect. Dispersal in the species is rare, but effects of shifts in social association on the dialect can be studied under controlled conditions. Individual call repertoires and social association were measured in three adult female killer whales and three males (two juveniles and an adult) during two periods, 2001-2003 and 2005-2006. Three distinct dialect repertoires were represented among the subjects. An adventitious experiment in social change resulted from the birth of a calf and the transfer of two non-focal subjects in 2004. Across the two periods, 1691 calls were collected, categorized and attributed to individuals. Repertoire overlap for each subject dyad was compared with an index of association. During 2005-2006, the two juvenile males increased association with the unrelated adult male. By the end of the period, both had begun producing novel calls and call features characteristic of his repertoire. However, there was little or no reciprocal change and the adult females did not acquire his calls. Repertoire overlap and association were significantly correlated in the first period. In the second, median association time and repertoire similarity increased, but the relationship was only marginally significant. The results provided evidence that juvenile male killer whales are capable of learning new call types, possibly stimulated by a change in social association. The pattern of learning was consistent with a selective convergence of male repertoires.

Sharing Histories-a transformative learning/teaching method to empower community health workers to support health behavior change of mothers.

PubMed

Altobelli, Laura C

2017-08-23

One of the keys to improving health globally is promoting mothers' adoption of healthy home practices for improved nutrition and illness prevention in the first 1000 days of life from conception. Customarily, mothers are taught health messages which, even if simplified, are hard to remember. The challenge is how to promote learning and behavior change of mothers more effectively in low-resource settings where access to health information is poor, educational levels are low, and traditional beliefs are strong. In addressing that challenge, a new learning/teaching method called "Sharing Histories" is in development to improve the performance of female community health workers (CHWs) in promoting mothers' behaviors for maternal, neonatal and child health (MNCH). This method builds self-confidence and empowerment of CHWs in learning sessions that are built on guided sharing of their own memories of childbearing and child care. CHWs can later share histories with the mother, building her trust and empowerment to change. For professional primary health care staff who are not educators, Sharing Histories is simple to learn and use so that the method can be easily incorporated into government health systems and ongoing CHW programs. I present here the Sharing Histories method, describe how it differs from other social and behavior change methods, and discuss selected literature from psychology, communications, and neuroscience that helps to explain how and why this method works as a transformative tool to engage, teach, transform, and empower CHWs to be more effective change agents with other mothers in their communities, thereby contributing to the attainment of the Sustainable Development Goals.

Research on CALL.

ERIC Educational Resources Information Center

Pederson, Kathleen Marshall

The status of research on computer-assisted language learning (CALL) is explored beginning with a historical perspective of research on the language laboratory, followed by analyses of applied research on CALL. A theoretical base is provided to illustrate the need for more basic research on CALL that considers computer capabilities, learner…

Machine learning with quantum relative entropy

NASA Astrophysics Data System (ADS)

Tsuda, Koji

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Learning Progressions as Tools for Assessment and Learning

ERIC Educational Resources Information Center

Shepard, Lorrie A.

2018-01-01

This article addresses the teaching and learning side of the learning progressions literature, calling out for measurement specialists the knowledge most needed when collaborating with subject-matter experts in the development of learning progressions. Learning progressions are one of the strongest instantiations of principles from "Knowing…

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

PubMed

Avey, Marc T; Hoeschele, Marisa; Moscicki, Michele K; Bloomfield, Laurie L; Sturdy, Christopher B

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

Dialogue-Based Call: A Case Study on Teaching Pronouns

ERIC Educational Resources Information Center

Vlugter, P.; Knott, A.; McDonald, J.; Hall, C.

2009-01-01

We describe a computer assisted language learning (CALL) system that uses human-machine dialogue as its medium of interaction. The system was developed to help students learn the basics of the Maori language and was designed to accompany the introductory course in Maori running at the University of Otago. The student engages in a task-based…

CALLing All Foreign Language Teachers: Computer-Assisted Language Learning in the Classroom

ERIC Educational Resources Information Center

Erben, Tony, Ed.; Sarieva, Iona, Ed.

2008-01-01

This book is a comprehensive guide to help foreign language teachers use technology in their classrooms. It offers the best ways to integrate technology into teaching for student-centered learning. CALL Activities include: Email; Building a Web site; Using search engines; Powerpoint; Desktop publishing; Creating sound files; iMovie; Internet chat;…

"Learning Maps" vs. "Instructional Maps": User Guide for Learners vs. Instructional Tool for Designers

ERIC Educational Resources Information Center

Gropper, George L.

2016-01-01

A prescription favored in this article calls for the joint use of "learning maps" and "instructional maps." Why then the "Vs." in the title? Simply put, it is a rhetorical device. It calls attention to a key difference between the two. This article explicates the difference. It also informs how alone and in…

A Corpus-Based System of Error Detection and Revision Suggestion for Spanish Learners in Taiwan: A Case Study

ERIC Educational Resources Information Center

Lu, Hui-Chuan; Chu, Yu-Hsin; Chang, Cheng-Yu

2013-01-01

Compared with English learners, Spanish learners have fewer resources for automatic error detection and revision and following the current integrative Computer Assisted Language Learning (CALL), we combined corpus-based approach and CALL to create the System of Error Detection and Revision Suggestion (SEDRS) for learning Spanish. Through…

A Crucible Moment: College Learning and Democracy's Future. A National Call to Action

ERIC Educational Resources Information Center

Association of American Colleges and Universities (NJ1), 2012

2012-01-01

This report from the National Task Force on Civic Learning and Democratic Engagement calls on the nation to reclaim higher education's civic mission. Commissioned by the Department of Education and released at a White House convening in January 2012, the report pushes back against a prevailing national dialogue that limits the mission of higher…

The Contribution of CALL to Advanced-Level Foreign/Second Language Instruction

ERIC Educational Resources Information Center

Burston, Jack; Arispe, Kelly

2016-01-01

This paper evaluates the contribution of instructional technology to advanced-level foreign/second language learning (AL2) over the past thirty years. It is shown that the most salient feature of AL2 practice and associated Computer-Assisted Language Learning (CALL) research are their rarity and restricted nature. Based on an analysis of four…

Does In-Service Professional Learning for High School Economics Teachers Improve Student Achievement?

ERIC Educational Resources Information Center

Swinton, John R.; De Berry, Thomas; Scafidi, Benjamin; Woodard, Howard C.

2010-01-01

Education policy analysts and professional educators have called for more and better professional learning opportunities for in-service teachers, and for at least 30 years economists called for more content training for high school economics teachers. Using new data from all Georgia high school economics students, we assess the impact of…

Students' and Instructors' Attitudes toward the Use of CALL in Foreign Language Teaching and Learning

ERIC Educational Resources Information Center

Wiebe, Grace; Kabata, Kaori

2010-01-01

This study examines the effects of educational technologies on the attitudes of both the instructors and the students. The results indicate that there is a discrepancy between the students' awareness of the instructors' goals for using new technologies and the importance instructors placed on computer assisted language learning (CALL). The data…

Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

PubMed

Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

2013-01-01

Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces sparser coefficients than the original SPF basis and results in significantly lower reconstruction error than Bilgic et al.'s method.

John M. Eisenberg Patient Safety Awards. System innovation: Veterans Health Administration National Center for Patient Safety.

PubMed

Heget, Jeffrey R; Bagian, James P; Lee, Caryl Z; Gosbee, John W

2002-12-01

In 1998 the Veterans Health Administration (VHA) created the National Center for Patient Safety (NCPS) to lead the effort to reduce adverse events and close calls systemwide. NCPS's aim is to foster a culture of safety in the Department of Veterans Affairs (VA) by developing and providing patient safety programs and delivering standardized tools, methods, and initiatives to the 163 VA facilities. To create a system-oriented approach to patient safety, NCPS looked for models in fields such as aviation, nuclear power, human factors, and safety engineering. Core concepts included a non-punitive approach to patient safety activities that emphasizes systems-based learning, the active seeking out of close calls, which are viewed as opportunities for learning and investigation, and the use of interdisciplinary teams to investigate close calls and adverse events through a root cause analysis (RCA) process. Participation by VA facilities and networks was voluntary. NCPS has always aimed to develop a program that would be applicable both within the VA and beyond. NCPS's full patient safety program was tested and implemented throughout the VA system from November 1999 to August 2000. Program components included an RCA system for use by caregivers at the front line, a system for the aggregate review of RCA results, information systems software, alerts and advisories, and cognitive acids. Following program implementation, NCPS saw a 900-fold increase in reporting of close calls of high-priority events, reflecting the level of commitment to the program by VHA leaders and staff.

The use of blended learning to create a module about ill-health during childbirth for pre-registration midwifery students.

PubMed

Young, Nicki; Randall, Jayne

2014-01-01

Reforms in the way higher education is delivered in order to address the needs of learners in the 21st century are increasingly being considered by university departments. This has led academics to combine e-learning with more traditional classroom based methods of teaching when designing new modules of study, a method commonly called blended learning. This paper will describe the different teaching and learning methods which were blended together to create a module for second year pre-registration midwifery students in England, which focused upon ill-health during pregnancy and childbearing. It is imperative that at the point of registration midwifery students possess the skills to identify deviations from normal, initiate immediate actions and make appropriate referrals. The health of women all over the world is of concern to health care professionals. Midwives are increasingly being upon to provide expert care. Midwives need a sound education to allow them to carry out their roles effectively. The International Confederation of Midwives global standards for midwifery education (2010) attempts to address the need for competent caring midwives to help women and families in every corner of the world. The paper will also cover the pedagogical issues considered when blending together the different elements of learning namely: traditional discursive lectures, small group work, e-learning, formative presentations and the use of simulation during a skills and drills day. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

PubMed

Liu, Zhenqiu; Hsiao, William; Cantarel, Brandi L; Drábek, Elliott Franco; Fraser-Liggett, Claire

2011-12-01

Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. zliu@umm.edu Supplementary data are available at Bioinformatics online.

Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

PubMed Central

Devlaminck, Dieter; Wyns, Bart; Grosse-Wentrup, Moritz; Otte, Georges; Santens, Patrick

2011-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low. PMID:22007194

Interpretable Deep Models for ICU Outcome Prediction

PubMed Central

Che, Zhengping; Purushotham, Sanjay; Khemani, Robinder; Liu, Yan

2016-01-01

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians. PMID:28269832

Twelve tips for facilitating Millennials' learning.

PubMed

Roberts, David H; Newman, Lori R; Schwartzstein, Richard M

2012-01-01

The current, so-called "Millennial" generation of learners is frequently characterized as having deep understanding of, and appreciation for, technology and social connectedness. This generation of learners has also been molded by a unique set of cultural influences that are essential for medical educators to consider in all aspects of their teaching, including curriculum design, student assessment, and interactions between faculty and learners.  The following tips outline an approach to facilitating learning of our current generation of medical trainees.  The method is based on the available literature and the authors' experiences with Millennial Learners in medical training.  The 12 tips provide detailed approaches and specific strategies for understanding and engaging Millennial Learners and enhancing their learning.  With an increased understanding of the characteristics of the current generation of medical trainees, faculty will be better able to facilitate learning and optimize interactions with Millennial Learners.

WFUMB Position Paper. Learning Gastrointestinal Ultrasound: Theory and Practice.

PubMed

Atkinson, Nathan S S; Bryant, Robert V; Dong, Yi; Maaser, Christian; Kucharzik, Torsten; Maconi, Giovanni; Asthana, Anil K; Blaivas, Michael; Goudie, Adrian; Gilja, Odd Helge; Nolsøe, Christian; Nürnberg, Dieter; Dietrich, Christoph F

2016-12-01

Gastrointestinal ultrasound (GIUS) is an ultrasound application that has been practiced for more than 30 years. Recently, GIUS has enjoyed a resurgence of interest, and there is now strong evidence of its utility and accuracy as a diagnostic tool for multiple indications. The method of learning GIUS is not standardised and may incorporate mentorship, didactic teaching and e-learning. Simulation, using either low- or high-fidelity models, can also play a key role in practicing and honing novice GIUS skills. A course for training as well as establishing and evaluating competency in GIUS is proposed in the manuscript, based on established learning theory practice. We describe the broad utility of GIUS in clinical medicine, including a review of the literature and existing meta-analyses. Further, the manuscript calls for agreement on international standards regarding education, training and indications. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Search for Minimal and Semi-Minimal Rule Sets in Incremental Learning of Context-Free and Definite Clause Grammars

NASA Astrophysics Data System (ADS)

Imada, Keita; Nakamura, Katsuhiko

This paper describes recent improvements to Synapse system for incremental learning of general context-free grammars (CFGs) and definite clause grammars (DCGs) from positive and negative sample strings. An important feature of our approach is incremental learning, which is realized by a rule generation mechanism called “bridging” based on bottom-up parsing for positive samples and the search for rule sets. The sizes of rule sets and the computation time depend on the search strategies. In addition to the global search for synthesizing minimal rule sets and serial search, another method for synthesizing semi-optimum rule sets, we incorporate beam search to the system for synthesizing semi-minimal rule sets. The paper shows several experimental results on learning CFGs and DCGs, and we analyze the sizes of rule sets and the computation time.

Pedagogical Scholarship in Public Health: A Call for Cultivating Learning Communities to Support Evidence-Based Education.

PubMed

Merzel, Cheryl; Halkitis, Perry; Healton, Cheryl

Public health education is experiencing record growth and transformation. The current emphasis on learning outcomes necessitates attention to creating and evaluating the best curricula and learning methods for helping public health students develop public health competencies. Schools and programs of public health would benefit from active engagement in pedagogical research and additional platforms to support dissemination and implementation of educational research findings. We reviewed current avenues for sharing public health educational research, curricula, and best teaching practices; we identified useful models from other health professions; and we offered suggestions for how the field of public health education can develop communities of learning devoted to supporting pedagogy. Our goal was to help advance an agenda of innovative evidence-based public health education, enabling schools and programs of public health to evaluate and measure success in meeting the current and future needs of the public health profession.

ESD and lifelong learning: a case study of the Shangri-la Institute's current engagement with the Bazhu community in Diqing, China

NASA Astrophysics Data System (ADS)

Liu, Yunhua; Constable, Alicia

2010-06-01

This article argues that ESD should be integrated into lifelong learning and provides an example of how this might be done. It draws on a case study of a joint project between the Shangri-la Institute and the Bazhu community in Diqing, southwest China, to analyse a community-based approach to Education for Sustainable Development and assess its implications for lifelong learning. The article examines the different knowledge, skills and values needed for ESD across the life span and asserts the need for these competencies to be informed by the local context. The importance of linking ESD with local culture and indigenous knowledge is emphasised. The article goes on to propose methods for integrating ESD into lifelong learning and underscore the need for learning at the individual, institutional and societal levels in formal, non-formal and informal learning settings. It calls for institutional changes that link formal, non-formal and informal learning through the common theme of ESD, and establish platforms to share experiences, reflect on these and thereby continually improve ESD.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Inquiry-Based Integrated Science Education: Implementation of Local Content “Soil Washing” Project To Improve Junior High School Students’ Environmental Literacy

NASA Astrophysics Data System (ADS)

Syifahayu

2017-02-01

The study was conducted based on teaching and learning problems led by conventional method that had been done in the process of learning science. It gave students lack opportunities to develop their competence and thinking skills. Consequently, the process of learning science was neglected. Students did not have opportunity to improve their critical attitude and creative thinking skills. To cope this problem, the study was conducted using Project-Based Learning model through inquiry-based science education about environment. The study also used an approach called Sains Lingkungan and Teknologi masyarakat - “Saling Temas” (Environmental science and Technology in Society) which promoted the local content in Lampung as a theme in integrated science teaching and learning. The study was a quasi-experimental with pretest-posttest control group design. Initially, the subjects were given a pre-test. The experimental group was given inquiry learning method while the control group was given conventional learning. After the learning process, the subjects of both groups were given post-test. Quantitative analysis was performed using the Mann-Whitney U-test and also a qualitative descriptive. Based on the result, environmental literacy skills of students who get inquiry learning strategy, with project-based learning model on the theme soil washing, showed significant differences. The experimental group is better than the control group. Data analysis showed the p-value or sig. (2-tailed) is 0.000 <α = 0.05 with the average N-gain of experimental group is 34.72 and control group is 16.40. Besides, the learning process becomes more meaningful.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Potential Paradigms and Possible Problems for CALL.

ERIC Educational Resources Information Center

Phillips, Martin

1987-01-01

Describes three models of CALL (computer assisted language learning) activity--games, the expert system, and the prosthetic approaches. A case is made for CALL development within a more instrumental view of the role of computers. (Author/CB)

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

The Effects of Learning Styles and Meaningful Learning on the Learning Achievement of Gamification Health Education Curriculum

ERIC Educational Resources Information Center

Fan, Kuo-Kuang; Xiao, Peng-wei; Su, Chung-Ho

2015-01-01

This study aims to discuss the correlations among learning styles, meaningful learning, and learning achievement. Directed at the rather difficult to comprehend human blood circulation unit in the biology materials for junior high school students, a Mobile Meaningful Blood Circulation Learning System, called MMBCLS gamification learning, was…

Critical perspectives of pedagogical approaches to reversing the order of integration in double integrals

NASA Astrophysics Data System (ADS)

Tisdell, Christopher C.

2017-11-01

This paper presents some critical perspectives regarding pedagogical approaches to the method of reversing the order of integration in double integrals from prevailing educational literature on multivariable calculus. First, we question the message found in popular textbooks that the traditional process of reversing the order of integration is necessary when solving well-known problems. Second, we illustrate that the method of integration by parts can be directly applied to many of the classic pedagogical problems in the literature concerning double integrals, without taking the well-worn steps associated with reversing the order of integration. Third, we examine the benefits and limitations of such a method. In our conclusion, we advocate for integration by parts to be a part of the pedagogical conversation in the learning and teaching of double integral methods; and call for more debate around its use in the learning and teaching of other areas of mathematics. Finally, we emphasize the need for critical approaches in the pedagogy of mathematics more broadly.

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

PubMed

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

The Perceived Effect of Duty Hour Restrictions on Learning Opportunities in the Intensive Care Unit.

PubMed

Sabri, Nessrine; Sun, Ning-Zi; Cummings, Beth-Ann; Jayaraman, Dev

2015-03-01

Many countries have reduced resident duty hours in an effort to promote patient safety and enhance resident quality of life. There are concerns that reducing duty hours may impact residents' learning opportunities. We (1) evaluated residents' perceptions of their current learning opportunities in a context of reduced duty hours, and (2) explored the perceived change in resident learning opportunities after call length was reduced from 24 continuous hours to 16 hours. We conducted an anonymous, cross-sectional online survey of 240 first-, second-, and third-year residents rotating through 3 McGill University-affiliated intensive care units (ICUs) in Montreal, Quebec, Canada, between July 1, 2012, and June 30, 2013. The survey investigated residents' perceptions of learning opportunities in both the 24-hour and 16-hour systems. Of 240 residents, 168 (70%) completed the survey. Of these residents, 63 (38%) had been exposed to both 24-hour and 16-hour call schedules. The majority of respondents (83%) reported that didactic teaching sessions held by ICU staff physicians were useful. However, of the residents trained in both approaches to overnight call, 44% reported a reduction in learner attendance at didactic teaching sessions, 48% reported a reduction in attendance at midday hospital rounds, and 40% reported a perceived reduction in self-directed reading after the implementation of the new call schedule. A substantial proportion of residents perceived a reduction in the attendance of instructor-directed and self-directed reading after the implementation of a 16-hour call schedule in the ICU.

Teaching strategies to promote concept learning by design challenges

NASA Astrophysics Data System (ADS)

Van Breukelen, Dave; Van Meel, Adrianus; De Vries, Marc

2017-07-01

Background: This study is the second study of a design-based research, organised around four studies, that aims to improve student learning, teaching skills and teacher training concerning the design-based learning approach called Learning by Design (LBD).

Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.

Development of soft scaffolding strategy to improve student’s creative thinking ability in physics

NASA Astrophysics Data System (ADS)

Nurulsari, Novinta; Abdurrahman; Suyatna, Agus

2017-11-01

Student’s creative thinking ability in physics learning can be developed through a learning experience. However, many students fail to gain a learning experience because of the lack of teacher roles in providing assistance to students when they face learning difficulties. In this study, a soft scaffolding strategy developed to improve student’s creative thinking ability in physics, especially in optical instruments. The methods used were qualitative and quantitative. The soft scaffolding strategy developed was called the 6E Soft Scaffolding Strategy where 6E stands for Explore real-life problems, Engage students with web technology, Enable experiment using analogies, Elaborate data through multiple representations, Encourage questioning, and Ensure the feedback. The strategy was applied to 60 students in secondary school through cooperative learning. As a comparison, conventional strategies were also applied to 60 students in the same school and grade. The result of the study showed that the soft scaffolding strategy was effective in improving student’s creative thinking ability.

Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls.

PubMed

Shamir, Lior; Yerby, Carol; Simpson, Robert; von Benda-Beckmann, Alexander M; Tyack, Peter; Samarra, Filipa; Miller, Patrick; Wallin, John

2014-02-01

Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method.

Connecting Neighborhood Councils and City Agencies--Evaluation of the Learning and Design Forums and Role of the University as a Mediating Institution

ERIC Educational Resources Information Center

Kathi, Pradeep Chandra

2012-01-01

The School of Planning Policy and Development at the University of Southern California brought together representatives of neighborhood councils and city agencies of the city of Los Angeles together in an action research program. This action research program called the Collaborative Learning Project developed a collaboration process called the…

Learning Partnerships: Strengthening American Jobs in the Global Economy. A Report of the Task Force on Workforce Development

ERIC Educational Resources Information Center

Albert Shanker Institute, 2004

2004-01-01

Global competition, sweeping technological change, and demographic shifts in the labor force call for a national campaign to improve the skills and professionalism of the American workforce. This document calls for the creation of new learning partnerships throughout communities and workplaces to sustain middle-class jobs, pay the social costs of…

The Path to English Literacy: Analyzing Elementary Sight Word Procurement Using Computer Assisted Language Learning (CALL) in Contrast to Traditional Methodologies

ERIC Educational Resources Information Center

Madill, Michael T. R.

2014-01-01

Didactical approaches related to teaching English as a Foreign Language (EFL) have developed into a complex array of instructional methodologies, each having potential benefits attributed to elementary reading development. One such effective practice is Computer Assisted Language Learning (CALL), which uses various forms of technology such as…

I'm a Useful NLP Tool--Get Me out of Here

ERIC Educational Resources Information Center

Ward, Monica

2015-01-01

Irish is a compulsory subject in Irish schools. However, there are several pedagogical issues with teaching and learning the language. Computer-Assisted Language Learning (CALL) is under-utilised in schools in Ireland and even more so in the case of Irish, as there are very few CALL resources for the language. This paper looks at the lessons…

The Effect of Computer Assisted Language Learning (CALL) on Performance in the Test of English for International Communication (TOEIC) Listening Module

ERIC Educational Resources Information Center

van Han, Nguyen; van Rensburg, Henriette

2014-01-01

Many companies and organizations have been using the Test of English for International Communication (TOEIC) for business and commercial communication purpose in Vietnam and around the world. The present study investigated the effect of Computer Assisted Language Learning (CALL) on performance in the Test of English for International Communication…

The Effect of Computer-Assisted Language Learning on Reading Comprehension in an Iranian EFL Context

ERIC Educational Resources Information Center

Saeidi, Mahnaz; Yusefi, Mahsa

2012-01-01

This study is an attempt to examine the effect of computer-assisted language learning (CALL) on reading comprehension in an Iranian English as a foreign language (EFL) context. It was hypothesized that CALL has an effect on reading comprehension. Forty female learners of English at intermediate level after administering a proficiency test were…

Validating a Computer-Assisted Language Learning Attitude Instrument Used in Iranian EFL Context: An Evidence-Based Approach

ERIC Educational Resources Information Center

Aryadoust, Vahid; Mehran, Parisa; Alizadeh, Mehrasa

2016-01-01

A few computer-assisted language learning (CALL) instruments have been developed in Iran to measure EFL (English as a foreign language) learners' attitude toward CALL. However, these instruments have no solid validity argument and accordingly would be unable to provide a reliable measurement of attitude. The present study aimed to develop a CALL…

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

PubMed Central

Guvensan, M. Amac; Kansiz, A. Oguz; Camgoz, N. Cihan; Turkmen, H. Irem; Yavuz, A. Gokhan; Karsligil, M. Elif

2017-01-01

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions. PMID:28644378

Pattern recognition neural-net by spatial mapping of biology visual field

NASA Astrophysics Data System (ADS)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

FloCon 2005 Proceedings

DTIC Science & Technology

2005-09-01

from one subject to another [5, 6]. Since covert com - munication is very difficult to detect, most researchers resort to investigating methods that...situations (unlike our own) where traffic is not filtered (a darknet , for example). To prevent isolated anomalies during the learning pe- riod from...call to the computer running the flow tools. Then, using a standard R data input function, the wrapper function reads in the ASCII output of the com

The Evolution of Distance Learning: Technology-Mediated Interactive Learning.

ERIC Educational Resources Information Center

Dede, Christopher J.

1990-01-01

Summarizes a paper prepared for the Office of Technology Assessment (OTA) on the evolution of distance learning which begins by describing technological, the demographic, economic, political, and pedagogical forces involved. A new field is proposed called technology-mediated interactive learning (TMIL), which synthesizes distance learning,…

Operation Breakthrough for Continuous Self-Systems Improvement.

ERIC Educational Resources Information Center

Given, Barbara K.

1994-01-01

Operation Breakthrough, in which graduate student interns teach life skills to adolescents with learning disabilities, provided an impetus for identifying a profile of learning and work habits necessary for production of an agile workforce. Agile learning for self-systems improvement calls for self-empowered learning, collaborative learning,…

Extended Relation Metadata for SCORM-Based Learning Content Management Systems

ERIC Educational Resources Information Center

Lu, Eric Jui-Lin; Horng, Gwoboa; Yu, Chia-Ssu; Chou, Ling-Ying

2010-01-01

To increase the interoperability and reusability of learning objects, Advanced Distributed Learning Initiative developed a model called Content Aggregation Model (CAM) to describe learning objects and express relationships between learning objects. However, the suggested relations defined in the CAM can only describe structure-oriented…

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

Integrating CALL into the Classroom: The Role of Podcasting in an ESL Listening Strategies Course

ERIC Educational Resources Information Center

O'Brien, Anne; Hegelheimer, Volker

2007-01-01

Despite the increase of teacher preparation programs that emphasize the importance of training teachers to select and develop appropriate computer-assisted language learning (CALL) materials, integration of CALL into classroom settings is still frequently relegated to the use of selected CALL activities to supplement instruction or to provide…

Call mimicry by eastern towhees and its significance in relation to auditory learning

Treesearch

Jon S. Greenlaw; Clifford E. Shackelford; Raymond E. Brown

1998-01-01

The authors document cases of eastern towhees (Pipilo erythrophthalmus) using mimicked alarm calls from three presumptive models (blue jay (Cyanocitta cristata), brown thrasher (Toxostoma rufum), and American robin (Turdus migratorius)). In four instances, male towhees employed heterospecific calls without substitution in their own call repertoires. Three birds (New...

ICALL's Relevance to CALL

ERIC Educational Resources Information Center

Ward, Monica

2017-01-01

The term Intelligent Computer Assisted Language Learning (ICALL) covers many different aspects of CALL that add something extra to a CALL resource. This could be with the use of computational linguistics or Artificial Intelligence (AI). ICALL tends to be not very well understood within the CALL community. There may also be the slight fear factor…

Causal cognition in a non-human primate: field playback experiments with Diana monkeys.

PubMed

Zuberbühler, K

2000-09-14

Crested guinea fowls (Guttera pucherani) living in West African rainforests give alarm calls to leopards (Panthera pardus) and sometimes humans (Homo sapiens), two main predators of sympatric Diana monkeys (Cercopithecus diana). When hearing these guinea fowl alarm calls, Diana monkeys respond as if a leopard were present, suggesting that by default the monkeys associate guinea fowl alarm calls with the presence of a leopard. To assess the monkeys' level of causal understanding, I primed monkeys to the presence of either a leopard or a human, before exposing them to playbacks of guinea fowl alarm calls. There were significant differences in the way leopard-primed groups and human-primed groups responded to guinea fowl alarm calls, suggesting that the monkeys' response was not directly driven by the alarm calls themselves but by the calls' underlying cause, i.e. the predator most likely to have caused the calls. Results are discussed with respect to three possible cognitive mechanisms - associative learning, specialized learning programs, and causal reasoning - that could have led to causal knowledge in Diana monkeys.

Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation

ERIC Educational Resources Information Center

Milligan, Sandra; Griffin, Patrick

2016-01-01

The paper describes empirical investigations of how participants in a MOOC learn, and the implications for MOOC design. A learner capability to generate higher order learning in MOOCs--called crowd-sourced learning (C-SL) capability--was defined from learning science literature. The capability comprised a complex yet interrelated array of…

Vertical transmission of learned signatures in a wild parrot

PubMed Central

Berg, Karl S.; Delgado, Soraya; Cortopassi, Kathryn A.; Beissinger, Steven R.; Bradbury, Jack W.

2012-01-01

Learned birdsong is a widely used animal model for understanding the acquisition of human speech. Male songbirds often learn songs from adult males during sensitive periods early in life, and sing to attract mates and defend territories. In presumably all of the 350+ parrot species, individuals of both sexes commonly learn vocal signals throughout life to satisfy a wide variety of social functions. Despite intriguing parallels with humans, there have been no experimental studies demonstrating learned vocal production in wild parrots. We studied contact call learning in video-rigged nests of a well-known marked population of green-rumped parrotlets (Forpus passerinus) in Venezuela. Both sexes of naive nestlings developed individually unique contact calls in the nest, and we demonstrate experimentally that signature attributes are learned from both primary care-givers. This represents the first experimental evidence for the mechanisms underlying the transmission of a socially acquired trait in a wild parrot population. PMID:21752824

Implications of Research on Human Memory for CALL Design.

ERIC Educational Resources Information Center

Forester, Lee

2002-01-01

Offers a brief overview of what is generally accepted about how human memory works as it applied to computer assisted language learning (CALL). Discusses a number of interactions from various CALL products in light of the research summarized. (Author/VWL)

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

Full-Physics Inverse Learning Machine for Satellite Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Loyola, D. G.

2017-12-01

The satellite remote sensing retrievals are usually ill-posed inverse problems that are typically solved by finding a state vector that minimizes the residual between simulated data and real measurements. The classical inversion methods are very time-consuming as they require iterative calls to complex radiative-transfer forward models to simulate radiances and Jacobians, and subsequent inversion of relatively large matrices. In this work we present a novel and extremely fast algorithm for solving inverse problems called full-physics inverse learning machine (FP-ILM). The FP-ILM algorithm consists of a training phase in which machine learning techniques are used to derive an inversion operator based on synthetic data generated using a radiative transfer model (which expresses the "full-physics" component) and the smart sampling technique, and an operational phase in which the inversion operator is applied to real measurements. FP-ILM has been successfully applied to the retrieval of the SO2 plume height during volcanic eruptions and to the retrieval of ozone profile shapes from UV/VIS satellite sensors. Furthermore, FP-ILM will be used for the near-real-time processing of the upcoming generation of European Sentinel sensors with their unprecedented spectral and spatial resolution and associated large increases in the amount of data.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Lessons Learned for Follow-up Phone Booster Counseling Calls with Substance Abusing Emergency Department Patients

PubMed Central

Donovan, Dennis M.; Hatch-Maillette, Mary A.; Phares, Melissa M.; McGarry, Ernest; Peavy, K. Michelle; Taborsky, Julie

2014-01-01

Background Post-visit “booster” sessions have been recommended to augment the impact of brief interventions delivered in the Emergency Department (ED). This paper, which focuses on implementation issues, presents descriptive information and interventionists’ qualitative perspectives on providing brief interventions over the phone, challenges, “lessons learned”, and recommendations for others attempting to implement adjunctive booster calls. Method Attempts were made to complete two 20-minute telephone “booster” calls within a week following a patient’s ED discharge with 425 patients who screened positive for and had recent problematic substance use other than alcohol or nicotine. Results Over half (56.2%) of participants completed the initial call; 66.9% of those who received the initial call also completed the second call. Median number of attempts to successfully contact participants for the first and second calls was 4 and 3, respectively. Each completed call lasted an average of about 22 minutes. Common challenges/barriers identified by booster callers included unstable housing, limited phone access, unavailability due to additional treatment, lack of compensation for booster calls, and booster calls coming from an area code different than the participants’ locale and from someone other than ED staff. Conclusions Specific recommendations are presented with respect to implementing a successful centralized adjunctive booster call system. Future use of booster calls might be informed by research on contingency management (e.g., incentivizing call completions), smoking cessation quitlines, and phone-based continuing care for substance abuse patients. Future research needs to evaluate the incremental benefit of adjunctive booster calls on outcomes over and above that of brief motivational interventions delivered in the ED setting. PMID:25534151

A Context-Aware Ubiquitous Learning Environment for Language Listening and Speaking

ERIC Educational Resources Information Center

Liu, T.-Y.

2009-01-01

This paper reported the results of a study that aimed to construct a sensor and handheld augmented reality (AR)-supported ubiquitous learning (u-learning) environment called the Handheld English Language Learning Organization (HELLO), which is geared towards enhancing students' language learning. The HELLO integrates sensors, AR, ubiquitous…

A Rebuttal of NTL Institute's Learning Pyramid

ERIC Educational Resources Information Center

Letrud, Kare

2012-01-01

This article discusses the learning pyramid corroborated by National Training Laboratories Institute. It present and compliment historical and methodological critique against the learning pyramid, and call upon NTL Institute ought to retract their model.

Workshop on Friction: Understanding and Addressing Students' Difficulties in Learning Science Through a Hermeneutical Perspective

NASA Astrophysics Data System (ADS)

Ha, Sangwoo; Lee, Gyoungho; Kalman, Calvin S.

2013-06-01

Hermeneutics is useful in science and science education by emphasizing the process of understanding. The purpose of this study was to construct a workshop based upon hermeneutical principles and to interpret students' learning in the workshop through a hermeneutical perspective. When considering the history of Newtonian mechanics, it could be considered that there are two methods of approaching Newtonian mechanics. One method is called the `prediction approach', and the other is called the `explanation approach'. The `prediction approach' refers to the application of the principles of Newtonian mechanics. We commonly use the prediction approach because its logical process is natural to us. However, its use is correct only when a force, such as gravitation, is exactly known. On the other hand, the `explanation approach' could be used when the nature of a force is not exactly known. In the workshop, students read a short text offering contradicting ideas about whether to analyze a friction situation using the explanation approach or the prediction approach. Twenty-two college students taking an upper-level mechanics course wrote their ideas about the text. The participants then discussed their ideas within six groups, each composed of three or four students. Through the group discussion, students were able to clarify their preconceptions about friction, and they responded to the group discussion positively. Students started to think about their learning from a holistic perspective. As students thought and discussed the friction problems in the manner of hermeneutical circles, they moved toward a better understanding of friction.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

The Learning Conference

ERIC Educational Resources Information Center

Ravn, Ib

2007-01-01

Purpose: The purpose of this paper is to call attention to the fact that conferences for professionals rely on massive one-way communication and hence produce little learning for delegates--and to introduce an alternative, the "learning conference", that involves delegates in fun and productive learning processes.…

Brain Type or Sex Differences? A Structural Equation Model of the Relation between Brain Type, Sex, and Motivation to Learn Science

ERIC Educational Resources Information Center

Zeyer, Albert; Bolsterli, Katrin; Brovelli, Dorothee; Odermatt, Freia

2012-01-01

Sex is considered to be one of the most significant factors influencing attitudes towards science. However, the so-called brain type approach from cognitive science suggests that the difference in motivation to learn science does not primarily differentiate the girls from the boys, but rather the so-called systemisers from the empathizers. The…

From Computer Assisted Language Learning (CALL) to Mobile Assisted Language Use (MALU)

ERIC Educational Resources Information Center

Jarvis, Huw; Achilleos, Marianna

2013-01-01

This article begins by critiquing the long-established acronym CALL (Computer Assisted Language Learning). We then go on to report on a small-scale study which examines how student non-native speakers of English use a range of digital devices beyond the classroom in both their first (L1) and second (L2) languages. We look also at the extent to…

Query-based learning for aerospace applications.

PubMed

Saad, E W; Choi, J J; Vian, J L; Wunsch, D C Ii

2003-01-01

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.

Process-Oriented Guided Inquiry Learning (POGIL) as a Culturally Relevant Pedagogy (CRP) in Qatar: a Perspective from Grade 10 Chemistry Classes

NASA Astrophysics Data System (ADS)

Treagust, David F.; Qureshi, Sheila S.; Vishnumolakala, Venkat Rao; Ojeil, Joseph; Mocerino, Mauro; Southam, Daniel C.

2018-04-01

Educational reforms in Qatar have seen the implementation of inquiry-based learning and other student-centred pedagogies. However, there have been few efforts to investigate how these adopted western pedagogies are aligned with the high context culture of Qatar. The study presented in this article highlights the implementation of a student-centred intervention called Process-Oriented Guided Inquiry Learning (POGIL) in selected independent Arabic government schools in Qatar. The study followed a theoretical framework composed of culturally relevant pedagogical practice and social constructivism in teaching and learning. A mixed method research design involving experimental and comparison groups was utilised. Carefully structured learning materials when implemented systematically in a POGIL intervention helped Grade 10 science students improve their perceptions of chemistry learning measured from pre- and post-tests as measured by the What Is Happening In this Class (WIHIC) questionnaire and school-administered achievement test. The study further provided school-based mentoring and professional development opportunities for teachers in the region. Significantly, POGIL was found to be adaptable in the Arabic context.

[Continuum, the continuing education platform based on a competency matrix].

PubMed

Ochoa Sangrador, C; Villaizán Pérez, C; González de Dios, J; Hijano Bandera, F; Málaga Guerrero, S

2016-04-01

Competency-Based Education is a learning method that has changed the traditional teaching-based focus to a learning-based one. Students are the centre of the process, in which they must learn to learn, solve problems, and adapt to changes in their environment. The goal is to provide learning based on knowledge, skills (know-how), attitude and behaviour. These sets of knowledge are called competencies. It is essential to have a reference of the required competencies in order to identify the need for them. Their acquisition is approached through teaching modules, in which one or more skills can be acquired. This teaching strategy has been adopted by Continuum, the distance learning platform of the Spanish Paediatric Association, which has developed a competency matrix based on the Global Paediatric Education Consortium training program. In this article, a review will be presented on the basics of Competency-Based Education and how it is applied in Continuum. Copyright © 2015 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

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.

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Validating e-learning in continuing pharmacy education: user acceptance and knowledge change

PubMed Central

2014-01-01

Background Continuing pharmacy education is becoming mandatory in most countries in order to keep the professional license valid. Increasing number of pharmacists are now using e-learning as part of their continuing education. Consequently, the increasing popularity of this method of education calls for standardization and validation practices. The conducted research explored validation aspects of e-learning in terms of knowledge increase and user acceptance. Methods Two e-courses were conducted as e-based continuing pharmacy education for graduated pharmacists. Knowledge increase and user acceptance were the two outcome measured. The change of knowledge in the first e-course was measured by a pre- and post-test and results analysed by the Wilcoxon signed–rank test. The acceptance of e-learning in the second e-course was investigated by a questionnaire and the results analysed using descriptive statistics. Results Results showed that knowledge increased significantly (p < 0.001) by 16 pp after participation in the first e-course. Among the participants who responded to the survey in the second course, 92% stated that e-courses were effective and 91% stated that they enjoyed the course. Conclusions The study shows that e-learning is a viable medium of conducting continuing pharmacy education; e-learning is effective in increasing knowledge and highly accepted by pharmacists from various working environments such as community and hospital pharmacies, faculties of pharmacy or wholesales. PMID:24528547

Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation

PubMed Central

Spratling, M. W.; De Meyer, K.; Kompass, R.

2009-01-01

This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance. PMID:19424442

Lessons Learned From a Community–Academic Initiative: The Development of a Core Competency–Based Training for Community–Academic Initiative Community Health Workers

PubMed Central

Matos, Sergio; Kapadia, Smiti; Islam, Nadia; Cusack, Arthur; Kwong, Sylvia; Trinh-Shevrin, Chau

2012-01-01

Objectives. Despite the importance of community health workers (CHWs) in strategies to reduce health disparities and the call to enhance their roles in research, little information exists on how to prepare CHWs involved in community–academic initiatives (CAIs). Therefore, the New York University Prevention Research Center piloted a CAI–CHW training program. Methods. We applied a core competency framework to an existing CHW curriculum and bolstered the curriculum to include research-specific sessions. We employed diverse training methods, guided by adult learning principles and popular education philosophy. Evaluation instruments assessed changes related to confidence, intention to use learned skills, usefulness of sessions, and satisfaction with the training. Results. Results demonstrated that a core competency–based training can successfully affect CHWs’ perceived confidence and intentions to apply learned content, and can provide a larger social justice context of their role and work. Conclusions. This program demonstrates that a core competency–based framework coupled with CAI-research–specific skill sessions (1) provides skills that CAI–CHWs intend to use, (2) builds confidence, and (3) provides participants with a more contextualized view of client needs and CHW roles. PMID:22594730

Integrative relational machine-learning for understanding drug side-effect profiles

PubMed Central

2013-01-01

Background Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. Results In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Conclusions Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs. PMID:23802887

Integrative relational machine-learning for understanding drug side-effect profiles.

PubMed

Bresso, Emmanuel; Grisoni, Renaud; Marchetti, Gino; Karaboga, Arnaud Sinan; Souchet, Michel; Devignes, Marie-Dominique; Smaïl-Tabbone, Malika

2013-06-26

Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Frank Gilbreth and health care delivery method study driven learning.

PubMed

Towill, Denis R

2009-01-01

The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised in the "learn-do-learn-do" feedback loop in the Gilbreth Knowledge Flywheel.

Teaching 2.0: Teams Keep Teachers and Students Plugged into Technology

ERIC Educational Resources Information Center

Bourgeois, Michelle; Hunt, Bud

2011-01-01

A Colorado district develops a two-year program that gives teacher teams an opportunity to learn how to use digital tools in the classroom. Called the Digital Learning Collaborative, it is built on three things about professional learning: (1) Learning takes time; (2) Learning is a social process; and (3) Learning about technology should be…

Can Learning Motivation Predict Learning Achievement? A Case Study of a Mobile Game-Based English Learning Approach

ERIC Educational Resources Information Center

Tsai, Chia-Hui; Cheng, Ching-Hsue; Yeh, Duen-Yian; Lin, Shih-Yun

2017-01-01

This study applied a quasi-experimental design to investigate the influence and predictive power of learner motivation for achievement, employing a mobile game-based English learning approach. A system called the Happy English Learning System, integrating learning material into a game-based context, was constructed and installed on mobile devices…

Toward Mobile Assisted Language Learning Apps for Professionals That Integrate Learning into the Daily Routine

ERIC Educational Resources Information Center

Pareja-Lora, Antonio; Arús-Hita, Jorge; Read, Timothy; Rodríguez-Arancón, Pilar; Calle-Martínez, Cristina; Pomposo, Lourdes; Martín-Monje, Elena; Bárcena, Elena

2013-01-01

In this short paper, we present some initial work on Mobile Assisted Language Learning (MALL) undertaken by the ATLAS research group. ATLAS embraced this multidisciplinary field cutting across Mobile Learning and Computer Assisted Language Learning (CALL) as a natural step in their quest to find learning formulas for professional English that…

Learning through inter- and intradisciplinary problem solving: using cognitive apprenticeship to analyse doctor-to-doctor consultation.

PubMed

Pimmer, Christoph; Pachler, Norbert; Nierle, Julia; Genewein, Urs

2012-12-01

Today's healthcare can be characterised by the increasing importance of specialisation that requires cooperation across disciplines and specialities. In view of the number of educational programmes for interdisciplinary cooperation, surprisingly little is known on how learning arises from interdisciplinary work. In order to analyse the learning and teaching practices of interdisciplinary cooperation, a multiple case study research focused on how consults, i.e., doctor-to-doctor consultations between medical doctors from different disciplines were carried out: semi-structured interviews with doctors of all levels of seniority from two hospital sites in Switzerland were conducted. Starting with a priori constructs based on the 'methods' underpinning cognitive apprenticeship (CA), the transcribed interviews were analysed according to the principles of qualitative content analysis. The research contributes to three debates: (1) socio-cognitive and situated learning, (2) intra- and interdisciplinary learning in clinical settings, and (3), more generally, to cooperation and problem solving. Patient cases, which necessitate the cooperation of doctors in consults across boundaries of clinical specialisms, trigger intra- as well as interdisciplinary learning and offer numerous and varied opportunities for learning by requesting doctors as well as for on-call doctors, in particular those in residence. The relevance of consults for learning can also be verified from the perspective of CA which is commonly used by experts, albeit in varying forms, degrees of frequency and quality, and valued by learners. Through data analysis a model for collaborative problem-solving and help-seeking was developed which shows the interplay of pedagogical 'methods' of CA in informal clinical learning contexts.

Exercise, character strengths, well-being, and learning climate in the prediction of performance over a 6-month period at a call center.

PubMed

Moradi, Saleh; Nima, Ali A; Rapp Ricciardi, Max; Archer, Trevor; Garcia, Danilo

2014-01-01

Performance monitoring might have an adverse influence on call center agents' well-being. We investigate how performance, over a 6-month period, is related to agents' perceptions of their learning climate, character strengths, well-being (subjective and psychological), and physical activity. Agents (N = 135) self-reported perception of the learning climate (Learning Climate Questionnaire), character strengths (Values In Action Inventory Short Version), well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version), and how often/intensively they engaged in physical activity. Performance, "time on the phone," was monitored for 6 consecutive months by the same system handling the calls. Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective) and Temperance (e.g., having self-control, being prudent, humble, and modest), and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation) and Justice (e.g., affiliation, fairness, leadership), positive affect, life satisfaction and exercise Intensity. Call centers may need to create opportunities to develop to increase agents' performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

Measuring learning potential in people with schizophrenia: A comparison of two tasks.

PubMed

Rempfer, Melisa V; McDowd, Joan M; Brown, Catana E

2017-12-01

Learning potential measures utilize dynamic assessment methods to capture performance changes following training on a cognitive task. Learning potential has been explored in schizophrenia research as a predictor of functional outcome and there have been calls for psychometric development in this area. Because the majority of learning potential studies have utilized the Wisconsin Card Sorting Test (WCST), we extended this work using a novel measure, the Rey Osterrieth Complex Figure Test (ROCFT). This study had the following aims: 1) to examine relationships among different learning potential indices for two dynamic assessment tasks, 2) to examine the association between WCST and ROCFT learning potential measures, and 3) to address concurrent validity with a performance-based measure of functioning (Test of Grocery Shopping Skills; TOGSS). Eighty-one adults with schizophrenia or schizoaffective disorder completed WCST and ROCFT learning measures and the TOGSS. Results indicated the various learning potential computational indices are intercorrelated and, similar to other studies, we found support for regression residuals and post-test scores as optimal indices. Further, we found modest relationships between the two learning potential measures and the TOGSS. These findings suggest learning potential includes both general and task-specific constructs but future research is needed to further explore this question. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Langan, Roisin T.; Archibald, Richard K.; Lamberti, Vincent

We have applied a new imputation-based method for analyzing incomplete data, called Monte Carlo Bayesian Database Generation (MCBDG), to the Spent Fuel Isotopic Composition (SFCOMPO) database. About 60% of the entries are absent for SFCOMPO. The method estimates missing values of a property from a probability distribution created from the existing data for the property, and then generates multiple instances of the completed database for training a machine learning algorithm. Uncertainty in the data is represented by an empirical or an assumed error distribution. The method makes few assumptions about the underlying data, and compares favorably against results obtained bymore » replacing missing information with constant values.« less

Lifeworthy Learning

ERIC Educational Resources Information Center

Perkins, David N.

2016-01-01

What learning really matters for today's learners? In this article, David N. Perkins promises not to provide the answer, but rather to consider how we might think about the question. Learning that matters--which he calls lifeworthy learning--is characterized by four earmarks: opportunity, insight, action, and ethics. Educators should ask…

On Entrepreneurial Education: Dilemmas and Tensions in Nonformal Learning

ERIC Educational Resources Information Center

Pantea, Maria-Carmen

2016-01-01

This paper revisits the current policy assumptions on youth entrepreneurship and their possible implications on entrepreneurial learning in nonformal settings. Based on secondary literature analysis, it interrogates the nonformal learning practices that promote entrepreneurship and calls for entrepreneurial learning to incorporate higher awareness…

Vocational Learning outside Institutions: Online Pedagogy and Deschooling.

ERIC Educational Resources Information Center

Whittington, Dave; McLean, Alan

2001-01-01

Using Illich's "Deschooling Society" as a framework, argues that online learning's flexibility and capacity to support dialogue will profoundly change vocational learning and challenge established institutions' dominance in vocational education and training. Calls for an inclusive approach involving informal learning and access for those…

Making Work and Learning More Visible by Reflective Practice

ERIC Educational Resources Information Center

Tikkamäki, Kati; Hilden, Sanna

2014-01-01

Several characteristics are necessary to have a flourishing workplace: one is organisational learning. Modern workplaces call for individual responsibility, ability, and willingness to share expertise, as well as continuous learning. However, critical elements of the process of organisational learning -- participating, knowing, cooperating and…

Intelligent Computer-Assisted Language Learning.

ERIC Educational Resources Information Center

Harrington, Michael

1996-01-01

Introduces the field of intelligent computer assisted language learning (ICALL) and relates them to current practice in computer assisted language learning (CALL) and second language learning. Points out that ICALL applies expertise from artificial intelligence and the computer and cognitive sciences to the development of language learning…

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Linear time relational prototype based learning.

PubMed

Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

2012-10-01

Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

Machine-learning in astronomy

NASA Astrophysics Data System (ADS)

Hobson, Michael; Graff, Philip; Feroz, Farhan; Lasenby, Anthony

2014-05-01

Machine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, called SkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. The SkyNet and BAMBI packages, which are fully parallelised using MPI, are available at http://www.mrao.cam.ac.uk/software/.

[Multilingualism and child psychiatry: on differential diagnoses of language disorder, specific learning disorder, and selective mutism].

PubMed

Tamiya, Satoshi

2014-01-01

Multilingualism poses unique psychiatric problems, especially in the field of child psychiatry. The author discusses several linguistic and transcultural issues in relation to Language Disorder, Specific Learning Disorder and Selective Mutism. Linguistic characteristics of multiple language development, including so-called profile effects and code-switching, need to be understood for differential diagnosis. It is also emphasized that Language Disorder in a bilingual person is not different or worse than that in a monolingual person. Second language proficiency, cultural background and transfer from the first language all need to be considered in an evaluation for Specific Learning Disorder. Selective Mutism has to be differentiated from the silent period observed in the normal successive bilingual development. The author concludes the review by remarking on some caveats around methods of language evaluation in a multilingual person.

Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

PubMed

Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

Guia para la Ensenanza Combinada de Ingles y Ciencia. Para Padres/sobre Padres. (A Guide to Teaching English and Science Together. For Parents/about Parents).

ERIC Educational Resources Information Center

Schwartz, Wendy

In the past, students who knew only a little English (called limited English proficient, or LEP), were usually taught only low-level science and mathematics. Now, new science and mathematics teaching methods can help LEP students get a good education in both fields. This guide will help parents know if their children are learning as much as…

Multi-Source Fusion for Explosive Hazard Detection in Forward Looking Sensors

DTIC Science & Technology

2016-12-01

include; (1) Investigating (a) thermal, (b) synthetic aperture acoustics ( SAA ) and (c) voxel space Radar for buried and side threat attacks. (2...detection. (3) With respect to SAA , we developed new approaches in the time and frequency domains for analyzing signature of concealed targets (called...Fraz). We also developed a method to extract a multi-spectral signature from SAA and deep learning was used on limited training and class imbalance

Improving Demonstration Using Better Interaction Techniques

DTIC Science & Technology

1997-01-14

Programming by demonstration (PBD) can be used to create tools and methods that eliminate the need to learn difficult computer languages. Gamut is a...do this, Gamut uses advanced interaction techniques that make it easier for a software author to express all needed aspects of one’s program. These...techniques include a simplified way to demonstrate new examples, called nudges, and a way to highlight objects to show they are important. Also, Gamut

Searching for exoplanets using artificial intelligence

NASA Astrophysics Data System (ADS)

Pearson, Kyle A.; Palafox, Leon; Griffith, Caitlin A.

2018-02-01

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

PubMed

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

PubMed Central

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

Confidential close call reporting system (C3RS) lessons learned team baseline phased report

DOT National Transportation Integrated Search

2015-05-08

The Federal Railroad Administration (FRA) has established a program called the Confidential Close Call Reporting System : (C3RS), which allows events to be reported anonymously and dealt with non-punitively and without fear or reprisal through : stru...

Confidential close call reporting system (C3RS) lessons learned team baseline phase report.

DOT National Transportation Integrated Search

2015-05-01

The Federal Railroad Administration (FRA) has established a program called the Confidential Close Call Reporting System : (C3 : RS), which allows events to be reported anonymously and dealt with non-punitively and without fear or reprisal through : s...

Sex differences in the representation of call stimuli in a songbird secondary auditory area

PubMed Central

Giret, Nicolas; Menardy, Fabien; Del Negro, Catherine

2015-01-01

Understanding how communication sounds are encoded in the central auditory system is critical to deciphering the neural bases of acoustic communication. Songbirds use learned or unlearned vocalizations in a variety of social interactions. They have telencephalic auditory areas specialized for processing natural sounds and considered as playing a critical role in the discrimination of behaviorally relevant vocal sounds. The zebra finch, a highly social songbird species, forms lifelong pair bonds. Only male zebra finches sing. However, both sexes produce the distance call when placed in visual isolation. This call is sexually dimorphic, is learned only in males and provides support for individual recognition in both sexes. Here, we assessed whether auditory processing of distance calls differs between paired males and females by recording spiking activity in a secondary auditory area, the caudolateral mesopallium (CLM), while presenting the distance calls of a variety of individuals, including the bird itself, the mate, familiar and unfamiliar males and females. In males, the CLM is potentially involved in auditory feedback processing important for vocal learning. Based on both the analyses of spike rates and temporal aspects of discharges, our results clearly indicate that call-evoked responses of CLM neurons are sexually dimorphic, being stronger, lasting longer, and conveying more information about calls in males than in females. In addition, how auditory responses vary among call types differ between sexes. In females, response strength differs between familiar male and female calls. In males, temporal features of responses reveal a sensitivity to the bird's own call. These findings provide evidence that sexual dimorphism occurs in higher-order processing areas within the auditory system. They suggest a sexual dimorphism in the function of the CLM, contributing to transmit information about the self-generated calls in males and to storage of information about the bird's auditory experience in females. PMID:26578918

Neural Correlates of Threat Perception: Neural Equivalence of Conspecific and Heterospecific Mobbing Calls Is Learned

PubMed Central

Avey, Marc T.; Hoeschele, Marisa; Moscicki, Michele K.; Bloomfield, Laurie L.; Sturdy, Christopher B.

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise [1]–[2]. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators [3]. Mobbing calls produced in response to smaller, higher-threat predators contain more “D” notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators [4]. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned. PMID:21909363

Sex differences in the representation of call stimuli in a songbird secondary auditory area.

PubMed

Giret, Nicolas; Menardy, Fabien; Del Negro, Catherine

2015-01-01

Understanding how communication sounds are encoded in the central auditory system is critical to deciphering the neural bases of acoustic communication. Songbirds use learned or unlearned vocalizations in a variety of social interactions. They have telencephalic auditory areas specialized for processing natural sounds and considered as playing a critical role in the discrimination of behaviorally relevant vocal sounds. The zebra finch, a highly social songbird species, forms lifelong pair bonds. Only male zebra finches sing. However, both sexes produce the distance call when placed in visual isolation. This call is sexually dimorphic, is learned only in males and provides support for individual recognition in both sexes. Here, we assessed whether auditory processing of distance calls differs between paired males and females by recording spiking activity in a secondary auditory area, the caudolateral mesopallium (CLM), while presenting the distance calls of a variety of individuals, including the bird itself, the mate, familiar and unfamiliar males and females. In males, the CLM is potentially involved in auditory feedback processing important for vocal learning. Based on both the analyses of spike rates and temporal aspects of discharges, our results clearly indicate that call-evoked responses of CLM neurons are sexually dimorphic, being stronger, lasting longer, and conveying more information about calls in males than in females. In addition, how auditory responses vary among call types differ between sexes. In females, response strength differs between familiar male and female calls. In males, temporal features of responses reveal a sensitivity to the bird's own call. These findings provide evidence that sexual dimorphism occurs in higher-order processing areas within the auditory system. They suggest a sexual dimorphism in the function of the CLM, contributing to transmit information about the self-generated calls in males and to storage of information about the bird's auditory experience in females.

Student Approaches to Learning and Studying. Research Monograph.

ERIC Educational Resources Information Center

Biggs, John B.

A common thread in contemporary research in student learning refers to the ways in which students go about learning. A theory of learning is presented that accentuates the interaction between the person and the situation. Research evidence implies a form of meta-cognition called meta-learning, the awareness of students of their own learning…

Self-Directed Lifelong Learning in Hybrid Learning Configurations

ERIC Educational Resources Information Center

Cremers, Petra H. M.; Wals, Arjen E. J.; Wesselink, Renate; Nieveen, Nienke; Mulder, Martin

2014-01-01

Present-day students are expected to be lifelong learners throughout their working life. Higher education must therefore prepare students to self-direct their learning beyond formal education, in real-life working settings. This can be achieved in so-called hybrid learning configurations in which working and learning are integrated. In such a…

Learning Initiatives in the Residential Setting. The First-Year Experience Monograph Series No. 48

ERIC Educational Resources Information Center

Luna, Gene, Ed.; Gahagan, Jimmie, Ed.

2008-01-01

In 2004, "Learning Reconsidered" urged educators to think more holistically about student learning and development. "Learning Initiatives in the Residential Setting" provides a framework for putting this call into action at large universities and small colleges alike. Chapters trace the history of learning in residence halls, discuss academic and…

Relationships among Learning Styles and Motivation with Computer-Aided Instruction in an Agronomy Course

ERIC Educational Resources Information Center

McAndrews, Gina M.; Mullen, Russell E.; Chadwick, Scott A.

2005-01-01

Multi-media learning tools were developed to enhance student learning for an introductory agronomy course at Iowa State University. During fall 2002, the new interactive computer program, called Computer Interactive Multimedia Program for Learning Enhancement (CIMPLE) was incorporated into the teaching, learning, and assessment processes of the…

Computer Assisted Language Learning. Routledge Studies in Computer Assisted Language Learning

ERIC Educational Resources Information Center

Pennington, Martha

2011-01-01

Computer-assisted language learning (CALL) is an approach to language teaching and learning in which computer technology is used as an aid to the presentation, reinforcement and assessment of material to be learned, usually including a substantial interactive element. This books provides an up-to date and comprehensive overview of…

Where's the Learning in Higher Learning?

ERIC Educational Resources Information Center

Keeling, Richard P.; Hersh, Richard H.

2012-01-01

While cost and completion are important issues, they are not the fundamental problems that have put higher learning in crisis. What calls for urgent attention is low "value"--a critical deficit in the quality and quantity of learning in college. To state it as plainly as possible: Most students graduate without learning enough. There is no longer…

Software Application for Computer Aided Vocabulary Learning in a Blended Learning Environment

ERIC Educational Resources Information Center

Essam, Rasha

2010-01-01

This study focuses on the effect of computer-aided vocabulary learning software called "ArabCAVL" on students' vocabulary acquisition. It was hypothesized that students who use the ArabCAVL software in blended learning environment will surpass students who use traditional vocabulary learning strategies in face-to-face learning…

Teaching Strategies to Promote Concept Learning by Design Challenges

ERIC Educational Resources Information Center

Van Breukelen, Dave; Van Meel, Adrianus; De Vries, Marc

2017-01-01

Background: This study is the second study of a design-based research, organised around four studies, that aims to improve student learning, teaching skills and teacher training concerning the design-based learning approach called Learning by Design (LBD). Purpose: LBD uses the context of design challenges to learn, among other things, science.…

Cognitive Anatomy of Tutor Learning: Lessons Learned with SimStudent

ERIC Educational Resources Information Center

Matsuda, Noboru; Yarzebinski, Evelyn; Keiser, Victoria; Raizada, Rohan; Cohen, William W.; Stylianides, Gabriel J.; Koedinger, Kenneth R.

2013-01-01

This article describes an advanced learning technology used to investigate hypotheses about learning by teaching. The proposed technology is an instance of a teachable agent, called SimStudent, that learns skills (e.g., for solving linear equations) from examples and from feedback on performance. SimStudent has been integrated into an online,…

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering.

PubMed

Peng, Xi; Yu, Zhiding; Yi, Zhang; Tang, Huajin

2017-04-01

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that l 1 -, l 2 -, l ∞ -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.

Sampling algorithms for validation of supervised learning models for Ising-like systems

NASA Astrophysics Data System (ADS)

Portman, Nataliya; Tamblyn, Isaac

2017-12-01

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

Preparation for frontline end-of-life care: exploring the perspectives of paramedics and emergency medical technicians.

PubMed

Waldrop, Deborah P; Clemency, Brian; Maguin, Eugene; Lindstrom, Heather

2014-03-01

Prehospital emergency providers (emergency medical technicians [EMTs] and paramedics) who respond to emergency calls for patients near the end of life (EOL) make critical decisions in the field about initiating care and transport to an emergency department. To identify how a sample of prehospital providers learned about EOL care, their perceived confidence with and perspectives on improved preparation for such calls. This descriptive study used a cross-sectional survey design with mixed methods. One hundred seventy-eight prehospital providers (76 EMT-basics and 102 paramedics) from an emergency medical services agency participated. Multiple choice and open-ended survey questions addressed how they learned about EOL calls, their confidence with advance directives, and perspectives on improving care in the field. The response rate was 86%. Education about do-not-resuscitate (DNR) orders was formal (92%), experiential (77%), and self-directed (38%). Education about medical orders for life-sustaining treatment (MOLST) was formal (72%), experiential (67%), and self-directed (25%). Ninety-three percent were confident in upholding a DNR order, 87% were confident interpreting MOLST, and 87% were confident sorting out conflict between differing patient and family wishes. Qualitative data analysis yielded six themes on improving preparation of prehospital providers for EOL calls: (1) prehospital provider education; (2) public education; (3) educating health care providers on scope of practice; (4) conflict resolution skills; (5) handling emotional families; and (6) clarification of transfer protocols. These study results suggest the need for addressing the potential interrelationship between prehospital and EOL care through improved education and protocols for care in the field.

[Organization development of the public health system].

PubMed

Pfaff, Holger; Klein, Jürgen

2002-05-15

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

Evolutionary learning processes as the foundation for behaviour change.

PubMed

Crutzen, Rik; Peters, Gjalt-Jorn Ygram

2018-03-01

We argue that the active ingredients of behaviour change interventions, often called behaviour change methods (BCMs) or techniques (BCTs), can usefully be placed on a dimension of psychological aggregation. We introduce evolutionary learning processes (ELPs) as fundamental building blocks that are on a lower level of psychological aggregation than BCMs/BCTs. A better understanding of ELPs is useful to select the appropriate BCMs/BCTs to target determinants of behaviour, or vice versa, to identify potential determinants targeted by a given BCM/BCT, and to optimally translate them into practical applications. Using these insights during intervention development may increase the likelihood of developing effective interventions - both in terms of behaviour change as well as maintenance of behaviour change.

Digital imaging biomarkers feed machine learning for melanoma screening.

PubMed

Gareau, Daniel S; Correa da Rosa, Joel; Yagerman, Sarah; Carucci, John A; Gulati, Nicholas; Hueto, Ferran; DeFazio, Jennifer L; Suárez-Fariñas, Mayte; Marghoob, Ashfaq; Krueger, James G

2017-07-01

We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q-score. These methods were applied to a set of 120 "difficult" dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified. This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation. Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions. © 2016 The Authors. Experimental Dermatology Published by John Wiley & Sons Ltd.

Transductive multi-view zero-shot learning.

PubMed

Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

2015-11-01

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.

Ten simple rules for drawing scientific comics

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

McDermott, Jason E.; Partridge, Matthew; Bromberg, Yana

Institutions around the world are fighting to improve science communication all the time. From calls for journal papers to be simplified to encouraging scientists to take more of an active role through community engagement, there is an impetus to demystify and improve public understanding and engagement with science. Technology has greatly helped expand the range of learning styles that a lecturer can call on to reach people in new ways. Social media outlets like Twitter, Facebook, Instagram, and Tumblr have expanded the reach of science communication within and across scientific disciplines and to the lay public. Here, with all themore » videos, interactive quizzes, and instant feedback it can be easy to overlook one of the simplest methods for communicating complex ideas: comics.« less

Ten simple rules for drawing scientific comics

DOE PAGES

McDermott, Jason E.; Partridge, Matthew; Bromberg, Yana; ...

2018-01-04

Institutions around the world are fighting to improve science communication all the time. From calls for journal papers to be simplified to encouraging scientists to take more of an active role through community engagement, there is an impetus to demystify and improve public understanding and engagement with science. Technology has greatly helped expand the range of learning styles that a lecturer can call on to reach people in new ways. Social media outlets like Twitter, Facebook, Instagram, and Tumblr have expanded the reach of science communication within and across scientific disciplines and to the lay public. Here, with all themore » videos, interactive quizzes, and instant feedback it can be easy to overlook one of the simplest methods for communicating complex ideas: comics.« less

Web 2.0 in Computer-Assisted Language Learning: A Research Synthesis and Implications for Instructional Design and Educational Practice

ERIC Educational Resources Information Center

Parmaxi, Antigoni; Zaphiris, Panayiotis

2017-01-01

This study explores the research development pertaining to the use of Web 2.0 technologies in the field of Computer-Assisted Language Learning (CALL). Published research manuscripts related to the use of Web 2.0 tools in CALL have been explored, and the following research foci have been determined: (1) Web 2.0 tools that dominate second/foreign…

Materials Development in Three Italian CALL Projects: Seeking an Optimal Mix between In-Class and Out-of-Class Learning

ERIC Educational Resources Information Center

Levy, Mike; Kennedy, Claire

2010-01-01

This paper considers the design and development of CALL materials with the aim of achieving an optimal mix between in-class and out-of-class learning in the context of teaching Italian at an Australian university. The authors discuss three projects in relation to the following themes: (a) conceptions of the in-class/out-of-class relationship, (b)…

Automating Rule Strengths in Expert Systems.

DTIC Science & Technology

1987-05-01

systems were designed in an incremental, iterative way. One of the most easily identifiable phases in this process, sometimes called tuning, consists...attenuators. The designer of the knowledge-based system must determine (synthesize) or adjust (xfine, if estimates of the values are given) these...values. We consider two ways in which the designer can learn the values. We call the first model of learning the complete case and the second model the

Myths about Technology-Supported Professional Learning

ERIC Educational Resources Information Center

Killion, Joellen; Treacy, Barbara

2014-01-01

The future of professional learning is shaped by its present and past. As new technologies emerge to increase affordability, access, and appropriateness of professional learning, three beliefs are visible in current practices related to online learning. Each contains a premise that merits identification and examination. The authors call these…

Improving Organizational Learning through Leadership Training

ERIC Educational Resources Information Center

Hasson, Henna; von Thiele Schwarz, Ulrica; Holmstrom, Stefan; Karanika-Murray, Maria; Tafvelin, Susanne

2016-01-01

Purpose: This paper aims to evaluate whether training of managers at workplaces can improve organizational learning. Managers play a crucial role in providing opportunities to employees for learning. Although scholars have called for intervention research on the effects of leadership development on organizational learning, no such research is…

Agile Learning: Sprinting through the Semester

ERIC Educational Resources Information Center

Lang, Guido

2017-01-01

This paper introduces agile learning, a novel pedagogical approach that applies the processes and principles of agile software development to the context of learning. Agile learning is characterized by short project cycles, called sprints, in which a usable deliverable is fully planned, designed, built, tested, reviewed, and launched. An…

Bottlenose dolphins can use learned vocal labels to address each other

PubMed Central

King, Stephanie L.; Janik, Vincent M.

2013-01-01

In animal communication research, vocal labeling refers to incidents in which an animal consistently uses a specific acoustic signal when presented with a specific object or class of objects. Labeling with learned signals is a foundation of human language but is notably rare in nonhuman communication systems. In natural animal systems, labeling often occurs with signals that are not influenced by learning, such as in alarm and food calling. There is a suggestion, however, that some species use learned signals to label conspecific individuals in their own communication system when mimicking individually distinctive calls. Bottlenose dolphins (Tursiops truncatus) are a promising animal for exploration in this area because they are capable of vocal production learning and can learn to use arbitrary signals to report the presence or absence of objects. Bottlenose dolphins develop their own unique identity signal, the signature whistle. This whistle encodes individual identity independently of voice features. The copying of signature whistles may therefore allow animals to label or address one another. Here, we show that wild bottlenose dolphins respond to hearing a copy of their own signature whistle by calling back. Animals did not respond to whistles that were not their own signature. This study provides compelling evidence that a dolphin’s learned identity signal is used as a label when addressing conspecifics. Bottlenose dolphins therefore appear to be unique as nonhuman mammals to use learned signals as individually specific labels for different social companions in their own natural communication system. PMID:23878217

Consensus-based distributed cooperative learning from closed-loop neural control systems.

PubMed

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

Learning Styles and Individual Differences in Learning English Idioms via Computer Assisted Language Learning in English as a Second Language.

ERIC Educational Resources Information Center

Viteli, Jarmo

The purpose of this study was to determine the learning styles of English-as-a-Second-Language (ESL) students and individual differences in learning English idioms via computer assisted language learning (CALL). Thirty-six Hispanic students, 26 Japanese students, and 6 students with various language backgrounds from the Nova University Intensive…

Proof of Learning Outcome by the Advanced Clinical Competency Examination Trial after the Long-term Student's Practice in Pharmaceutical Education.

PubMed

Komori, Koji; Kataoka, Makoto; Kuramoto, Nobuyuki; Tsuji, Takumi; Nakatani, Takafumi; Yasuhara, Tomohisa; Mitamura, Shinobu; Hane, Yumiko; Ogita, Kiyokazu

2016-01-01

At Setsunan University, a debrief session (a poster session) is commonly performed by the students who have completed the long-term students' practice. Since the valuable changes in practical competency of the students cannot be evaluated through this session, we specified items that can help evaluate and methods that can help estimate the students' competency as clinical pharmacists. We subsequently carried out a trial called the "Advanced Clinical Competency Examination". We evaluated 103 students who had concluded the students' practice for the second period (Sep 1, 2014, to Nov 16, 2014): 70 students (called "All finish students") who had completed the practice in a hospital and pharmacy, and 33 students (called "Hospital finish students") who had finished the practice at a hospital only. The trial was executed in four stages. In the first stage, students drew pictures of something impressive they had learned during the practice. In the second stage, students were given patient cases and were asked, "What is this patient's problem?" and "How would you solve this problem?". In the third stage, the students discussed their answers in a group. In the fourth stage, each group made a poster presentation in separate rooms. By using a rubric, the teachers evaluated each student individually, the results of which showed that the "All finish students" could identify more problems than the "Hospital finish students".

From TPACK-in-Action Workshops to English Classrooms: CALL Competencies Developed and Adopted into Classroom Teaching

ERIC Educational Resources Information Center

Tai, Shu-Ju

2013-01-01

As researchers in the CALL teacher education field noted, teachers play the pivotal role in the language learning classrooms because they are the gate keepers who decide whether technology or CALL has a place in their teaching, and they select technology to support their teaching, which determines what CALL activities language learners are exposed…

A Review of Technology Choice for Teaching Language Skills and Areas in the CALL Literature

ERIC Educational Resources Information Center

Stockwell, Glenn

2007-01-01

The use of technology in language teaching and learning has been the focus of a number of recent research review studies, including developments in technology and CALL research (Zhao, 2003), CALL as an academic discipline (Debski, 2003), ICT effectiveness (Felix, 2005), and subject characteristics in CALL research (Hubbard, 2005), to name a few.…

Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies

PubMed Central

Liu, Ying; ZENG, Donglin; WANG, Yuanjia

2014-01-01

Summary Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient’s time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual’s response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data. PMID:25642116

An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

PubMed

Yu, Hualong; Ni, Jun

2014-01-01

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

Learning while having fun: the use of video gaming to teach geriatric house calls to medical students.

PubMed

Duque, Gustavo; Fung, Shek; Mallet, Louise; Posel, Nancy; Fleiszer, David

2008-07-01

Although most health professionals perform home visits, there is not a structured method for performing them. In addition, in-training health professionals' exposure to home visits is limited for logistical reasons. A new method for medical students to learn how to perform an effective home visit was developed using an instructional video game. It was expected that students would learn the principles of a home visit using a video game while identifying the usefulness of video gaming (edutainment) in geriatrics education. A video game was created simulating a patient's house that the students were able to explore. Students played against time and distracters while being expected to click on those elements that they considered to be risk factors for falls or harmful for the patient. At the end of the game, the students received feedback on the chosen elements that were right or wrong. Finally, evaluation of the tool was obtained using pre- and posttests and pre- and postexposure feedback surveys. Fifty-six fourth-year medical students used the video game and completed the tests and the feedback surveys. This method showed a high level of engagement that is associated with improvement in knowledge. Additionally, users' feedback indicated that it was an innovative approach to the teaching of health sciences. In summary, this method provides medical students with a fun and structured experience that has an effect not only on their learning, but also on their understanding of the particular needs of the elderly population.

ASPECT: A Survey to Assess Student Perspective of Engagement in an Active-Learning Classroom

PubMed Central

Wiggins, Benjamin L.; Eddy, Sarah L.; Wener-Fligner, Leah; Freisem, Karen; Grunspan, Daniel Z.; Theobald, Elli J.; Timbrook, Jerry; Crowe, Alison J.

2017-01-01

The primary measure used to determine relative effectiveness of in-class activities has been student performance on pre/posttests. However, in today’s active-learning classrooms, learning is a social activity, requiring students to interact and learn from their peers. To develop effective active-learning exercises that engage students, it is important to gain a more holistic view of the student experience in an active-learning classroom. We have taken a mixed-methods approach to iteratively develop and validate a 16-item survey to measure multiple facets of the student experience during active-learning exercises. The instrument, which we call Assessing Student Perspective of Engagement in Class Tool (ASPECT), was administered to a large introductory biology class, and student responses were subjected to exploratory factor analysis. The 16 items loaded onto three factors that cumulatively explained 52% of the variation in student response: 1) value of activity, 2) personal effort, and 3) instructor contribution. ASPECT provides a rapid, easily administered means to measure student perception of engagement in an active-learning classroom. Gaining a better understanding of students’ level of engagement will help inform instructor best practices and provide an additional measure for comprehensively assessing the impact of different active-learning strategies. PMID:28495936

Online learning in optical tomography: a stochastic approach

NASA Astrophysics Data System (ADS)

Chen, Ke; Li, Qin; Liu, Jian-Guo

2018-07-01

We study the inverse problem of radiative transfer equation (RTE) using stochastic gradient descent method (SGD) in this paper. Mathematically, optical tomography amounts to recovering the optical parameters in RTE using the incoming–outgoing pair of light intensity. We formulate it as a PDE-constraint optimization problem, where the mismatch of computed and measured outgoing data is minimized with same initial data and RTE constraint. The memory and computation cost it requires, however, is typically prohibitive, especially in high dimensional space. Smart iterative solvers that only use partial information in each step is called for thereafter. Stochastic gradient descent method is an online learning algorithm that randomly selects data for minimizing the mismatch. It requires minimum memory and computation, and advances fast, therefore perfectly serves the purpose. In this paper we formulate the problem, in both nonlinear and its linearized setting, apply SGD algorithm and analyze the convergence performance.

A better communcation with the patients improves the management of HIV disease: a nonsystematic review

PubMed Central

TZANEVA, VALENTINA; IACOB, TEODORA

2013-01-01

The human immunodeficiency virus (HIV) is a blood-borne, sexually transmissible virus which belongs to a subset of viruses called retroviruses. Patients with HIV disease face problems like stigma, discrimination, poverty and marginalization. These problems also affect the physician-patient communication in HIV disease. Learning to conduct a consultation is a complex skill which is gradually learned and perfected during training and career. Good physician-patient communication in HIV disease demands medical professional competence, good communication skills, ethical behaviour, respect of patient’s dignity, good teamwork skills and maintaining confidentiality. The most important aspect of patient care is education, which should include empowering patients with basic knowledge about HIV infection, methods of transmission, progression, prognosis, and prevention. A multidisciplinary approach that uses the special skills of nurses, pharmacists, nutritionists, social workers, and case managers is desirable. Effective methods for clinicians to support such development are needed. PMID:26527943

Selective habituation shapes acoustic predator recognition in harbour seals.

PubMed

Deecke, Volker B; Slater, Peter J B; Ford, John K B

2002-11-14

Predation is a major force in shaping the behaviour of animals, so that precise identification of predators will confer substantial selective advantages on animals that serve as food to others. Because experience with a predator can be lethal, early researchers studying birds suggested that predator recognition does not require learning. However, a predator image that can be modified by learning and experience will be advantageous in situations where cues associated with the predator are highly variable or change over time. In this study, we investigated the response of harbour seals (Phoca vitulina) to the underwater calls of different populations of killer whales (Orcinus orca). We found that the seals responded strongly to the calls of mammal-eating killer whales and unfamiliar fish-eating killer whales but not to the familiar calls of the local fish-eating population. This demonstrates that wild harbour seals are capable of complex acoustic discrimination and that they modify their predator image by selectively habituating to the calls of harmless killer whales. Fear in these animals is therefore focused on local threats by learning and experience.

Deep Learning through Concept-Based Inquiry

ERIC Educational Resources Information Center

Donham, Jean

2010-01-01

Learning in the library should present opportunities to enrich student learning activities to address concerns of interest and cognitive complexity, but these must be tasks that call for in-depth analysis--not merely gathering facts. Library learning experiences need to demand enough of students to keep them interested and also need to be…

Education for a Learning Society.

ERIC Educational Resources Information Center

Tempero, Howard E., Ed.

The essays contained in this booklet are 1) "Education for a 'Learning Society': The Challenge" by Ernest Bayles in which he calls for focus on learning to live, developing skills of reflection and judgment applicable to vital issues, and reflective teaching; 2) "Teacher Education in a Learning Society" in which David Turney demands teacher…

Educational States of Suspension

ERIC Educational Resources Information Center

Lewis, Tyson E.; Friedrich, Daniel

2016-01-01

In response to the growing emphasis on learning outcomes, life-long learning, and what could be called the learning society, scholars are turning to alternative educational logics that problematize the reduction of education to learning. In this article, we draw on these critics but also extend their thinking in two ways. First, we use Giorgio…

Exploring Cloud Computing for Distance Learning

ERIC Educational Resources Information Center

He, Wu; Cernusca, Dan; Abdous, M'hammed

2011-01-01

The use of distance courses in learning is growing exponentially. To better support faculty and students for teaching and learning, distance learning programs need to constantly innovate and optimize their IT infrastructures. The new IT paradigm called "cloud computing" has the potential to transform the way that IT resources are utilized and…

Discovery and Use of Online Learning Resources: Case Study Findings

ERIC Educational Resources Information Center

Recker, Mimi M.; Dorward, James; Nelson, Laurie Miller

2004-01-01

Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called "learning objects." Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article…

Self-Regulated Out-of-Class Language Learning with Technology

ERIC Educational Resources Information Center

Lai, Chun; Gu, Mingyue

2011-01-01

Current computer-assisted language learning (CALL) research has identified various potentials of technology for language learning. To realize and maximize these potentials, engaging students in self-initiated use of technology for language learning is a must. This study investigated Hong Kong university students' use of technology outside the…

The Teacher as Designer: Pedagogy in the New Media Age

ERIC Educational Resources Information Center

Kalantzis, Mary; Cope, Bill

2010-01-01

This article outlines a learning intervention which the authors call Learning by Design. The goal of this intervention is classroom and curriculum transformation, and the professional learning of teachers. The experiment involves the practical application of the learning theory to everyday classroom practice. Its ideas are grounded in pedagogical…

Designing Personalized Learning Products for Middle School Mathematics: The Case for Networked Learning Games

ERIC Educational Resources Information Center

Evans, Michael A.; Pruett, Jordan; Chang, Mido; Nino, Miguel

2014-01-01

Middle school mathematics education is subject to ongoing reform based on advances in digital instructional technologies, especially learning games, leading to recent calls for investment in "personalized learning." Through an extensive literature review, this investigation identified three priority areas that should be taken into…

Performance, Cognitive Load, and Behaviour of Technology-Assisted English Listening Learning: From CALL to MALL

ERIC Educational Resources Information Center

Chang, Chi-Cheng; Warden, Clyde A.; Liang, Chaoyun; Chou, Pao-Nan

2018-01-01

This study examines differences in English listening comprehension, cognitive load, and learning behaviour between outdoor ubiquitous learning and indoor computer-assisted learning. An experimental design, employing a pretest-posttest control group is employed. Randomly assigned foreign language university majors joined either the experimental…

Impact of a Blended Environment with m-Learning on EFL Skills

ERIC Educational Resources Information Center

Obari, Hiroyuki; Lambacher, Stephen

2014-01-01

A longitudinal study conducted from April 2013 to January 2014 sought to ascertain whether a blended learning (BL) environment incorporating m-learning could help Japanese undergraduates improve their English language skills. In this paper, various emerging technologies (including Globalvoice English, ATR CALL Brix, the mobile learning-oriented…

Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Tsai, Chin-Chung; Yang, Stephen J. H.

2008-01-01

Recent progress in wireless and sensor technologies has lead to a new development of learning environments, called context-aware ubiquitous learning environment, which is able to sense the situation of learners and provide adaptive supports. Many researchers have been investigating the development of such new learning environments; nevertheless,…

Mood Moderates the Effect of Self-Generation during Learning

ERIC Educational Resources Information Center

Schindler, Julia; Richter, Tobias; Eyßer, Carolin

2017-01-01

Generating information, compared to reading, improves learning and enhances long-term retention of the learned content. This so-called generation effect has been demonstrated repeatedly for recall and recognition of single words. However, before adopting generating as a learning strategy in educational contexts, conditions moderating the effect…

Action Learning: Towards a Framework in Inter-Organisational Settings

ERIC Educational Resources Information Center

Coughlan, Paul; Coghlan, David

2004-01-01

While much of the literature on action learning focuses on managers developing their capacity to learn and transform their own organizations, this article explores how action learning has been used in inter-organisational settings. Two settings are presented: the first an EU-funded management development programme called the National Action…

Agent-based traffic management and reinforcement learning in congested intersection network.

DOT National Transportation Integrated Search

2012-08-01

This study evaluates the performance of traffic control systems based on reinforcement learning (RL), also called approximate dynamic programming (ADP). Two algorithms have been selected for testing: 1) Q-learning and 2) approximate dynamic programmi...

Learning Theory and Prosocial Behavior

ERIC Educational Resources Information Center

Rosenhan, D. L.

1972-01-01

Although theories of learning which stress the role of reinforcement can help us understand altruistic behaviors, it seems clear that a more complete comprehension calls for an expansion of our notions of learning, such that they incorporate affect and cognition. (Author/JM)

Fuzzy self-learning control for magnetic servo system

NASA Technical Reports Server (NTRS)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Reconstructing spatial organizations of chromosomes through manifold learning

PubMed Central

Deng, Wenxuan; Hu, Hailin; Ma, Rui; Zhang, Sai; Yang, Jinglin; Peng, Jian; Kaplan, Tommy; Zeng, Jianyang

2018-01-01

Abstract Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data. PMID:29408992

Teaching adaptive leadership to family medicine residents: what? why? how?

PubMed

Eubank, Daniel; Geffken, Dominic; Orzano, John; Ricci, Rocco

2012-09-01

Health care reform calls for patient-centered medical homes built around whole person care and healing relationships. Efforts to transform primary care practices and deliver these qualities have been challenging. This study describes one Family Medicine residency's efforts to develop an adaptive leadership curriculum and use coaching as a teaching method to address this challenge. We review literature that describes a parallel between the skills underlying such care and those required for adaptive leadership. We address two questions: What is leadership? Why focus on adaptive leadership? We then present a synthesis of leadership theories as a set of process skills that lead to organization learning through effective work relationships and adaptive leadership. Four models of the learning process needed to acquire such skills are explored. Coaching is proposed as a teaching method useful for going beyond information transfer to create the experiential learning necessary to acquire the process skills. Evaluations of our efforts to date are summarized. We discuss key challenges to implementing such a curriculum and propose that teaching adaptive leadership is feasible but difficult in the current medical education and practice contexts.

Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

PubMed

Witoonchart, Peerajak; Chongstitvatana, Prabhas

2017-08-01

In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.