learning method based: Topics by Science.gov

Sample records for learning method based

Comparing team-based and mixed active-learning methods in an ambulatory care elective course.

PubMed

Zingone, Michelle M; Franks, Andrea S; Guirguis, Alexander B; George, Christa M; Howard-Thompson, Amanda; Heidel, Robert E

2010-11-10

To assess students' performance and perceptions of team-based and mixed active-learning methods in 2 ambulatory care elective courses, and to describe faculty members' perceptions of team-based learning. Using the 2 teaching methods, students' grades were compared. Students' perceptions were assessed through 2 anonymous course evaluation instruments. Faculty members who taught courses using the team-based learning method were surveyed regarding their impressions of team-based learning. The ambulatory care course was offered to 64 students using team-based learning (n = 37) and mixed active learning (n = 27) formats. The mean quality points earned were 3.7 (team-based learning) and 3.3 (mixed active learning), p < 0.001. Course evaluations for both courses were favorable. All faculty members who used the team-based learning method reported that they would consider using team-based learning in another course. Students were satisfied with both teaching methods; however, student grades were significantly higher in the team-based learning course. Faculty members recognized team-based learning as an effective teaching strategy for small-group active learning.
37: COMPARISON OF TWO METHODS: TBL-BASED AND LECTURE-BASED LEARNING IN NURSING CARE OF PATIENTS WITH DIABETES IN NURSING STUDENTS

PubMed Central

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

2017-01-01

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

NASA Astrophysics Data System (ADS)

Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

2018-02-01

Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.
A comparison of conventional lecture and team-based learning methods in terms of student learning and teaching satisfaction

PubMed Central

Jafari, Zahra

2014-01-01

Background: Team-based learning (TBL) is a structured type of cooperative learning that has growing application in medical education. This study compares levels of student learning and teaching satisfaction for a neurology course between conventional lecture and team-based learning. Methods: The study incorporated 70 students aged 19 to 22 years at the school of rehabilitation. One half of the 16 sessions of the neurology course was taught by lectures and the second half with team-based learning. Teaching satisfaction for the teaching methods was determined on a scale with 5 options in response to 20 questions. Results: Significant difference was found between lecture-based and team-based learning in final scores (p<0.001). Content validity index of the scale of student satisfaction was 94%, and external and internal consistencies of the scale were 0.954 and 0.921 orderly (p<0.001). The degree of satisfaction from TBL compared to the lecture method was 81.3%. Conclusion: Results revealed more success and student satisfaction from team-based learning compared to conventional lectures in teaching neurology to undergraduate students. It seems that application of new teaching methods such as team-based learning could be effectively introduced to improve levels of education and student learning PMID:25250250
Examining the Effectiveness of Problem-Based Learning in the Teaching of Information Technology: A Comparison with Lectured-Based Learning

ERIC Educational Resources Information Center

Liu, YuFing

2013-01-01

This paper applies a quasi-experimental research method to compare the difference in students' approaches to learning and their learning achievements between the group that follows the problem based learning (PBL) teaching method with computer support and the group that follows the non-PBL teaching methods. The study sample consisted of 68 junior…
Effectiveness of E-Learning for Students Vocational High School Building Engineering Program

NASA Astrophysics Data System (ADS)

Soeparno; Muslim, Supari

2018-04-01

Implementation of vocational learning in accordance with the 2013 curriculum must meet the criteria, one of which is learning to be consistent with advances in technology and information. Technology-based learning in vocational commonly referred to as E-Learning, online (in the network) and WBL (Web-Based Learning). Facts on the ground indicate that based learning technology and information on Vocational High School of Building Engineering is still not going well. The purpose of this research is to know: advantages and disadvantages of learning with E-Learning, conformity of learning with E-Learning with characteristics of students on Vocational High School of Building Engineering and effective learning method based on E-Learning for students on Vocational High School of Building Engineering. Research done by literature method, get the following conclusion as follow: the advantages of E-Learning is learning can be done anywhere and anytime, efficient in accessing materials and tasks, ease of communication and discussion; while the shortage is the need for additional costs for good internet access and lack of social interaction between teachers and students. E-learning is appropriate to basic knowledge competencies, and not appropriate at the level of advanced competencies and skills. Effective E-Learning Based Learning Method on Vocational High School of Building Engineering is a Blended method that is a mix between conventional method and e-learning.
Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

PubMed Central

Xu, Junyi; Yao, Li; Li, Le

2015-01-01

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. PMID:25966359
Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.
Learning linear transformations between counting-based and prediction-based word embeddings

PubMed Central

Hayashi, Kohei; Kawarabayashi, Ken-ichi

2017-01-01

Despite the growing interest in prediction-based word embedding learning methods, it remains unclear as to how the vector spaces learnt by the prediction-based methods differ from that of the counting-based methods, or whether one can be transformed into the other. To study the relationship between counting-based and prediction-based embeddings, we propose a method for learning a linear transformation between two given sets of word embeddings. Our proposal contributes to the word embedding learning research in three ways: (a) we propose an efficient method to learn a linear transformation between two sets of word embeddings, (b) using the transformation learnt in (a), we empirically show that it is possible to predict distributed word embeddings for novel unseen words, and (c) empirically it is possible to linearly transform counting-based embeddings to prediction-based embeddings, for frequent words, different POS categories, and varying degrees of ambiguities. PMID:28926629
A comparison of conventional lecture and team-based learning methods in terms of student learning and teaching satisfaction.

PubMed

Jafari, Zahra

2014-01-01

Team-based learning (TBL) is a structured type of cooperative learning that has growing application in medical education. This study compares levels of student learning and teaching satisfaction for a neurology course between conventional lecture and team-based learning. The study incorporated 70 students aged 19 to 22 years at the school of rehabilitation. One half of the 16 sessions of the neurology course was taught by lectures and the second half with team-based learning. Teaching satisfaction for the teaching methods was determined on a scale with 5 options in response to 20 questions. Significant difference was found between lecture-based and team-based learning in final scores (p<0.001). Content validity index of the scale of student satisfaction was 94%, and external and internal consistencies of the scale were 0.954 and 0.921 orderly (p<0.001). The degree of satisfaction from TBL compared to the lecture method was 81.3%. RESULTS revealed more success and student satisfaction from team-based learning compared to conventional lectures in teaching neurology to undergraduate students. It seems that application of new teaching methods such as team-based learning could be effectively introduced to improve levels of education and student learning.
Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

PubMed Central

2013-01-01

Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention. PMID:23442203
Spiral and Project-Based Learning with Peer Assessment in a Computer Science Project Management Course

ERIC Educational Resources Information Center

Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol

2016-01-01

Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning,…
The Development of Online Tutorial Program Design Using Problem-Based Learning in Open Distance Learning System

ERIC Educational Resources Information Center

Said, Asnah; Syarif, Edy

2016-01-01

This research aimed to evaluate of online tutorial program design by applying problem-based learning Research Methods currently implemented in the system of Open Distance Learning (ODL). The students must take a Research Methods course to prepare themselves for academic writing projects. Problem-based learning basically emphasizes the process of…
An online supervised learning method based on gradient descent for spiking neurons.

PubMed

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.
Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

PubMed

Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

2013-02-25

Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.
A study of active learning methods for named entity recognition in clinical text.

PubMed

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

2015-12-01

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

ERIC Educational Resources Information Center

Shultz, Ginger V.; Li, Ye

2016-01-01

Problem-based learning methods support student learning of content as well as scientific skills. In the course of problem-based learning, students seek outside information related to the problem, and therefore, information literacy skills are practiced when problem-based learning is used. This work describes a mixed-methods approach to investigate…
Evaluation of Deep Learning Based Stereo Matching Methods: from Ground to Aerial Images

NASA Astrophysics Data System (ADS)

Liu, J.; Ji, S.; Zhang, C.; Qin, Z.

2018-05-01

Dense stereo matching has been extensively studied in photogrammetry and computer vision. In this paper we evaluate the application of deep learning based stereo methods, which were raised from 2016 and rapidly spread, on aerial stereos other than ground images that are commonly used in computer vision community. Two popular methods are evaluated. One learns matching cost with a convolutional neural network (known as MC-CNN); the other produces a disparity map in an end-to-end manner by utilizing both geometry and context (known as GC-net). First, we evaluate the performance of the deep learning based methods for aerial stereo images by a direct model reuse. The models pre-trained on KITTI 2012, KITTI 2015 and Driving datasets separately, are directly applied to three aerial datasets. We also give the results of direct training on target aerial datasets. Second, the deep learning based methods are compared to the classic stereo matching method, Semi-Global Matching(SGM), and a photogrammetric software, SURE, on the same aerial datasets. Third, transfer learning strategy is introduced to aerial image matching based on the assumption of a few target samples available for model fine tuning. It experimentally proved that the conventional methods and the deep learning based methods performed similarly, and the latter had greater potential to be explored.
Evaluation of medical students of teacher-based and student-based teaching methods in Infectious diseases course.

PubMed

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

2015-01-01

Introduction: In recent years, medical education has changed dramatically and many medical schools in the world have been trying for expand modern training methods. Purpose of the research is to appraise the medical students of teacher-based and student-based teaching methods in Infectious diseases course, in the Medical School of Hormozgan Medical Sciences University. Methods: In this interventional study, a total of 52 medical scholars that used Section in this Infectious diseases course were included. About 50% of this course was presented by a teacher-based teaching method (lecture) and 50% by a student-based teaching method (problem-based learning). The satisfaction of students regarding these methods was assessed by a questionnaire and a test was used to measure their learning. information are examined with using SPSS 19 and paired t-test. Results: The satisfaction of students of student-based teaching method (problem-based learning) was more positive than their satisfaction of teacher-based teaching method (lecture).The mean score of students in teacher-based teaching method was 12.03 (SD=4.08) and in the student-based teaching method it was 15.50 (SD=4.26) and where is a considerable variation among them (p<0.001). Conclusion: The use of the student-based teaching method (problem-based learning) in comparison with the teacher-based teaching method (lecture) to present the Infectious diseases course led to the student satisfaction and provided additional learning opportunities.
Resource Letter ALIP-1: Active-Learning Instruction in Physics

NASA Astrophysics Data System (ADS)

Meltzer, David E.; Thornton, Ronald K.

2012-06-01

This Resource Letter provides a guide to the literature on research-based active-learning instruction in physics. These are instructional methods that are based on, assessed by, and validated through research on the teaching and learning of physics. They involve students in their own learning more deeply and more intensely than does traditional instruction, particularly during class time. The instructional methods and supporting body of research reviewed here offer potential for significantly improved learning in comparison to traditional lecture-based methods of college and university physics instruction. We begin with an introduction to the history of active learning in physics in the United States, and then discuss some methods for and outcomes of assessing pedagogical effectiveness. We enumerate and describe common characteristics of successful active-learning instructional strategies in physics. We then discuss a range of methods for introducing active-learning instruction in physics and provide references to those methods for which there is published documentation of student learning gains.

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.
The Development of a Robot-Based Learning Companion: A User-Centered Design Approach

ERIC Educational Resources Information Center

Hsieh, Yi-Zeng; Su, Mu-Chun; Chen, Sherry Y.; Chen, Gow-Dong

2015-01-01

A computer-vision-based method is widely employed to support the development of a variety of applications. In this vein, this study uses a computer-vision-based method to develop a playful learning system, which is a robot-based learning companion named RobotTell. Unlike existing playful learning systems, a user-centered design (UCD) approach is…
Implementation of Simulation Based-Concept Attainment Method to Increase Interest Learning of Engineering Mechanics Topic

NASA Astrophysics Data System (ADS)

Sultan, A. Z.; Hamzah, N.; Rusdi, M.

2018-01-01

The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.
A Learner-Centered Grading Method Focused on Reaching Proficiency with Course Learning Outcomes

ERIC Educational Resources Information Center

Toledo, Santiago; Dubas, Justin M.

2017-01-01

Getting students to use grading feedback as a tool for learning is a continual challenge for educators. This work proposes a method for evaluating student performance that provides feedback to students based on standards of learning dictated by clearly delineated course learning outcomes. This method combines elements of standards-based grading…
A Numerical Methods Course Based on B-Learning: Integrated Learning Design and Follow Up

ERIC Educational Resources Information Center

Cepeda, Francisco Javier Delgado

2013-01-01

Information and communication technologies advance continuously, providing a real support for learning processes. Learning technologies address areas which previously have corresponded to face-to-face learning, while mobile resources are having a growing impact on education. Numerical Methods is a discipline and profession based on technology. In…
The Effects of Computer-Supported Inquiry-Based Learning Methods and Peer Interaction on Learning Stellar Parallax

ERIC Educational Resources Information Center

Ruzhitskaya, Lanika

2011-01-01

The presented research study investigated the effects of computer-supported inquiry-based learning and peer interaction methods on effectiveness of learning a scientific concept. The stellar parallax concept was selected as a basic, and yet important in astronomy, scientific construct, which is based on a straightforward relationship of several…
Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

ERIC Educational Resources Information Center

Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

2016-01-01

This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…
Case-based learning facilitates critical thinking in undergraduate nutrition education: students describe the big picture.

PubMed

Harman, Tara; Bertrand, Brenda; Greer, Annette; Pettus, Arianna; Jennings, Jill; Wall-Bassett, Elizabeth; Babatunde, Oyinlola Toyin

2015-03-01

The vision of dietetics professions is based on interdependent education, credentialing, and practice. Case-based learning is a method of problem-based learning that is designed to heighten higher-order thinking. Case-based learning can assist students to connect education and specialized practice while developing professional skills for entry-level practice in nutrition and dietetics. This study examined student perspectives of their learning after immersion into case-based learning in nutrition courses. The theoretical frameworks of phenomenology and Bloom's Taxonomy of Educational Objectives triangulated the design of this qualitative study. Data were drawn from 426 written responses and three focus group discussions among 85 students from three upper-level undergraduate nutrition courses. Coding served to deconstruct the essence of respondent meaning given to case-based learning as a learning method. The analysis of the coding was the constructive stage that led to configuration of themes and theoretical practice pathways about student learning. Four leading themes emerged. Story or Scenario represents the ways that students described case-based learning, changes in student thought processes to accommodate case-based learning are illustrated in Method of Learning, higher cognitive learning that was achieved from case-based learning is represented in Problem Solving, and Future Practice details how students explained perceived professional competency gains from case-based learning. The skills that students acquired are consistent with those identified as essential to professional practice. In addition, the common concept of Big Picture was iterated throughout the themes and demonstrated that case-based learning prepares students for multifaceted problems that they are likely to encounter in professional practice. Copyright © 2015 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.
Kinespell: Kinesthetic Learning Activity and Assessment in a Digital Game-Based Learning Environment

NASA Astrophysics Data System (ADS)

Cariaga, Ada Angeli; Salvador, Jay Andrae; Solamo, Ma. Rowena; Feria, Rommel

Various approaches in learning are commonly classified into visual, auditory and kinesthetic (VAK) learning styles. One way of addressing the VAK learning styles is through game-based learning which motivates learners pursue knowledge holistically. The paper presents Kinespell, an unconventional method of learning through digital game-based learning. Kinespell is geared towards enhancing not only the learner’s spelling abilities but also the motor skills through utilizing wireless controllers. It monitors player’s performance through integrated assessment scheme. Results show that Kinespell may accommodate the VAK learning styles and is a promising alternative to established methods in learning and assessing students’ performance in Spelling.
A reward optimization method based on action subrewards in hierarchical reinforcement learning.

PubMed

Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

2014-01-01

Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.
The Effect of Teaching Methods and Learning Style on Learning Program Design in Web-Based Education Systems

ERIC Educational Resources Information Center

Hung, Yen-Chu

2012-01-01

The instructional value of web-based education systems has been an important area of research in information systems education. This study investigates the effect of various teaching methods on program design learning for students with specific learning styles in web-based education systems. The study takes first-year Computer Science and…
Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

ERIC Educational Resources Information Center

Dwiyogo, Wasis D.

2018-01-01

The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…
Problem-Based Learning: Instructor Characteristics, Competencies, and Professional Development

DTIC Science & Technology

2011-01-01

cognitive learning objectives addressed by student -centered instruction . For instance, experiential learning , a variation of which is used at the...based learning in grade school science or mathematics . However, the measures could be modified to focus on adult PBL (or student -centered learning ... student -centered learning methods, the findings should generalize across instructional methods of interest to the Army. Further research is required
Preparing Students for Flipped or Team-Based Learning Methods

ERIC Educational Resources Information Center

Balan, Peter; Clark, Michele; Restall, Gregory

2015-01-01

Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…
Comparison of Text-Based and Visual-Based Programming Input Methods for First-Time Learners

ERIC Educational Resources Information Center

Saito, Daisuke; Washizaki, Hironori; Fukazawa, Yoshiaki

2017-01-01

Aim/Purpose: When learning to program, both text-based and visual-based input methods are common. However, it is unclear which method is more appropriate for first-time learners (first learners). Background: The differences in the learning effect between text-based and visual-based input methods for first learners are compared the using a…
Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.
A Compound LAMS-MOODLE Environment to Support Collaborative Project-Based Learning: A Case Study with the Group Investigation Method

ERIC Educational Resources Information Center

Paschalis, Giorgos

2017-01-01

Collaborative project-based learning is well established as a component of several courses in higher education, since it seems to motivate students and make them active in the learning process. Collaborative Project-Based Learning methods are demanded so that tutors become able to intervene and guide the students in flexible ways: by encouraging…
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.
Problem-Based Learning: Exploiting Knowledge of How People Learn to Promote Effective Learning

ERIC Educational Resources Information Center

Wood, E. J.

2004-01-01

There is much information from educational psychology studies on how people learn. The thesis of this paper is that we should use this information to guide the ways in which we teach rather than blindly using our traditional methods. In this context, problem-based learning (PBL), as a method of teaching widely used in medical schools but…
The Effectiveness of the Game-Based Learning System for the Improvement of American Sign Language Using Kinect

ERIC Educational Resources Information Center

Kamnardsiri, Teerawat; Hongsit, Ler-on; Khuwuthyakorn, Pattaraporn; Wongta, Noppon

2017-01-01

This paper investigated students' achievement for learning American Sign Language (ASL), using two different methods. There were two groups of samples. The first experimental group (Group A) was the game-based learning for ASL, using Kinect. The second control learning group (Group B) was the traditional face-to-face learning method, generally…

Inquiry-Based Learning in China: Lesson Learned for School Science Practices

ERIC Educational Resources Information Center

Nuangchalerm, Prasart

2014-01-01

Inquiry-based learning is widely considered for science education in this era. This study aims to explore inquiry-based learning in teacher preparation program and the findings will help us to understanding what inquiry-based classroom is and how inquiry-based learning are. Data were collected by qualitative methods; classroom observation,…
Aligning professional skills and active learning methods: an application for information and communications technology engineering

NASA Astrophysics Data System (ADS)

Llorens, Ariadna; Berbegal-Mirabent, Jasmina; Llinàs-Audet, Xavier

2017-07-01

Engineering education is facing new challenges to effectively provide the appropriate skills to future engineering professionals according to market demands. This study proposes a model based on active learning methods, which is expected to facilitate the acquisition of the professional skills most highly valued in the information and communications technology (ICT) market. The theoretical foundations of the study are based on the specific literature on active learning methodologies. The Delphi method is used to establish the fit between learning methods and generic skills required by the ICT sector. An innovative proposition is therefore presented that groups the required skills in relation to the teaching method that best develops them. The qualitative research suggests that a combination of project-based learning and the learning contract is sufficient to ensure a satisfactory skills level for this profile of engineers.
Learning Practice-Based Research Methods: Capturing the Experiences of MSW Students

ERIC Educational Resources Information Center

Natland, Sidsel; Weissinger, Erika; Graaf, Genevieve; Carnochan, Sarah

2016-01-01

The literature on teaching research methods to social work students identifies many challenges, such as dealing with the tensions related to producing research relevant to practice, access to data to teach practice-based research, and limited student interest in learning research methods. This is an exploratory study of the learning experiences of…
A Qualitative Research on Active Learning Practices in Pre-School Education

ERIC Educational Resources Information Center

Pekdogan, Serpil; Kanak, Mehmet

2016-01-01

In educational environments prepared based on the active learning method, children learn with interest and pleasure, doing and experiencing, and directly through their own experiences. Considering the contributions of the active learning method and the educational environments designed based on it to children's development, it can be said that…
Trainees as Teachers in Team-Based Learning

ERIC Educational Resources Information Center

Ravindranath, Divy; Gay, Tamara L.; Riba, Michelle B.

2010-01-01

Objective: Team-based learning is an active learning modality that is gaining popularity in medical education. The authors studied the effect of using trainees as facilitators of team-based learning sessions. Methods: Team-based learning modules were developed and implemented by faculty members and trainees for the third-year medical student…
Game-Based E-Learning Is More Effective than a Conventional Instructional Method: A Randomized Controlled Trial with Third-Year Medical Students

PubMed Central

Boeker, Martin; Andel, Peter; Vach, Werner; Frankenschmidt, Alexander

2013-01-01

Background When compared with more traditional instructional methods, Game-based e-learning (GbEl) promises a higher motivation of learners by presenting contents in an interactive, rule-based and competitive way. Most recent systematic reviews and meta-analysis of studies on Game-based learning and GbEl in the medical professions have shown limited effects of these instructional methods. Objectives To compare the effectiveness on the learning outcome of a Game-based e-learning (GbEl) instruction with a conventional script-based instruction in the teaching of phase contrast microscopy urinalysis under routine training conditions of undergraduate medical students. Methods A randomized controlled trial was conducted with 145 medical students in their third year of training in the Department of Urology at the University Medical Center Freiburg, Germany. 82 subjects where allocated for training with an educational adventure-game (GbEl group) and 69 subjects for conventional training with a written script-based approach (script group). Learning outcome was measured with a 34 item single choice test. Students' attitudes were collected by a questionnaire regarding fun with the training, motivation to continue the training and self-assessment of acquired knowledge. Results The students in the GbEl group achieved significantly better results in the cognitive knowledge test than the students in the script group: the mean score was 28.6 for the GbEl group and 26.0 for the script group of a total of 34.0 points with a Cohen's d effect size of 0.71 (ITT analysis). Attitudes towards the recent learning experience were significantly more positive with GbEl. Students reported to have more fun while learning with the game when compared to the script-based approach. Conclusions Game-based e-learning is more effective than a script-based approach for the training of urinalysis in regard to cognitive learning outcome and has a high positive motivational impact on learning. Game-based e-learning can be used as an effective teaching method for self-instruction. PMID:24349257
Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.
Incorporating Problem-Based Learning in Physical Education Teacher Education

ERIC Educational Resources Information Center

Hushman, Glenn; Napper-Owen, Gloria

2011-01-01

Problem-based learning (PBL) is an educational method that identifies a problem as a context for student learning. Critical-thinking skills, deductive reasoning, knowledge, and behaviors are developed as students learn how theory can be applied to practical settings. Problem-based learning encourages self-direction, lifelong learning, and sharing…
A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

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

Xu, Songhua; Tourassi, Georgia

2012-01-01

The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using themore » BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.« less
Developing and Assessing Teachers' Knowledge of Game-Based Learning

ERIC Educational Resources Information Center

Shah, Mamta; Foster, Aroutis

2015-01-01

Research focusing on the development and assessment of teacher knowledge in game-based learning is in its infancy. A mixed-methods study was undertaken to educate pre-service teachers in game-based learning using the Game Network Analysis (GaNA) framework. Fourteen pre-service teachers completed a methods course, which prepared them in game…
Enhancing Student Learning in Knowledge-Based Courses: Integrating Team-Based Learning in Mass Communication Theory Classes

ERIC Educational Resources Information Center

Han, Gang; Newell, Jay

2014-01-01

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

ERIC Educational Resources Information Center

He, Wu; Yuan, Xiaohong; Yang, Li

2013-01-01

Case-based learning has been widely used in many disciplines. As an effective pedagogical method, case-based learning is also being used to support teaching and learning in the domain of information security. In this paper, we demonstrate case-based learning in information security by sharing our experiences in using a case study to teach security…
Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.
Web-Based Learning in a Geometry Course

ERIC Educational Resources Information Center

Chan, Hsungrow; Tsai, Pengheng; Huang, Tien-Yu

2006-01-01

This study concerns applying Web-based learning with learner controlled instructional materials in a geometry course. The experimental group learned in a Web-based learning environment, and the control group learned in a classroom. We observed that the learning method accounted for a total variation in learning effect of 19.1% in the 3rd grade and…
A deep learning-based multi-model ensemble method for cancer prediction.

PubMed

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.
Spatial Visualization Learning in Engineering: Traditional Methods vs. a Web-Based Tool

ERIC Educational Resources Information Center

Pedrosa, Carlos Melgosa; Barbero, Basilio Ramos; Miguel, Arturo Román

2014-01-01

This study compares an interactive learning manager for graphic engineering to develop spatial vision (ILMAGE_SV) to traditional methods. ILMAGE_SV is an asynchronous web-based learning tool that allows the manipulation of objects with a 3D viewer, self-evaluation, and continuous assessment. In addition, student learning may be monitored, which…
PBL and beyond: trends in collaborative learning.

PubMed

Pluta, William J; Richards, Boyd F; Mutnick, Andrew

2013-01-01

Building upon the disruption to lecture-based methods triggered by the introduction of problem-based learning, approaches to promote collaborative learning are becoming increasingly diverse, widespread and generally well accepted within medical education. Examples of relatively new, structured collaborative learning methods include team-based learning and just-in-time teaching. Examples of less structured approaches include think-pair share, case discussions, and the flipped classroom. It is now common practice in medical education to employ a range of instructional approaches to support collaborative learning. We believe that the adoption of such approaches is entering a new and challenging era. We define collaborate learning by drawing on the broader literature, including Chi's ICAP framework that emphasizes the importance of sustained, interactive explanation and elaboration by learners. We distinguish collaborate learning from constructive, active, and passive learning and provide preliminary evidence documenting the growth of methods that support collaborative learning. We argue that the rate of adoption of collaborative learning methods will accelerate due to a growing emphasis on the development of team competencies and the increasing availability of digital media. At the same time, the adoption collaborative learning strategies face persistent challenges, stemming from an overdependence on comparative-effectiveness research and a lack of useful guidelines about how best to adapt collaborative learning methods to given learning contexts. The medical education community has struggled to consistently demonstrate superior outcomes when using collaborative learning methods and strategies. Despite this, support for their use will continue to expand. To select approaches with the greatest utility, instructors must carefully align conditions of the learning context with the learning approaches under consideration. Further, it is critical that modifications are made with caution and that instructors verify that modifications do not impede the desired cognitive activities needed to support meaningful collaborative learning.
The Impact of Team-Based Learning on Nervous System Examination Knowledge of Nursing Students.

PubMed

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

2015-12-01

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

ERIC Educational Resources Information Center

Balim, Ali Günay; Inel-Ekici, Didem; Özcan, Erkan

2016-01-01

Problem based learning, in which events from daily life are presented as interesting scenarios, is one of the active learning approaches that encourages students to self-direct learning. Problem based learning, generally used in higher education, requires students to use high end thinking skills in learning environments. In order to use…
Generalized query-based active learning to identify differentially methylated regions in DNA.

PubMed

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

An Investigation into Pre-Service Teachers' Perceptions of Learning Primary School Science Using the Method of Problem Based Learning (PBL)

ERIC Educational Resources Information Center

Mohamed, Musa El Sharief

2015-01-01

The aim of this study was to investigate the pre-service teachers' perceptions into learning primary school science using the method of Problem Based Learning (PBL). This learning strategy has been introduced into the B.Ed. programme at the University of Trinidad and Tobago for pre-service teachers who are expected to implement it in their…
Creating Learning Communities in the Classroom

ERIC Educational Resources Information Center

Saville, Bryan K.; Lawrence, Natalie Kerr; Jakobsen, Krisztina V.

2012-01-01

There are many ways to construct classroom-based learning communities. Nevertheless, the emphasis is always on cooperative learning. In this article, the authors focus on three teaching methods--interteaching, team-based learning, and cooperative learning in large, lecture-based courses--that they have used successfully to create classroom-based…
Applying Item Response Theory Methods to Design a Learning Progression-Based Science Assessment

ERIC Educational Resources Information Center

Chen, Jing

2012-01-01

Learning progressions are used to describe how students' understanding of a topic progresses over time and to classify the progress of students into steps or levels. This study applies Item Response Theory (IRT) based methods to investigate how to design learning progression-based science assessments. The research questions of this study are: (1)…
Comparison of Standardized Test Scores from Traditional Classrooms and Those Using Problem-Based Learning

ERIC Educational Resources Information Center

Needham, Martha Elaine

2010-01-01

This research compares differences between standardized test scores in problem-based learning (PBL) classrooms and a traditional classroom for 6th grade students using a mixed-method, quasi-experimental and qualitative design. The research shows that problem-based learning is as effective as traditional teaching methods on standardized tests. The…
Is Student Knowledge of Anatomy Affected by a Problem-Based Learning Approach? A Review

ERIC Educational Resources Information Center

Williams, Jonathan M.

2014-01-01

A fundamental understanding of anatomy is critical for students on many health science courses. It has been suggested that a problem-based approach to learning anatomy may result in deficits in foundation knowledge. The aim of this review is to compare traditional didactic methods with problem-based learning methods for obtaining anatomy…
A Framework for Problem-Based Learning: Teaching Mathematics with a Relational Problem-Based Pedagogy

ERIC Educational Resources Information Center

Schettino, Carmel

2016-01-01

One recommendation for encouraging young women and other underrepresented students in their mathematical studies is to find instructional methods, such as problem-based learning (PBL), that allow them to feel included in the learning process. Using a more relationally centered pedagogy along with more inclusive instructional methods may be a way…
What Did They Learn in School Today? A Method for Exploring Aspects of Learning in Physical Education

ERIC Educational Resources Information Center

Quennerstedt, Mikael; Annerstedt, Claes; Barker, Dean; Karlefors, Inger; Larsson, Håkan; Redelius, Karin; Öhman, Marie

2014-01-01

This paper outlines a method for exploring learning in educational practice. The suggested method combines an explicit learning theory with robust methodological steps in order to explore aspects of learning in school physical education. The design of the study is based on sociocultural learning theory, and the approach adds to previous research…
An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

NASA Astrophysics Data System (ADS)

Balasubramanian, R.

2007-12-01

Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.
Effectiveness of Jigsaw learning compared to lecture-based learning in dental education.

PubMed

Sagsoz, O; Karatas, O; Turel, V; Yildiz, M; Kaya, E

2017-02-01

The objective of this study was to evaluate the success levels of students using the Jigsaw learning method in dental education. Fifty students with similar grade point average (GPA) scores were selected and randomly assigned into one of two groups (n = 25). A pretest concerning 'adhesion and bonding agents in dentistry' was administered to all students before classes. The Jigsaw learning method was applied to the experimental group for 3 weeks. At the same time, the control group was taking classes using the lecture-based learning method. At the end of the 3 weeks, all students were retested (post-test) on the subject. A retention test was administered 3 weeks after the post-test. Mean scores were calculated for each test for the experimental and control groups, and the data obtained were analysed using the independent samples t-test. No significant difference was determined between the Jigsaw and lecture-based methods at pretest or post-test. The highest mean test score was observed in the post-test with the Jigsaw method. In the retention test, success with the Jigsaw method was significantly higher than that with the lecture-based method. The Jigsaw method is as effective as the lecture-based method. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Problem-based Learning Behavior: The Impact of Differences in Problem-Based Learning Style and Activity on Students' Achievement.

ERIC Educational Resources Information Center

van Til, Cita T.; And Others

Problem-based learning (PBL) as a new instructional method is becoming increasingly popular. PBL is hypothesized to have a number of advantages for learning because it applies insights from cognitive learning theory and it fosters a lifelong learning strategy. As in all learning programs there are individual differences between students. This…
State-of-the-Art Model Driven Game Development: A Survey of Technological Solutions for Game-Based Learning

ERIC Educational Resources Information Center

Tang, Stephen; Hanneghan, Martin

2011-01-01

Game-based learning harnesses the advantages of computer games technology to create a fun, motivating and interactive virtual learning environment that promotes problem-based experiential learning. Such an approach is advocated by many commentators to provide an enhanced learning experience than those based on traditional didactic methods.…
A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
Comparison of Electronic Learning Versus Lecture-based Learning in Improving Emergency Medicine Residents' Knowledge About Mild Induced Hypothermia After Cardiac Arrest.

PubMed

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

2017-08-01

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

ERIC Educational Resources Information Center

Williams van Rooij, Shahron

2007-01-01

This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…
Exploring the Effects of Online Team-Based Learning and Co-Regulated Learning on Students' Development of Computing Skills

ERIC Educational Resources Information Center

Tsai, Chia-Wen

2016-01-01

As more and more educational institutions are providing online courses, it is necessary to design effective teaching methods integrated with technologies to benefit both teachers and students. The researcher in this study designed innovative online teaching methods of team-based learning (TBL) and co-regulated learning (CRL) to improve students'…
Project-Based Learning in Undergraduate Environmental Chemistry Laboratory: Using EPA Methods to Guide Student Method Development for Pesticide Quantitation

ERIC Educational Resources Information Center

Davis, Eric J.; Pauls, Steve; Dick, Jonathan

2017-01-01

Presented is a project-based learning (PBL) laboratory approach for an upper-division environmental chemistry or quantitative analysis course. In this work, a combined laboratory class of 11 environmental chemistry students developed a method based on published EPA methods for the extraction of dichlorodiphenyltrichloroethane (DDT) and its…
Problem-Based Learning and Structural Redesign in a Choral Methods Course

ERIC Educational Resources Information Center

Freer, Patrick

2017-01-01

This article describes the process of structural redesign of an undergraduate music education choral methods course. A framework incorporating Problem-based Learning was developed to promote individualized student learning. Ten students participated in the accompanying research study, contributing an array of written and spoken comments as well as…
Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.
Problem-Based Learning: Student Engagement, Learning and Contextualized Problem-Solving. Occasional Paper

ERIC Educational Resources Information Center

Mossuto, Mark

2009-01-01

The adoption of problem-based learning as a teaching method in the advertising and public relations programs offered by the Business TAFE (Technical and Further Education) School at RMIT University is explored in this paper. The effect of problem-based learning on student engagement, student learning and contextualised problem-solving was…
How Teaching Science Using Project-Based Learning Strategies Affects the Classroom Learning Environment

ERIC Educational Resources Information Center

Hugerat, Muhamad

2016-01-01

This study involved 458 ninth-grade students from two different Arab middle schools in Israel. Half of the students learned science using project-based learning strategies and the other half learned using traditional methods (non-project-based). The classes were heterogeneous regarding their achievements in the sciences. The adapted questionnaire…

Game-based e-learning is more effective than a conventional instructional method: a randomized controlled trial with third-year medical students.

PubMed

Boeker, Martin; Andel, Peter; Vach, Werner; Frankenschmidt, Alexander

2013-01-01

When compared with more traditional instructional methods, Game-based e-learning (GbEl) promises a higher motivation of learners by presenting contents in an interactive, rule-based and competitive way. Most recent systematic reviews and meta-analysis of studies on Game-based learning and GbEl in the medical professions have shown limited effects of these instructional methods. To compare the effectiveness on the learning outcome of a Game-based e-learning (GbEl) instruction with a conventional script-based instruction in the teaching of phase contrast microscopy urinalysis under routine training conditions of undergraduate medical students. A randomized controlled trial was conducted with 145 medical students in their third year of training in the Department of Urology at the University Medical Center Freiburg, Germany. 82 subjects where allocated for training with an educational adventure-game (GbEl group) and 69 subjects for conventional training with a written script-based approach (script group). Learning outcome was measured with a 34 item single choice test. Students' attitudes were collected by a questionnaire regarding fun with the training, motivation to continue the training and self-assessment of acquired knowledge. The students in the GbEl group achieved significantly better results in the cognitive knowledge test than the students in the script group: the mean score was 28.6 for the GbEl group and 26.0 for the script group of a total of 34.0 points with a Cohen's d effect size of 0.71 (ITT analysis). Attitudes towards the recent learning experience were significantly more positive with GbEl. Students reported to have more fun while learning with the game when compared to the script-based approach. Game-based e-learning is more effective than a script-based approach for the training of urinalysis in regard to cognitive learning outcome and has a high positive motivational impact on learning. Game-based e-learning can be used as an effective teaching method for self-instruction.
Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills

ERIC Educational Resources Information Center

Jewpanich, Chaiwat; Piriyasurawong, Pallop

2015-01-01

This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…
Development and Evaluation of an E-Learning Course for Deaf and Hard of Hearing Based on the Advanced Adapted Pedagogical Index Method

ERIC Educational Resources Information Center

Debevc, Matjaž; Stjepanovic, Zoran; Holzinger, Andreas

2014-01-01

Web-based and adapted e-learning materials provide alternative methods of learning to those used in a traditional classroom. Within the study described in this article, deaf and hard of hearing people used an adaptive e-learning environment to improve their computer literacy. This environment included streaming video with sign language interpreter…
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.
Problem based learning: the effect of real time data on the website to student independence

NASA Astrophysics Data System (ADS)

Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

2018-05-01

Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.
Creating Stimulating Learning and Thinking Using New Models of Activity-Based Learning and Metacognitive-Based Activities

ERIC Educational Resources Information Center

Pang, Katherine

2010-01-01

The purpose of this paper is to present a novel way to stimulate learning, creativity, and thinking based on a new understanding of activity-based learning (ABL) and two methods for developing metacognitive-based activities for the classroom. ABL, in this model, is based on the premise that teachers are distillers and facilitators of information…
[Application and case analysis on the problem-based teaching of Jingluo Shuxue Xue (Science of Meridian and Acupoint) in reference to the team oriented learning method].

PubMed

Ma, Ruijie; Lin, Xianming

2015-12-01

The problem based teaching (PBT) has been the main approach to the training in the universities o the world. Combined with the team oriented learning method, PBT will become the method available to the education in medical universities. In the paper, based on the common questions in teaching Jingluo Shuxue Xue (Science of Meridian and Acupoint), the concepts and characters of PBT and the team oriented learning method were analyzed. The implementation steps of PBT were set up in reference to the team oriented learning method. By quoting the original text in Beiji Qianjin Yaofang (Essential recipes for emergent use worth a thousand gold), the case analysis on "the thirteen devil points" was established with PBT.
Use of Case-Based or Hands-On Laboratory Exercises with Physiology Lectures Improves Knowledge Retention, but Veterinary Medicine Students Prefer Case-Based Activities

ERIC Educational Resources Information Center

McFee, Renee M.; Cupp, Andrea S.; Wood, Jennifer R.

2018-01-01

Didactic lectures are prevalent in physiology courses within veterinary medicine programs, but more active learning methods have also been utilized. Our goal was to identify the most appropriate learning method to augment the lecture component of our physiology course. We hypothesized that case-based learning would be well received by students and…
A Framework for the Flexible Content Packaging of Learning Objects and Learning Designs

ERIC Educational Resources Information Center

Lukasiak, Jason; Agostinho, Shirley; Burnett, Ian; Drury, Gerrard; Goodes, Jason; Bennett, Sue; Lockyer, Lori; Harper, Barry

2004-01-01

This paper presents a platform-independent method for packaging learning objects and learning designs. The method, entitled a Smart Learning Design Framework, is based on the MPEG-21 standard, and uses IEEE Learning Object Metadata (LOM) to provide bibliographic, technical, and pedagogical descriptors for the retrieval and description of learning…
Teaching Prevention in Pediatrics.

ERIC Educational Resources Information Center

Cheng, Tina L.; Greenberg, Larrie; Loeser, Helen; Keller, David

2000-01-01

Reviews methods of teaching preventive medicine in pediatrics and highlights innovative programs. Methods of teaching prevention in pediatrics include patient interactions, self-directed learning, case-based learning, small-group learning, standardized patients, computer-assisted instruction, the Internet, student-centered learning, and lectures.…
Comparing Learning Outcomes of Video-Based E-Learning with Face-to-Face Lectures of Agricultural Engineering Courses in Korean Agricultural High Schools

ERIC Educational Resources Information Center

Park, Sung Youl; Kim, Soo-Wook; Cha, Seung-Bong; Nam, Min-Woo

2014-01-01

This study investigated the effectiveness of e-learning by comparing the learning outcomes in conventional face-to-face lectures and e-learning methods. Two video-based e-learning contents were developed based on the rapid prototyping model and loaded onto the learning management system (LMS), which was available at http://www.greenehrd.com.…
Internet-Based Distance Learning in Higher Education.

ERIC Educational Resources Information Center

Hofmann, Donald W.

2002-01-01

Suggests that the effectiveness of Internet-based distance learning has increased with its increased popularity. Looks at the differences between the effectiveness of Internet-based distance learning and traditional methods. Indicates that distance learning is more effective because of the necessity for students to become active learners.…
Learning outcomes of "The Oncology Patient" study among nursing students: A comparison of teaching strategies.

PubMed

Roca, Judith; Reguant, Mercedes; Canet, Olga

2016-11-01

Teaching strategies are essential in order to facilitate meaningful learning and the development of high-level thinking skills in students. To compare three teaching methodologies (problem-based learning, case-based teaching and traditional methods) in terms of the learning outcomes achieved by nursing students. This quasi-experimental research was carried out in the Nursing Degree programme in a group of 74 students who explored the subject of The Oncology Patient through the aforementioned strategies. A performance test was applied based on Bloom's Revised Taxonomy. A significant correlation was found between the intragroup theoretical and theoretical-practical dimensions. Likewise, intergroup differences were related to each teaching methodology. Hence, significant differences were estimated between the traditional methodology (x-=9.13), case-based teaching (x-=12.96) and problem-based learning (x-=14.84). Problem-based learning was shown to be the most successful learning method, followed by case-based teaching and the traditional methodology. Copyright © 2016 Elsevier Ltd. All rights reserved.
Effectiveness of Project Based Learning in Statistics for Lower Secondary Schools

ERIC Educational Resources Information Center

Siswono, Tatag Yuli Eko; Hartono, Sugi; Kohar, Ahmad Wachidul

2018-01-01

Purpose: This study aimed at investigating the effectiveness of implementing Project Based Learning (PBL) on the topic of statistics at a lower secondary school in Surabaya city, Indonesia, indicated by examining student learning outcomes, student responses, and student activity. Research Methods: A quasi experimental method was conducted over two…
Service-Learning's Ongoing Journey as a Method of Instruction: Implications for School-Based Agricultural Education

ERIC Educational Resources Information Center

Roberts, Richie; Edwards, M. Craig

2015-01-01

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

ERIC Educational Resources Information Center

Smilkstein, Rita

1991-01-01

The natural teaching method is active and student-centered, based on schema and constructivist theories, and informed by research in neuroplasticity. A schema is a mental picture or understanding of something we have learned. Humans can have knowledge only to the degree to which they have constructed schemas from learning experiences and practice.…
Experiential Learning Methods, Simulation Complexity and Their Effects on Different Target Groups

ERIC Educational Resources Information Center

Kluge, Annette

2007-01-01

This article empirically supports the thesis that there is no clear and unequivocal argument in favor of simulations and experiential learning. Instead the effectiveness of simulation-based learning methods depends strongly on the target group's characteristics. Two methods of supporting experiential learning are compared in two different complex…
The "Anchor" Method: Principle and Practice.

ERIC Educational Resources Information Center

Selgin, Paul

This report discusses the "anchor" language learning method that is based upon derivation rather than construction, using Italian as an example of a language to be learned. This method borrows from the natural process of language learning as it asks the student to remember whole expressions that serve as vehicles for learning both words…
Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

PubMed

Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

2016-08-01

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.
Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.
Achievement of learning outcome after implemented physical modules based on problem based learning

NASA Astrophysics Data System (ADS)

Isna, R.; Masykuri, M.; Sukarmin

2018-03-01

Implementation of Problem BasedLearning (PBL) modules can grow the students' thinking skills to solve the problems in daily life and equip the students into higher education levels. The purpose of this research is to know the achievement of learning outcome after implementation physics module based on PBL in Newton,s Law of Gravity. This research method use the experimental method with posttest only group design. To know the achievement of student learning outcomes was analyzed using t test through application of SPSS 18. Based on research result, it is found that the average of student learning outcomes after appliying physics module based on PBL has reached the minimal exhaustiveness criteria. In addition, students' scientific attitudes also improved at each meeting. Presentation activities which contained at learning sync are also able to practice speaking skills and broaden their knowledge. Looking at some shortcomings during the study, it is suggested the issues raised into learning should be a problem close to the life of students so that, the students are more active and enthusiastic in following the learning of physics.
Active learning in capstone design courses.

PubMed

Goldberg, Jay R

2012-01-01

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

PubMed

Han, Xu; Kim, Jung-jae; Kwoh, Chee Keong

2016-01-01

Biomedical text mining may target various kinds of valuable information embedded in the literature, but a critical obstacle to the extension of the mining targets is the cost of manual construction of labeled data, which are required for state-of-the-art supervised learning systems. Active learning is to choose the most informative documents for the supervised learning in order to reduce the amount of required manual annotations. Previous works of active learning, however, focused on the tasks of entity recognition and protein-protein interactions, but not on event extraction tasks for multiple event types. They also did not consider the evidence of event participants, which might be a clue for the presence of events in unlabeled documents. Moreover, the confidence scores of events produced by event extraction systems are not reliable for ranking documents in terms of informativity for supervised learning. We here propose a novel committee-based active learning method that supports multi-event extraction tasks and employs a new statistical method for informativity estimation instead of using the confidence scores from event extraction systems. Our method is based on a committee of two systems as follows: We first employ an event extraction system to filter potential false negatives among unlabeled documents, from which the system does not extract any event. We then develop a statistical method to rank the potential false negatives of unlabeled documents 1) by using a language model that measures the probabilities of the expression of multiple events in documents and 2) by using a named entity recognition system that locates the named entities that can be event arguments (e.g. proteins). The proposed method further deals with unknown words in test data by using word similarity measures. We also apply our active learning method for the task of named entity recognition. We evaluate the proposed method against the BioNLP Shared Tasks datasets, and show that our method can achieve better performance than such previous methods as entropy and Gibbs error based methods and a conventional committee-based method. We also show that the incorporation of named entity recognition into the active learning for event extraction and the unknown word handling further improve the active learning method. In addition, the adaptation of the active learning method into named entity recognition tasks also improves the document selection for manual annotation of named entities.
Skills-Based Learning for Reproducible Expertise: Looking Elsewhere for Guidance

ERIC Educational Resources Information Center

Roessger, Kevin M.

2016-01-01

Despite the prevalence of adult skills-based learning, adult education researchers continue to ignore effective interdisciplinary skills-based methods. Prominent researchers dismiss empirically supported teaching guidelines, preferring situational, emancipatory methods with no demonstrable effect on skilled performance or reproducible expertise.…
Comparing Modes of Delivery: Classroom and On-Line (and Other) Learning.

ERIC Educational Resources Information Center

deLeon, Linda; Killian, Jerri

2000-01-01

Moving beyond question of whether on-line education is beneficial or harmful, explores conditions under which one or another of six instructional methods lecture, collaborative learning, experiential learning, learning contracts, televised courses, and Web-based learning work best. Finds specific methods more appropriate for some subject matters,…
Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

ERIC Educational Resources Information Center

Soroush, Masoud; Weinberger, Charles B.

2010-01-01

This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…
Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

ERIC Educational Resources Information Center

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…
Course Evaluation: Reconfigurations for Learning with Learning Management Systems

ERIC Educational Resources Information Center

Park, Ji Yong

2014-01-01

The introduction of online delivery platforms such as learning management systems (LMS) in tertiary education has changed the methods and modes of curriculum delivery and communication. While course evaluation methods have also changed from paper-based in-class-administered methods to largely online-administered methods, the data collection…
Case-based explanation of non-case-based learning methods.

PubMed Central

Caruana, R.; Kangarloo, H.; Dionisio, J. D.; Sinha, U.; Johnson, D.

1999-01-01

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. PMID:10566351
Comparison of lecture and team-based learning in medical ethics education.

PubMed

Ozgonul, Levent; Alimoglu, Mustafa Kemal

2017-01-01

Medical education literature suggests that ethics education should be learner-centered and problem-based rather than theory-based. Team-based learning is an appropriate method for this suggestion. However, its effectiveness was not investigated enough in medical ethics education. Is team-based learning effective in medical ethics education in terms of knowledge retention, in-class learner engagement, and learner reactions? This was a prospective controlled follow-up study. We changed lecture with team-based learning method to teach four topics in a 2-week medical ethics clerkship, while the remaining topics were taught by lectures. For comparison, we formed team-based learning and lecture groups, in which the students and instructor are the same, but the topics and teaching methodologies are different. We determined in-class learner engagement by direct observation and student satisfaction by feedback forms. Student success for team-based learning and lecture topics in the end-of-clerkship exam and two retention tests performed 1 year and 2 years later were compared. Ethical considerations: Ethical approval for the study was granted by Akdeniz University Board of Ethics on Noninvasive Clinical Human Studies Ethics committee. Short-term knowledge retention did not differ; however, team-based learning was found superior to lecture at long-term retention tests. Student satisfaction was high with team-based learning and in-class engagement was better in team-based learning sessions. Our results on learner engagement and satisfaction with team-based learning were similar to those of previous reports. However, knowledge retention results in our study were contrary to literature. The reason might be the fact that students prepared for the end-of-clerkship pass/fail exam (short term) regardless of the teaching method. But, at long-term retention tests, they did not prepare for the exam and answered the questions just using the knowledge retained in their memories. Our findings suggest that team-based learning is a better alternative to lecture to teach ethics in medical education.
Renewed roles for librarians in problem-based learning in the medical curriculum.

PubMed

Mi, Misa

2011-01-01

Problem-based learning (PBL) is a teaching-learning process or method of instruction that is widely used in medical education curricula. Librarians play important roles as facilitators for PBL as well as guides for information resources. Involvement in PBL activities presents unique opportunities to incorporate library resources and instruction into the medical curriculum. This article reviews the problem-based learning method within the conceptual framework of the learning theory of constructivism. It describes how a medical librarian at a U.S. medical school used emerging technologies to facilitate PBL small group case discussions, guide students to quality information resources, and enhance the learning environment for the PBL process.
Creation of Exercises for Team-Based Learning in Business

ERIC Educational Resources Information Center

Timmerman, John E.; Morris, R. Franklin, Jr.

2015-01-01

Team-based learning (TBL) is an approach that builds on both the case method and problem-based learning and has been widely adopted in the sciences and healthcare disciplines. In recent years business disciplines have also discovered the value of this approach. One of the key characteristics of the team-based learning approach consists of…
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach

ERIC Educational Resources Information Center

Fazlollahtabar, Hamed; Mahdavi, Iraj

2009-01-01

Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…
A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566
Comparison of meaningful learning characteristics in simulated nursing practice after traditional versus computer-based simulation method: a qualitative videography study.

PubMed

Poikela, Paula; Ruokamo, Heli; Teräs, Marianne

2015-02-01

Nursing educators must ensure that nursing students acquire the necessary competencies; finding the most purposeful teaching methods and encouraging learning through meaningful learning opportunities is necessary to meet this goal. We investigated student learning in a simulated nursing practice using videography. The purpose of this paper is to examine how two different teaching methods presented students' meaningful learning in a simulated nursing experience. The 6-hour study was divided into three parts: part I, general information; part II, training; and part III, simulated nursing practice. Part II was delivered by two different methods: a computer-based simulation and a lecture. The study was carried out in the simulated nursing practice in two universities of applied sciences, in Northern Finland. The participants in parts II and I were 40 first year nursing students; 12 student volunteers continued to part III. Qualitative analysis method was used. The data were collected using video recordings and analyzed by videography. The students who used a computer-based simulation program were more likely to report meaningful learning themes than those who were first exposed to lecture method. Educators should be encouraged to use computer-based simulation teaching in conjunction with other teaching methods to ensure that nursing students are able to receive the greatest educational benefits. Copyright © 2014 Elsevier Ltd. All rights reserved.
Improving the quality of learning in science through optimization of lesson study for learning community

NASA Astrophysics Data System (ADS)

Setyaningsih, S.

2018-03-01

Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.
Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

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

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

PubMed

de Jong, Nynke; Savin-Baden, Maggi; Cunningham, Anne Marie; Verstegen, Daniëlle M L

2014-09-01

Blended learning in which online education is combined with face-to-face education is especially useful for (future) health care professionals who need to keep up-to-date. Blended learning can make learning more efficient, for instance by removing barriers of time and distance. In the past distance-based learning activities have often been associated with traditional delivery-based methods, individual learning and limited contact. The central question in this paper is: can blended learning be active and collaborative? Three cases of blended, active and collaborative learning are presented. In case 1 a virtual classroom is used to realize online problem-based learning (PBL). In case 2 PBL cases are presented in Second Life, a 3D immersive virtual world. In case 3 discussion forums, blogs and wikis were used. In all cases face-to-face meetings were also organized. Evaluation results of the three cases clearly show that active, collaborative learning at a distance is possible. Blended learning enables the use of novel instructional methods and student-centred education. The three cases employ different educational methods, thus illustrating diverse possibilities and a variety of learning activities in blended learning. Interaction and communication rules, the role of the teacher, careful selection of collaboration tools and technical preparation should be considered when designing and implementing blended learning.

Deep learning and texture-based semantic label fusion for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

2018-02-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.
Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

PubMed

Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

2018-01-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.
A mixed methods evaluation of team-based learning for applied pathophysiology in undergraduate nursing education.

PubMed

Branney, Jonathan; Priego-Hernández, Jacqueline

2018-02-01

It is important for nurses to have a thorough understanding of the biosciences such as pathophysiology that underpin nursing care. These courses include content that can be difficult to learn. Team-based learning is emerging as a strategy for enhancing learning in nurse education due to the promotion of individual learning as well as learning in teams. In this study we sought to evaluate the use of team-based learning in the teaching of applied pathophysiology to undergraduate student nurses. A mixed methods observational study. In a year two, undergraduate nursing applied pathophysiology module circulatory shock was taught using Team-based Learning while all remaining topics were taught using traditional lectures. After the Team-based Learning intervention the students were invited to complete the Team-based Learning Student Assessment Instrument, which measures accountability, preference and satisfaction with Team-based Learning. Students were also invited to focus group discussions to gain a more thorough understanding of their experience with Team-based Learning. Exam scores for answers to questions based on Team-based Learning-taught material were compared with those from lecture-taught material. Of the 197 students enrolled on the module, 167 (85% response rate) returned the instrument, the results from which indicated a favourable experience with Team-based Learning. Most students reported higher accountability (93%) and satisfaction (92%) with Team-based Learning. Lectures that promoted active learning were viewed as an important feature of the university experience which may explain the 76% exhibiting a preference for Team-based Learning. Most students wanted to make a meaningful contribution so as not to let down their team and they saw a clear relevance between the Team-based Learning activities and their own experiences of teamwork in clinical practice. Exam scores on the question related to Team-based Learning-taught material were comparable to those related to lecture-taught material. Most students had a preference for, and reported higher accountability and satisfaction with Team-based Learning. Through contextualisation and teamwork, Team-based Learning appears to be a strategy that confers strong pedagogical benefits for teaching applied pathophysiology (bioscience) to student nurses. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
A stacked sequential learning method for investigator name recognition from web-based medical articles

NASA Astrophysics Data System (ADS)

Zhang, Xiaoli; Zou, Jie; Le, Daniel X.; Thoma, George

2010-01-01

"Investigator Names" is a newly required field in MEDLINE citations. It consists of personal names listed as members of corporate organizations in an article. Extracting investigator names automatically is necessary because of the increasing volume of articles reporting collaborative biomedical research in which a large number of investigators participate. In this paper, we present an SVM-based stacked sequential learning method in a novel application - recognizing named entities such as the first and last names of investigators from online medical journal articles. Stacked sequential learning is a meta-learning algorithm which can boost any base learner. It exploits contextual information by adding the predicted labels of the surrounding tokens as features. We apply this method to tag words in text paragraphs containing investigator names, and demonstrate that stacked sequential learning improves the performance of a nonsequential base learner such as an SVM classifier.
Information Literacy of Medical Students Studying in the Problem-Based and Traditional Curriculum

ERIC Educational Resources Information Center

Eskola, Eeva-Liisa

2005-01-01

Introduction: This paper reports on part of a research project on relationships between learning methods and students' information behaviour in Finland. It has been suggested that student-centred learning methods, such as problem-based learning, influence students' information needs, seeking and use. The focus of this paper is on the concept of…
Reform-Based-Instructional Method and Learning Styles on Students' Achievement and Retention in Mathematics: Administrative Implications

ERIC Educational Resources Information Center

Modebelu, M. N.; Ogbonna, C. C.

2014-01-01

This study aimed at determining the effect of reform-based-instructional method learning styles on students' achievement and retention in mathematics. A sample size of 119 students was randomly selected. The quasiexperimental design comprising pre-test, post-test, and randomized control group were employed. The Collin Rose learning styles…
The Use of Problem-Based Learning Model to Improve Quality Learning Students Morals

ERIC Educational Resources Information Center

Nurzaman

2017-01-01

Model of moral cultivation in MTsN Bangunharja done using three methods, classical cultivation methods, extra-curricular activities in the form of religious activities, scouting, sports, and Islamic art, and habituation of morals. Problem base learning models in MTsN Bangunharja applied using the following steps: find the problem, define the…
Database Design Learning: A Project-Based Approach Organized through a Course Management System

ERIC Educational Resources Information Center

Dominguez, Cesar; Jaime, Arturo

2010-01-01

This paper describes an active method for database design learning through practical tasks development by student teams in a face-to-face course. This method integrates project-based learning, and project management techniques and tools. Some scaffolding is provided at the beginning that forms a skeleton that adapts to a great variety of…
A combined learning algorithm for prostate segmentation on 3D CT images.

PubMed

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

2017-11-01

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

PubMed Central

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

2015-01-01

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

PubMed

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

2015-01-01

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

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.
Problem-Based Learning and Learning Approach: Is There a Relationship?

ERIC Educational Resources Information Center

Groves, Michele

2005-01-01

Aim: To assess the influence of a graduate-entry PBL (problem-based learning) curriculum on individual learning style; and to investigate the relationship between learning style, academic achievement and clinical reasoning skill. Method: Subjects were first-year medical students completed the Study Process Questionnaire at the commencement, and…
Toward an Instructionally Oriented Theory of Example-Based Learning

ERIC Educational Resources Information Center

Renkl, Alexander

2014-01-01

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from…
Team-Based Learning in Pharmacy Education

PubMed Central

Ofstad, William

2013-01-01

Instructors wanting to engage students in the classroom seek methods to augment the delivery of factual information and help students move from being passive recipients to active participants in their own learning. One such method that has gained interest is team-based learning. This method encourages students to be prepared before class and has students work in teams while in the classroom. Key benefits to this pedagogy are student engagement, improved communication skills, and enhanced critical-thinking abilities. In most cases, student satisfaction and academic performance are also noted. This paper reviews the fundamentals of team-based learning in pharmacy education and its implementation in the classroom. Literature reports from medical, nursing, and pharmacy programs are also discussed. PMID:23716738
Exercise in Inquiry: Critical Thinking in an Inquiry-Based Exercise Physiology Laboratory Course.

ERIC Educational Resources Information Center

DiPasquale, Dana M.; Mason, Cheryl L.; Kolkhorst, Fred W.

2003-01-01

Describes an inquiry-based teaching method implemented in an undergraduate exercise physiology laboratory course. Indicates students' strong, positive feelings about the inquiry-based teaching method and shows that inquiry-based learning results in a higher order of learning not typically observed in traditional style classes. This teaching method…
Time and Learning Efficiency in Internet-Based Learning: A Systematic Review and Meta-Analysis

ERIC Educational Resources Information Center

Cook, David A.; Levinson, Anthony J.; Garside, Sarah

2010-01-01

Authors have claimed that Internet-based instruction promotes greater learning efficiency than non-computer methods. Objectives Determine, through a systematic synthesis of evidence in health professions education, how Internet-based instruction compares with non-computer instruction in time spent learning, and what features of Internet-based…
Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453
Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.
Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

PubMed

Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

2017-01-01

Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

Model-based reinforcement learning with dimension reduction.

PubMed

Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi

2016-12-01

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

ERIC Educational Resources Information Center

Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando

2013-01-01

Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…
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.
Group-Based Active Learning of Classification Models.

PubMed

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.
Student Analysis of Handout Development based on Guided Discovery Method in Process Evaluation and Learning Outcomes of Biology

NASA Astrophysics Data System (ADS)

Nerita, S.; Maizeli, A.; Afza, A.

2017-09-01

Process Evaluation and Learning Outcomes of Biology subjects discusses the evaluation process in learning and application of designed and processed learning outcomes. Some problems found during this subject was the student difficult to understand the subject and the subject unavailability of learning resources that can guide and make students independent study. So, it necessary to develop a learning resource that can make active students to think and to make decisions with the guidance of the lecturer. The purpose of this study is to produce handout based on guided discovery method that match the needs of students. The research was done by using 4-D models and limited to define phase that is student requirement analysis. Data obtained from the questionnaire and analyzed descriptively. The results showed that the average requirement of students was 91,43%. Can be concluded that students need a handout based on guided discovery method in the learning process.
[Problem-based learning, a comparison in the acquisition of transversal competencies].

PubMed

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

2009-01-01

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

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic
Spiral and Project-Based Learning with Peer Assessment in a Computer Science Project Management Course

NASA Astrophysics Data System (ADS)

Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol

2016-06-01

Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning, spiral learning and peer assessment. Namely, the course is articulated during a semester through the structured (progressive and incremental) development of a sequence of four projects, whose duration, scope and difficulty of management increase as the student gains theoretical and instrumental knowledge related to planning, monitoring and controlling projects. Moreover, the proposal is complemented using peer assessment. The proposal has already been implemented and validated for the last 3 years in two different universities. In the first year, project-based learning and spiral learning methods were combined. Such a combination was also employed in the other 2 years; but additionally, students had the opportunity to assess projects developed by university partners and by students of the other university. A total of 154 students have participated in the study. We obtain a gain in the quality of the subsequently projects derived from the spiral project-based learning. Moreover, this gain is significantly bigger when peer assessment is introduced. In addition, high-performance students take advantage of peer assessment from the first moment, whereas the improvement in poor-performance students is delayed.
Piloting a Process Maturity Model as an e-Learning Benchmarking Method

ERIC Educational Resources Information Center

Petch, Jim; Calverley, Gayle; Dexter, Hilary; Cappelli, Tim

2007-01-01

As part of a national e-learning benchmarking initiative of the UK Higher Education Academy, the University of Manchester is carrying out a pilot study of a method to benchmark e-learning in an institution. The pilot was designed to evaluate the operational viability of a method based on the e-Learning Maturity Model developed at the University of…
Use of the 5E learning cycle model combined with problem-based learning for a fundamentals of nursing course.

PubMed

Jun, Won Hee; Lee, Eun Ju; Park, Han Jong; Chang, Ae Kyung; Kim, Mi Ja

2013-12-01

The 5E learning cycle model has shown a positive effect on student learning in science education, particularly in courses with theory and practice components. Combining problem-based learning (PBL) with the 5E learning cycle was suggested as a better option for students' learning of theory and practice. The purpose of this study was to compare the effects of the traditional learning method with the 5E learning cycle model with PBL. The control group (n = 78) was subjected to a learning method that consisted of lecture and practice. The experimental group (n = 83) learned by using the 5E learning cycle model with PBL. The results showed that the experimental group had significantly improved self-efficacy, critical thinking, learning attitude, and learning satisfaction. Such an approach could be used in other countries to enhance students' learning of fundamental nursing. Copyright 2013, SLACK Incorporated.
Using a Brief Form of Problem-Based Learning in a Research Methods Class: Perspectives of Instructor and Students

ERIC Educational Resources Information Center

Elder, Anastasia D.

2015-01-01

Problem based learning (PBL) is an instructional method aimed at engaging students in collaboratively solving an ill-structured problem. PBL has been presented and researched as an overhaul of existing curriculum design, yet a modified version may be attractive to college instructors who desire active learning on the part of their students, but…
An Intelligent Web-Based System for Diagnosing Student Learning Problems Using Concept Maps

ERIC Educational Resources Information Center

Acharya, Anal; Sinha, Devadatta

2017-01-01

The aim of this article is to propose a method for development of concept map in web-based environment for identifying concepts a student is deficient in after learning using traditional methods. Direct Hashing and Pruning algorithm was used to construct concept map. Redundancies within the concept map were removed to generate a learning sequence.…
Introduction and Evaluation of Case-Based Learning in the First Foundational Course of an Undergraduate Medical Curriculum

ERIC Educational Resources Information Center

Fortun, Jenny; Morales, Ana Cecilia; Tempest, Helen Ghislaine

2017-01-01

Case-based learning (CBL) has been proposed as an effective method to promote student knowledge and motivation. The timing and methods for implementation have varied among schools, and data regarding the effectiveness of this pedagogy compared to other learning modalities are inconclusive. We introduced five different cases in the first course of…
Interactive and collaborative learning in the classroom at the medical school Automated response systems and team-based learning.

PubMed

Nasr, Rihab; Antoun, Jumana; Sabra, Ramzi; Zgheib, Nathalie K

2016-01-01

There has been a pedagogic shift in higher education from the traditional teacher centered to the student centered approach in teaching, necessitating a change in the role of the teacher from a supplier of information to passive receptive students into a more facilitative role. Active learning activities are based on various learning theories such as self-directed learning, cooperative learning and adult learning. There exist many instructional activities that enhance active and collaborative learning. The aim of this manuscript is to describe two methods of interactive and collaborative learning in the classroom, automated response systems (ARS) and team-based learning (TBL), and to list some of their applications and advantages. The success of these innovative teaching and learning methods at a large scale depends on few elements, probably the most important of which is the support of the higher administration and leadership in addition to the availability of “champions” who are committed to lead the change.
Pedagogical effectiveness of innovative teaching methods initiated at the Department of Physiology, Government Medical College, Chandigarh.

PubMed

Nageswari, K Sri; Malhotra, Anita S; Kapoor, Nandini; Kaur, Gurjit

2004-12-01

Modern teaching trends in medical education exhibit a paradigm shift from the conventional classroom teaching methods adopted in the past to nonconventional teaching aids so as to encourage interactive forms of learning in medical students through active participation and integrative reasoning where the relationship of the teacher and the taught has undergone tremendous transformation. Some of the nonconventional teaching methods adopted at our department are learning through active participation by the students through computer-assisted learning (CD-ROMs), Web-based learning (undergraduate projects), virtual laboratories, seminars, audiovisual aids (video-based demonstrations), and "physioquiz."
Research and Development of Web-Based Virtual Online Classroom

ERIC Educational Resources Information Center

Yang, Zongkai; Liu, Qingtang

2007-01-01

To build a web-based virtual learning environment depends on information technologies, concerns technology supporting learning methods and theories. A web-based virtual online classroom is designed and developed based on learning theories and streaming media technologies. And it is composed of two parts: instructional communicating environment…
Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

NASA Astrophysics Data System (ADS)

Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

2016-03-01

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.
Impedance learning for robotic contact tasks using natural actor-critic algorithm.

PubMed

Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul

2010-04-01

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.
Project-Based Learning Involving Sensory Panelists Improves Student Learning Outcomes

ERIC Educational Resources Information Center

Lee, Yee Ming

2015-01-01

Project-based, collaborative learning is an effective teaching method when compared to traditional cognitive learning. The purpose of this study was to assess student learning after the completion of a final meal project that involved a group of sensory panelists. A paper survey was conducted among 73 senior nutrition and dietetics students…
Practice-Based Learning and Improvement: A Dream that Can Become a Reality

ERIC Educational Resources Information Center

Manning, Phil R.

2003-01-01

Systematically enhancing learning from experience (practice-based learning) dominates the teachings of Sir Willian Osler and adult learning theorists such as Eduard Lindeman, Malcolm Knowles, and Cyril Houle. Because of time constraints, most physicians have not implemented methods that systematically facilitate learning from day-to-day work, but…

An Inquiry-Based Approach to Teaching Research Methods in Information Studies

ERIC Educational Resources Information Center

Albright, Kendra; Petrulis, Robert; Vasconcelos, Ana; Wood, Jamie

2012-01-01

This paper presents the results of a project that aimed at restructuring the delivery of research methods training at the Information School at the University of Sheffield, UK, based on an Inquiry-Based Learning (IBL) approach. The purpose of this research was to implement inquiry-based learning that would allow customization of research methods…
www.teld.net: Online Courseware Engine for Teaching by Examples and Learning by Doing.

ERIC Educational Resources Information Center

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

2001-01-01

Describes TELD (Teaching by Examples and Learning by Doing), a Web-based online courseware engine for higher education. Topics include problem-based learning; project-based learning; case methods; TELD as a Web server; course materials; TELD as a search engine; and TELD as an online virtual classroom for electronic delivery of electronic…
Investigating the Impact of a LEGO(TM)-Based, Engineering-Oriented Curriculum Compared to an Inquiry-Based Curriculum on Fifth Graders' Content Learning of Simple Machines

ERIC Educational Resources Information Center

Marulcu, Ismail

2010-01-01

This mixed method study examined the impact of a LEGO-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. This study takes a social constructivist theoretical stance that science learning involves learning scientific concepts and their relations to each other. From…
Comparisons of Internet-Based and Face-to-Face Learning Systems Based on "Equivalency of Experiences" According to Students' Academic Achievements and Satisfactions

ERIC Educational Resources Information Center

Karatas, Sercin; Simsek, Nurettin

2009-01-01

The purpose of this study is to determine whether "equivalent learning experiences" ensure equivalency, in the Internet-based and face-to-face interaction methods on learning results and student satisfaction. In the experimental process of this study, the effect of the Internet-based and face-to-face learning on the equivalency in…
Promoting "Social and Emotional Learning" through Service-Learning Art Projects

ERIC Educational Resources Information Center

Russell, Robert L.; Hutzel, Karen

2007-01-01

This article intends to encourage teachers to explore ways "social and emotional learning" (SEL) and art education can enhance each other. Service-learning art projects were presented as one example, employing collaborate-and-create, asset-based methods integrated with SEL instruction. Advantages anticipated from combining these methods result…
Immersive Learning: Using a Web-Based Learning Tool in a PhD Course to Enhance the Learning Experience

ERIC Educational Resources Information Center

Ly, Samie Li Shang; Saadé, Raafat; Morin, Danielle

2017-01-01

Aim/Purpose: Teaching and learning is no longer the same and the paradigm shift has not settled yet. Information technology (IT) and its worldwide use impacts student learning methods and associated pedagogical models. Background: In this study we frame immersive learning as a method that we believe can be designed by pedagogical models such as…
DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Q; Han, H; Xing, L

Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, amore » conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning based method.« less
Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659
Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.
Usability Evaluation of a Web-Based Learning System

ERIC Educational Resources Information Center

Nguyen, Thao

2012-01-01

The paper proposes a contingent, learner-centred usability evaluation method and a prototype tool of such systems. This is a new usability evaluation method for web-based learning systems using a set of empirically-supported usability factors and can be done effectively with limited resources. During the evaluation process, the method allows for…
Problem-Based Learning Method: Secondary Education 10th Grade Chemistry Course Mixtures Topic

ERIC Educational Resources Information Center

Üce, Musa; Ates, Ismail

2016-01-01

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

PubMed

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-03-27

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K -nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.
Analyzing Interactions by an IIS-Map-Based Method in Face-to-Face Collaborative Learning: An Empirical Study

ERIC Educational Resources Information Center

Zheng, Lanqin; Yang, Kaicheng; Huang, Ronghuai

2012-01-01

This study proposes a new method named the IIS-map-based method for analyzing interactions in face-to-face collaborative learning settings. This analysis method is conducted in three steps: firstly, drawing an initial IIS-map according to collaborative tasks; secondly, coding and segmenting information flows into information items of IIS; thirdly,…
Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758
Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.
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.
Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

NASA Astrophysics Data System (ADS)

Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

2017-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.
Semantic-gap-oriented active learning for multilabel image annotation.

PubMed

Tang, Jinhui; Zha, Zheng-Jun; Tao, Dacheng; Chua, Tat-Seng

2012-04-01

User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process.
Is lecture dead? A preliminary study of medical students' evaluation of teaching methods in the preclinical curriculum.

PubMed

Zinski, Anne; Blackwell, Kristina T C Panizzi Woodley; Belue, F Mike; Brooks, William S

2017-09-22

To investigate medical students' perceptions of lecture and non-lecture-based instructional methods and compare preferences for use and quantity of each during preclinical training. We administered a survey to first- and second-year undergraduate medical students at the University of Alabama School of Medicine in Birmingham, Alabama, USA aimed to evaluate preferred instructional methods. Using a cross-sectional study design, Likert scale ratings and student rankings were used to determine preferences among lecture, laboratory, team-based learning, simulation, small group case-based learning, large group case-based learning, patient presentation, and peer teaching. We calculated mean ratings for each instructional method and used chi-square tests to compare proportions of first- and second-year cohorts who ranked each in their top 5 preferred methods. Among participating students, lecture (M=3.6, SD=1.0), team based learning (M=4.2, SD=1.0), simulation (M=4.0, SD=1.0), small group case-based learning (M=3.8, SD=1.0), laboratory (M=3.6, SD=1.0), and patient presentation (M=3.8, SD=0.9) received higher scores than other instructional methods. Overall, second-year students ranked lecture lower (χ 2 (1, N=120) =16.33, p<0.0001) and patient presentation higher (χ 2 (1, N=120) =3.75, p=0.05) than first-year students. While clinically-oriented teaching methods were preferred by second-year medical students, lecture-based instruction was popular among first-year students. Results warrant further investigation to determine the ideal balance of didactic methods in undergraduate medical education, specifically curricula that employ patient-oriented instruction during the second preclinical year.
Team-Based Learning Enhances Performance in Introductory Biology

ERIC Educational Resources Information Center

Carmichael, Jeffrey

2009-01-01

Given the problems associated with the traditional lecture method, the constraints associated with large classes, and the effectiveness of active learning, continued development and testing of efficient student-centered learning approaches are needed. This study explores the effectiveness of team-based learning (TBL) in a large-enrollment…

Enhanced teaching and student learning through a simulator-based course in chemical unit operations design

NASA Astrophysics Data System (ADS)

Ghasem, Nayef

2016-07-01

This paper illustrates a teaching technique used in computer applications in chemical engineering employed for designing various unit operation processes, where the students learn about unit operations by designing them. The aim of the course is not to teach design, but rather to teach the fundamentals and the function of unit operation processes through simulators. A case study presenting the teaching method was evaluated using student surveys and faculty assessments, which were designed to measure the quality and effectiveness of the teaching method. The results of the questionnaire conclusively demonstrate that this method is an extremely efficient way of teaching a simulator-based course. In addition to that, this teaching method can easily be generalised and used in other courses. A student's final mark is determined by a combination of in-class assessments conducted based on cooperative and peer learning, progress tests and a final exam. Results revealed that peer learning can improve the overall quality of student learning and enhance student understanding.
Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

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

2018-03-01

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

PubMed

Moore, Lesley

2010-09-01

Healthcare policy makers have advocated the introduction of work-based learning (WBL) methods to help improve care. Some healthcare professionals and academics do not support WBL, however, possibly because they experienced, and value, traditional learning methods. This article reports on one of the findings of a longitudinal evaluation of WBL among registered nurses and highlights the pivotal role that managers play in supporting WBL.
Small Private Online Research: A Proposal for A Numerical Methods Course Based on Technology Use and Blended Learning

ERIC Educational Resources Information Center

Cepeda, Francisco Javier Delgado

2017-01-01

This work presents a proposed model in blended learning for a numerical methods course evolved from traditional teaching into a research lab in scientific visualization. The blended learning approach sets a differentiated and flexible scheme based on a mobile setup and face to face sessions centered on a net of research challenges. Model is…
Problem-Based Learning Approaches in Meteorology

ERIC Educational Resources Information Center

Charlton-Perez, Andrew James

2013-01-01

Problem-Based Learning, despite recent controversies about its effectiveness, is used extensively as a teaching method throughout higher education. In meteorology, there has been little attempt to incorporate Problem-Based Learning techniques into the curriculum. Motivated by a desire to enhance the reflective engagement of students within a…
Ontology-Based Adaptive Dynamic e-Learning Map Planning Method for Conceptual Knowledge Learning

ERIC Educational Resources Information Center

Chen, Tsung-Yi; Chu, Hui-Chuan; Chen, Yuh-Min; Su, Kuan-Chun

2016-01-01

E-learning improves the shareability and reusability of knowledge, and surpasses the constraints of time and space to achieve remote asynchronous learning. Since the depth of learning content often varies, it is thus often difficult to adjust materials based on the individual levels of learners. Therefore, this study develops an ontology-based…
Students' perceptions of a blended learning experience in dental education.

PubMed

Varthis, S; Anderson, O R

2018-02-01

"Flipped" instructional sequencing is a new instructional method where online instruction precedes the group meeting, allowing for more sophisticated learning through discussion and critical thinking during the in-person class session; a novel approach studied in this research. The purpose of this study was to document dental students' perceptions of flipped-based blended learning and to apply a new method of displaying their perceptions based on Likert-scale data analysis using a network diagramming method known as an item correlation network diagram (ICND). In addition, this article aimed to encourage institutions or course directors to consider self-regulated learning and social constructivism as a theoretical framework when blended learning is incorporated in dental curricula. Twenty (second year) dental students at a Northeastern Regional Dental School in the United States participated in this study. A Likert scale was administered before and after the learning experience to obtain evidence of their perceptions of its quality and educational merits. Item correlation network diagrams, based on the intercorrelations amongst the responses to the Likert-scale items, were constructed to display students' changes in perceptions before and after the learning experience. Students reported positive perceptions of the blended learning, and the ICND analysis of their responses before and after the learning experience provided insights into their social (group-based) cognition about the learning experience. The ICNDs are considered evidence of social or group-based cognition, because they are constructed from evidence obtained using intercorrelations of the total group responses to the Likert-scale items. The students positively received blended learning in dental education, and the ICND analyses demonstrated marked changes in their social cognition of the learning experience based on the pre- and post-Likert survey data. Self-regulated learning and social constructivism are encouraged as useful theoretical frameworks for a blended learning approach. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Rule-based mechanisms of learning for intelligent adaptive flight control

NASA Technical Reports Server (NTRS)

Handelman, David A.; Stengel, Robert F.

1990-01-01

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.
A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.
Evaluating team-based, lecture-based, and hybrid learning methods for neurology clerkship in China: a method-comparison study

PubMed Central

2014-01-01

Background Neurology is complex, abstract, and difficult for students to learn. However, a good learning method for neurology clerkship training is required to help students quickly develop strong clinical thinking as well as problem-solving skills. Both the traditional lecture-based learning (LBL) and the relatively new team-based learning (TBL) methods have inherent strengths and weaknesses when applied to neurology clerkship education. However, the strengths of each method may complement the weaknesses of the other. Combining TBL with LBL may produce better learning outcomes than TBL or LBL alone. We propose a hybrid method (TBL + LBL) and designed an experiment to compare the learning outcomes with those of pure LBL and pure TBL. Methods One hundred twenty-seven fourth-year medical students attended a two-week neurology clerkship program organized by the Department of Neurology, Sun Yat-Sen Memorial Hospital. All of the students were from Grade 2007, Department of Clinical Medicine, Zhongshan School of Medicine, Sun Yat-Sen University. These students were assigned to one of three groups randomly: Group A (TBL + LBL, with 41 students), Group B (LBL, with 43 students), and Group C (TBL, with 43 students). The learning outcomes were evaluated by a questionnaire and two tests covering basic knowledge of neurology and clinical practice. Results The practice test scores of Group A were similar to those of Group B, but significantly higher than those of Group C. The theoretical test scores and the total scores of Group A were significantly higher than those of Groups B and C. In addition, 100% of the students in Group A were satisfied with the combination of TBL + LBL. Conclusions Our results support our proposal that the combination of TBL + LBL is acceptable to students and produces better learning outcomes than either method alone in neurology clerkships. In addition, the proposed hybrid method may also be suited for other medical clerkships that require students to absorb a large amount of abstract and complex course materials in a short period, such as pediatrics and internal medicine clerkships. PMID:24884854
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

PubMed

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

PubMed Central

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242
Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.
Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Addressing Cultural Diversity: Effects of a Problem-Based Intercultural Learning Unit

ERIC Educational Resources Information Center

Busse, Vera; Krause, Ulrike-Marie

2015-01-01

This article explores to what extent a problem-based learning unit in combination with cooperative learning and affectively oriented teaching methods facilitates intercultural learning. As part of the study, students reflected on critical incidents, which display misunderstandings or conflicts that arise as a result of cultural differences. In…
A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.
A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

PubMed

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.
A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability.

PubMed

Yeung, Dit-Yan; Chang, Hong; Dai, Guang

2008-11-01

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
Lecture-based versus problem-based learning in ethics education among nursing students.

PubMed

Khatiban, Mahnaz; Falahan, Seyede Nayereh; Amini, Roya; Farahanchi, Afshin; Soltanian, Alireza

2018-01-01

Moral reasoning is a vital skill in the nursing profession. Teaching moral reasoning to students is necessary toward promoting nursing ethics. The aim of this study was to compare the effectiveness of problem-based learning and lecture-based methods in ethics education in improving (1) moral decision-making, (2) moral reasoning, (3) moral development, and (4) practical reasoning among nursing students. This is a repeated measurement quasi-experimental study. Participants and research context: The participants were nursing students in a University of Medical Sciences in west of Iran who were randomly assigned to the lecture-based (n = 33) or the problem-based learning (n = 33) groups. The subjects were provided nursing ethics education in four 2-h sessions. The educational content was similar, but the training methods were different. The subjects completed the Nursing Dilemma Test before, immediately after, and 1 month after the training. The data were analyzed and compared using the SPSS-16 software. Ethical considerations: The program was explained to the students, all of whom signed an informed consent form at the baseline. The two groups were similar in personal characteristics (p > 0.05). A significant improvement was observed in the mean scores on moral development in the problem-based learning compared with the lecture-based group (p < 0.05). Although the mean scores on moral reasoning improved in both the problem-based learning and the lecture-based groups immediately after the training and 1 month later, the change was significant only in the problem-based learning group (p < 0.05). The mean scores on moral decision-making, practical considerations, and familiarity with dilemmas were relatively similar for the two groups. The use of the problem-based learning method in ethics education enhances moral development among nursing students. However, further studies are needed to determine whether such method improves moral decision-making, moral reasoning, practical considerations, and familiarity with the ethical issues among nursing students.
Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Examination of Pre-Service Science Teachers' Activities Using Problem Based Learning Method

ERIC Educational Resources Information Center

Ekici, Didem Inel

2016-01-01

In this study, both the activities prepared by pre-service science teachers regarding the Problem Based Learning method and the pre-service science teachers' views regarding the method were examined before and after applying their activities in a real class environment. 69 pre-service science teachers studying in the 4th grade of the science…
Developing an Efficient Computational Method that Estimates the Ability of Students in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Lee, Young-Jin

2012-01-01

This paper presents a computational method that can efficiently estimate the ability of students from the log files of a Web-based learning environment capturing their problem solving processes. The computational method developed in this study approximates the posterior distribution of the student's ability obtained from the conventional Bayes…
Thai Undergraduate Chemistry Practical Learning Experiences Using the Jigsaw IV Method

ERIC Educational Resources Information Center

Jansoon, Ninna; Somsook, Ekasith; Coll, Richard K.

2008-01-01

The research reported in this study consisted of an investigation of student learning experiences in Thai chemistry laboratories using the Jigsaw IV method. A hands-on experiment based on the Jigsaw IV method using a real life example based on green tea beverage was designed to improve student affective variables for studying topics related to…
Case-Based Web Learning Versus Face-to-Face Learning: A Mixed-Method Study on University Nursing Students.

PubMed

Chan, Aileen Wai-Kiu; Chair, Sek-Ying; Sit, Janet Wing-Hung; Wong, Eliza Mi-Ling; Lee, Diana Tze-Fun; Fung, Olivia Wai-Man

2016-03-01

Case-based learning (CBL) is an effective educational method for improving the learning and clinical reasoning skills of students. Advances in e-learning technology have supported the development of the Web-based CBL approach to teaching as an alternative or supplement to the traditional classroom approach. This study aims to examine the CBL experience of Hong Kong students using both traditional classroom and Web-based approaches in undergraduate nursing education. This experience is examined in terms of the perceived self-learning ability, clinical reasoning ability, and satisfaction in learning of these students. A mixture of quantitative and qualitative approaches was adopted. All Year-3 undergraduate nursing students were recruited. CBL was conducted using the traditional classroom approach in Semester 1, and the Web-based approach was conducted in Semester 2. Student evaluations were collected at the end of each semester using a self-report questionnaire. In-depth, focus-group interviews were conducted at the end of Semester 2. One hundred twenty-two students returned their questionnaires. No difference between the face-to-face and Web-based approaches was found in terms of self-learning ability (p = .947), clinical reasoning ability (p = .721), and satisfaction (p = .083). Focus group interview findings complemented survey findings and revealed five themes that reflected the CBL learning experience of Hong Kong students. These themes were (a) the structure of CBL, (b) the learning environment of Web-based CBL, (c) critical thinking and problem solving, (d) cultural influence on CBL learning experience, and (e) student-centered and teacher-centered learning. The Web-based CBL approach was comparable but not superior to the traditional classroom CBL approach. The Web-based CBL experience of these students sheds light on the impact of Chinese culture on student learning behavior and preferences.
Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

PubMed

Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

2017-04-01

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Practice-based learning and improvement: a dream that can become a reality.

PubMed

Manning, Phil R

2003-01-01

Systematically enhancing learning from experience (practice-based learning) dominates the teachings of Sir William Osler and adult learning theorists such as Eduard Lindeman, Malcolm Knowles, and Cyril Houle. Because of time constraints, most physicians have not implemented methods that systematically facilitate learning from day-to-day work, but improvements in information technology offer the promise of making systematic practice-based learning practical. At least four ingredients need to be incorporated to significantly enhance learning from experience: a database that makes it possible to study individual practices; methods for supplying short, quick answers to questions while seeing patients; a reminder system to avoid errors of omission; and the opportunity to discuss practice data with colleagues. Great progress has been made, but significant barriers still must be overcome before a majority of physicians will participate. In particular, methods of data collection must be simplified, the delivery of point-of-care information and reminders must become more automatic, and physicians must develop skills to make the discussion of practice data acceptable, stimulating, and not unduly punitive.
Case study of a problem-based learning course of physics in a telecommunications engineering degree

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

ERIC Educational Resources Information Center

Ching, Yu-Hui; Hsu, Yu-Chang

2013-01-01

There has been limited research examining the pedagogical benefits of peer feedback for facilitating project-based learning in an online environment. Using a mixed method approach, this paper examines graduate students' participation and perceptions of peer feedback activity that supports project-based learning in an online instructional design…
A comparison of problem-based learning and conventional teaching in nursing ethics education.

PubMed

Lin, Chiou-Fen; Lu, Meei-Shiow; Chung, Chun-Chih; Yang, Che-Ming

2010-05-01

The aim of this study was to compare the learning effectiveness of peer tutored problem-based learning and conventional teaching of nursing ethics in Taiwan. The study adopted an experimental design. The peer tutored problem-based learning method was applied to an experimental group and the conventional teaching method to a control group. The study sample consisted of 142 senior nursing students who were randomly assigned to the two groups. All the students were tested for their nursing ethical discrimination ability both before and after the educational intervention. A learning satisfaction survey was also administered to both groups at the end of each course. After the intervention, both groups showed a significant increase in ethical discrimination ability. There was a statistically significant difference between the ethical discrimination scores of the two groups (P < 0.05), with the experimental group on average scoring higher than the control group. There were significant differences in satisfaction with self-motivated learning and critical thinking between the groups. Peer tutored problem-based learning and lecture-type conventional teaching were both effective for nursing ethics education, but problem-based learning was shown to be more effective. Peer tutored problem-based learning has the potential to enhance the efficacy of teaching nursing ethics in situations in which there are personnel and resource constraints.
Examining the Implementation of a Problem-Based Learning and Traditional Hybrid Model of Instruction in Remedial Mathematics Classes Designed for State Testing Preparation of Eleventh Grade Students

ERIC Educational Resources Information Center

Rodgers, Lindsay D.

2011-01-01

The following paper examined the effects of a new method of teaching for remedial mathematics, named the hybrid model of instruction. Due to increasing importance of high stakes testing, the study sought to determine if this method of instruction, that blends traditional teaching and problem-based learning, had different learning effects on…
Flipped Learning With Simulation in Undergraduate Nursing Education.

PubMed

Kim, HeaRan; Jang, YounKyoung

2017-06-01

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

ERIC Educational Resources Information Center

Raiyn, Jamal; Tilchin, Oleg

2016-01-01

The aim of this paper is to present "the three steps method" of the self-formation of collaborative groups in a problem-based learning environment. The self-formation of collaborative groups is based on sharing of accountability among students for solving instructional problems. The steps of the method are planning collaborative problem…
Evaluation of Intelligent Grouping Based on Learners' Collaboration Competence Level in Online Collaborative Learning Environment

ERIC Educational Resources Information Center

Muuro, Maina Elizaphan; Oboko, Robert; Wagacha, Waiganjo Peter

2016-01-01

In this paper we explore the impact of an intelligent grouping algorithm based on learners' collaborative competency when compared with (a) instructor based Grade Point Average (GPA) method level and (b) random method, on group outcomes and group collaboration problems in an online collaborative learning environment. An intelligent grouping…
Impact of Problem-Based Learning to Students and Teachers

ERIC Educational Resources Information Center

Hirca, Necati

2011-01-01

The Ministry of National Education of Turkey has decided to give up traditional methods to be used in the classes and to develop a new secondary school curriculum based on Context-Based Learning (CBL) in 2007. This paper discusses integrating Problem-Based Learning (PBL) tasks into the new physics curriculum in Turkey. A brief overview of a…
Learning Strategies for Success in a Web-Based Course: A Descriptive Exploration

ERIC Educational Resources Information Center

Hu, Haihong; Gramling, Jennifer

2009-01-01

Web-based distance instruction has become a popular delivery method for education. How are learning strategies helping make the connection between Web-based technologies and educational goals? The purpose of this study was to examine learners' use of self-regulated learning strategies in a Web-based course. Twelve students from an information…
Coupled dictionary learning for joint MR image restoration and segmentation

NASA Astrophysics Data System (ADS)

Yang, Xuesong; Fan, Yong

2018-03-01

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

PubMed

Merisier, Sophia; Larue, Caroline; Boyer, Louise

2018-06-01

Problem-based learning is an educational method promoting clinical reasoning that has been implemented in many fields of health education. Questioning is a learning strategy often employed in problem-based learning sessions. To explore what is known about the influence of questioning on the promotion of clinical reasoning of students in health care education, specifically in the field of nursing and using the educational method of problem-based learning. A scoping review following Arksey and O'Malley's five stages was conducted. The CINAHL, EMBASE, ERIC, Medline, and PubMed databases were searched for articles published between the years of 2000 and 2017. Each article was summarized and analyzed using a data extraction sheet in relation to its purpose, population group, setting, methods, and results. A descriptive explication of the studies based on an inductive analysis of their findings to address the aim of the review was made. Nineteen studies were included in the analysis. The studies explored the influence of questioning on critical thinking rather than on clinical reasoning. The nature of the questions asked and the effect of higher-order questions on critical thinking were the most commonly occurring themes. Few studies addressed the use of questioning in problem-based learning. More empirical evidence is needed to gain a better understanding of the benefit of questioning in problem-based learning to promote students' clinical reasoning. Copyright © 2018 Elsevier Ltd. All rights reserved.
Meta Cognition as a Means for Dialogue, Self Regulation and Learning--A Case Study from an Implementation of Problem-Based Learning

ERIC Educational Resources Information Center

Venkatachary, Ranga; Kumar, Muthu

2005-01-01

One of the key arguments for problem-based learning as a holistic, learner centred pedagogical method rests on the premise it addresses multiple facets of learner development rather than decontextualised, content related learning outcomes. Fostering meta-cognitive ability in an attempt to develop self regulatory, autonomous learning habits is an…
Construction and Evaluation of an Integrated Formal/Informal Learning Environment for Foreign Language Learning across Real and Virtual Spaces

ERIC Educational Resources Information Center

Waragai, Ikumi; Ohta, Tatsuya; Kurabayashi, Shuichi; Kiyoki, Yasushi; Sato, Yukiko; Brückner, Stefan

2017-01-01

This paper presents the prototype of a foreign language learning space, based on the construction of an integrated formal/informal learning environment. Before the background of the continued innovation of information technology that places conventional learning styles and educational methods into new contexts based on new value-standards,…
Generational Learning Style Preferences Based on Computer-Based Healthcare Training

ERIC Educational Resources Information Center

Knight, Michaelle H.

2016-01-01

Purpose. The purpose of this mixed-method study was to determine the degree of perceived differences for auditory, visual and kinesthetic learning styles of Traditionalist, Baby Boomers, Generation X and Millennial generational healthcare workers participating in technology-assisted healthcare training. Methodology. This mixed-method research…

A Meta-Analysis Method to Advance Design of Technology-Based Learning Tool: Combining Qualitative and Quantitative Research to Understand Learning in Relation to Different Technology Features

ERIC Educational Resources Information Center

Zhang, Lin

2014-01-01

Educators design and create various technology tools to scaffold students' learning. As more and more technology designs are incorporated into learning, growing attention has been paid to the study of technology-based learning tool. This paper discusses the emerging issues, such as how can learning effectiveness be understood in relation to…
Realization of Deflection-type Bridge instruments to determine soil moisture using Research-Based Learning

NASA Astrophysics Data System (ADS)

Yuliza, E.; Munir, M. M.; Abdullah, M.; Khairurrijal

2016-08-01

It is clear that the quality of education is directly related to the quality of teachers and the teaching methods. One of the teaching methods that can improve the quality of education is research-based learning (RBL) method. In this method, students act as the center of learning while teachers become the guides that provide direction and advice. RBL is a learning method that combines cooperative learning, problem solving, authentic learning, contextual case study and inquiry approach discovery. The main goal of this method is to construct a student that can think critically, analyze and evaluate problems, and find a new science from these problems (learning by doing). In this paper, RBL is used to improve the understanding about measurement using deflection-type Bridge that is implemented in the determination of ground water changes. In general, there are three stages that have been done. Firstly the exposure stage, then the experience stage and lastly the capstone stage. The exposure stage aims to increase the knowledge and the comprehension of student about the topic through understanding the basics concepts, reviewing the literature and others. The understanding gained in the exposure stage is being used for application and analysis at the experience stage. While the final stage is the publication of research results both verbally and in writing. Based on the steps that have been conducted, it can be showed that deflection-type Bridge can be utilized in soil moisture meter.
[Verification of Learning Effects by Team-based Learning].

PubMed

Ono, Shin-Ichi; Ito, Yoshihisa; Ishige, Kumiko; Inokuchi, Norio; Kosuge, Yasuhiro; Asami, Satoru; Izumisawa, Megumi; Kobayashi, Hiroko; Hayashi, Hiroyuki; Suzuki, Takashi; Kishikawa, Yukinaga; Hata, Harumi; Kose, Eiji; Tabata, Kei-Ichi

2017-11-01

It has been recommended that active learning methods, such as team-based learning (TBL) and problem-based learning (PBL), be introduced into university classes by the Central Council for Education. As such, for the past 3 years, we have implemented TBL in a medical therapeutics course for 4-year students. Based upon our experience, TBL is characterized as follows: TBL needs fewer teachers than PBL to conduct a TBL module. TBL enables both students and teachers to recognize and confirm the learning results from preparation and reviewing. TBL grows students' responsibility for themselves and their teams, and likely facilitates learning activities through peer assessment.
Why Problem-Based Learning Works: Theoretical Foundations

ERIC Educational Resources Information Center

Marra, Rose M.; Jonassen, David H.; Palmer, Betsy; Luft, Steve

2014-01-01

Problem-based learning (PBL) is an instructional method where student learning occurs in the context of solving an authentic problem. PBL was initially developed out of an instructional need to help medical school students learn their basic sciences knowledge in a way that would be more lasting while helping to develop clinical skills…
Implementation of Project Based Learning in Mechatronic Lab Course at Bandung State Polytechnic

ERIC Educational Resources Information Center

Basjaruddin, Noor Cholis; Rakhman, Edi

2016-01-01

Mechatronics is a multidisciplinary that includes a combination of mechanics, electronics, control systems, and computer science. The main objective of mechatronics learning is to establish a comprehensive mindset in the development of mechatronic systems. Project Based Learning (PBL) is an appropriate method for use in the learning process of…
[Public Health as an Applied, Multidisciplinary Subject: Is Research-Based Learning the Answer to Challenges in Learning and Teaching?

PubMed

Gerhardus, A; Schilling, I; Voss, M

2017-03-01

Public health education aims at enabling students to deal with complex health-related challenges using appropriate methods based on sound theoretical understanding. Virtually all health-related problems in science and practice require the involvement of different disciplines. However, the necessary interdisciplinarity is only partly reflected in the curricula of public health courses. Also theories, methods, health topics, and their application are often taught side-by-side and not together. For students, it can become an insurmountable challenge to integrate the different disciplines ("horizontal integration") and theories, methods, health topics, and their application ("vertical integration"). This situation is specific for education in public health but is representative for other interdisciplinary fields as well. Several approaches are available to achieve the horizontal integration of different disciplines and vertical integration of theories, methods, health topics, and their application. A curriculum that is structured by topics, rather than disciplines might be more successful in integrating different disciplines. Vertical integration can be achieved by research-based learning. Research-based learning places a student-led research project at the centre of teaching. Students choose a topic and a research question, raise their own questions for theories and methods and will hopefully cross the seeming chasm between science and practice. Challenges of research-based learning are enhanced demands on students, teachers and curriculum design. © Georg Thieme Verlag KG Stuttgart · New York.
Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

PubMed

Andrusyszyn, M A; Cragg, C E; Humbert, J

2001-04-01

The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.
A New Approach for Laboratory Exercise of Pathophysiology in China Based on Student-Centered Learning

ERIC Educational Resources Information Center

Chen, Jian; Zhou, Junhai; Sun, Li; Wu, Qiuhui; Lu, Huiling; Tian, Jing

2015-01-01

Student-centered learning is generally defined as any instructional method that purportedly engages students in active learning and critical thinking. The student-centered method of teaching moves the focus from teaching to learning, from the teachers' conveying course concepts via lecture to the understanding of concepts by students. The…
Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

ERIC Educational Resources Information Center

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

2014-01-01

Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…
Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.
Triangular model integrating clinical teaching and assessment

PubMed Central

Abdelaziz, Adel; Koshak, Emad

2014-01-01

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

PubMed

Abdelaziz, Adel; Koshak, Emad

2014-01-01

Structuring clinical teaching is a challenge facing medical education curriculum designers. A variety of instructional methods on different domains of learning are indicated to accommodate different learning styles. Conventional methods of clinical teaching, like training in ambulatory care settings, are prone to the factor of coincidence in having varieties of patient presentations. Accordingly, alternative methods of instruction are indicated to compensate for the deficiencies of these conventional methods. This paper presents an initiative that can be used to design a checklist as a blueprint to guide appropriate selection and implementation of teaching/learning and assessment methods in each of the educational courses and modules based on educational objectives. Three categories of instructional methods were identified, and within each a variety of methods were included. These categories are classroom-type settings, health services-based settings, and community service-based settings. Such categories have framed our triangular model of clinical teaching and assessment.
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

NASA Astrophysics Data System (ADS)

Lotte, F.; Bougrain, L.; Cichocki, A.; Clerc, M.; Congedo, M.; Rakotomamonjy, A.; Yger, F.

2018-06-01

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach. We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results. We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance. This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.
Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

PubMed Central

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-01-01

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. PMID:28346385
Associations between Verbal Learning Slope and Neuroimaging Markers across the Cognitive Aging Spectrum.

PubMed

Gifford, Katherine A; Phillips, Jeffrey S; Samuels, Lauren R; Lane, Elizabeth M; Bell, Susan P; Liu, Dandan; Hohman, Timothy J; Romano, Raymond R; Fritzsche, Laura R; Lu, Zengqi; Jefferson, Angela L

2015-07-01

A symptom of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is a flat learning profile. Learning slope calculation methods vary, and the optimal method for capturing neuroanatomical changes associated with MCI and early AD pathology is unclear. This study cross-sectionally compared four different learning slope measures from the Rey Auditory Verbal Learning Test (simple slope, regression-based slope, two-slope method, peak slope) to structural neuroimaging markers of early AD neurodegeneration (hippocampal volume, cortical thickness in parahippocampal gyrus, precuneus, and lateral prefrontal cortex) across the cognitive aging spectrum [normal control (NC); (n=198; age=76±5), MCI (n=370; age=75±7), and AD (n=171; age=76±7)] in ADNI. Within diagnostic group, general linear models related slope methods individually to neuroimaging variables, adjusting for age, sex, education, and APOE4 status. Among MCI, better learning performance on simple slope, regression-based slope, and late slope (Trial 2-5) from the two-slope method related to larger parahippocampal thickness (all p-values<.01) and hippocampal volume (p<.01). Better regression-based slope (p<.01) and late slope (p<.01) were related to larger ventrolateral prefrontal cortex in MCI. No significant associations emerged between any slope and neuroimaging variables for NC (p-values ≥.05) or AD (p-values ≥.02). Better learning performances related to larger medial temporal lobe (i.e., hippocampal volume, parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCI only. Regression-based and late slope were most highly correlated with neuroimaging markers and explained more variance above and beyond other common memory indices, such as total learning. Simple slope may offer an acceptable alternative given its ease of calculation.
The Effect of Inquiry-Based Learning Method on Students' Academic Achievement in Science Course

ERIC Educational Resources Information Center

Abdi, Ali

2014-01-01

The purpose of this study was to investigate the effects of inquiry-based learning method on students' academic achievement in sciences lesson. A total of 40 fifth grade students from two different classes were involved in the study. They were selected through purposive sampling method. The group which was assigned as experimental group was…
The Effects of Using Jigsaw Method Based on Cooperative Learning Model in the Undergraduate Science Laboratory Practices

ERIC Educational Resources Information Center

Karacop, Ataman

2017-01-01

The main aim of the present study is to determine the influence of a Jigsaw method based on cooperative learning and a confirmatory laboratory method on prospective science teachers' achievements of physics in science teaching laboratory practice courses. The sample of this study consisted of 33 female and 15 male third-grade prospective science…
Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.
Students' satisfaction to hybrid problem-based learning format for basic life support/advanced cardiac life support teaching.

PubMed

Chilkoti, Geetanjali; Mohta, Medha; Wadhwa, Rachna; Saxena, Ashok Kumar; Sharma, Chhavi Sarabpreet; Shankar, Neelima

2016-11-01

Students are exposed to basic life support (BLS) and advanced cardiac life support (ACLS) training in the first semester in some medical colleges. The aim of this study was to compare students' satisfaction between lecture-based traditional method and hybrid problem-based learning (PBL) in BLS/ACLS teaching to undergraduate medical students. We conducted a questionnaire-based, cross-sectional survey among 118 1 st -year medical students from a university medical college in the city of New Delhi, India. We aimed to assess the students' satisfaction between lecture-based and hybrid-PBL method in BLS/ACLS teaching. Likert 5-point scale was used to assess students' satisfaction levels between the two teaching methods. Data were collected and scores regarding the students' satisfaction levels between these two teaching methods were analysed using a two-sided paired t -test. Most students preferred hybrid-PBL format over traditional lecture-based method in the following four aspects; learning and understanding, interest and motivation, training of personal abilities and being confident and satisfied with the teaching method ( P < 0.05). Implementation of hybrid-PBL format along with the lecture-based method in BLS/ACLS teaching provided high satisfaction among undergraduate medical students.
Using a collaborative Mobile Augmented Reality learning application (CoMARLA) to improve Improve Student Learning

NASA Astrophysics Data System (ADS)

Hanafi, Hafizul Fahri bin; Soh Said, Che; Hanee Ariffin, Asma; Azlan Zainuddin, Nur; Samsuddin, Khairulanuar

2016-11-01

This study was carried out to improve student learning in ICT course using a collaborative mobile augmented reality learning application (CoMARLA). This learning application was developed based on the constructivist framework that would engender collaborative learning environment, in which students could learn collaboratively using their mobile phones. The research design was based on the pretest posttest control group design. The dependent variable was students’ learning performance after learning, and the independent variables were learning method and gender. Students’ learning performance before learning was treated as the covariate. The sample of the study comprised 120 non-IT (non-technical) undergraduates, with the mean age of 19.5. They were randomized into two groups, namely the experimental and control group. The experimental group used CoMARLA to learn one of the topics of the ICT Literacy course, namely Computer System; whereas the control group learned using the conventional approach. The research instrument used was a set of multiple-choice questions pertaining to the above topic. Pretesting was carried out before the learning sessions, and posttesting was performed after 6 hours of learning. Using the SPSS, Analysis of Covariance (ANCOVA) was performed on the data. The analysis showed that there were main effects attributed to the learning method and gender. The experimental group outperformed the control group by almost 9%, and male students outstripped their opposite counterparts by as much as 3%. Furthermore, an interaction effect was also observed showing differential performances of male students based on the learning methods, which did not occur among female students. Hence, the tool can be used to help undergraduates learn with greater efficacy when contextualized in an appropriate setting.

Hard exudates segmentation based on learned initial seeds and iterative graph cut.

PubMed

Kusakunniran, Worapan; Wu, Qiang; Ritthipravat, Panrasee; Zhang, Jian

2018-05-01

(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same. Copyright © 2018 Elsevier B.V. All rights reserved.
Teaching for Engagement: Part 2: Technology in the Service of Active Learning

ERIC Educational Resources Information Center

Hunter, William J.

2015-01-01

In the first piece in this series ("Teaching for Engagement: Part 1: Constructivist Principles, Case-Based Teaching, and Active Learning"), William Hunter sought to make the case that a wide range of teaching methods (e.g., case-based teaching, problem-based learning, anchored instruction) that share an intellectual grounding in…
Version Control in Project-Based Learning

ERIC Educational Resources Information Center

Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

2008-01-01

This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…
The Effect of Virtual versus Traditional Learning in Achieving Competency-Based Skills

ERIC Educational Resources Information Center

Mosalanejad, Leili; Shahsavari, Sakine; Sobhanian, Saeed; Dastpak, Mehdi

2012-01-01

Background: By rapid developing of the network technology, the internet-based learning methods are substituting the traditional classrooms making them expand to the virtual network learning environment. The purpose of this study was to determine the effectiveness of virtual systems on competency-based skills of first-year nursing students.…
Dimensions of Problem Based Learning--Dialogue and Online Collaboration in Projects

ERIC Educational Resources Information Center

Andreasen,, Lars Birch; Nielsen, Jørgen Lerche

2013-01-01

The article contributes to the discussions on problem based learning and project work, building on and reflecting the experiences of the authors. Four perspectives are emphasized as central to a contemporary approach to problem- and project-based learning: the exploration of problems, projects as a method, online collaboration, and the dialogic…
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…
A Study of Student Engagement in Project-Based Learning across Multiple Approaches to STEM Education Programs

ERIC Educational Resources Information Center

Hall, Alfred; Miro, Danielle

2016-01-01

Objective: In this study, we investigated the implementation of project-based learning (PBL) activities in four secondary science, technology, engineering, and mathematics (STEM) education settings to examine the impact of inquiry based instructional practices on student learning. Method: Direct classroom observations were conducted during the…
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.
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
Teaching/Learning Methods and Students' Classification of Food Items

ERIC Educational Resources Information Center

Hamilton-Ekeke, Joy-Telu; Thomas, Malcolm

2011-01-01

Purpose: This study aims to investigate the effectiveness of a teaching method (TLS (Teaching/Learning Sequence)) based on a social constructivist paradigm on students' conceptualisation of classification of food. Design/methodology/approach: The study compared the TLS model developed by the researcher based on the social constructivist paradigm…
Enhancing Learning Outcomes in Computer-Based Training via Self-Generated Elaboration

ERIC Educational Resources Information Center

Cuevas, Haydee M.; Fiore, Stephen M.

2014-01-01

The present study investigated the utility of an instructional strategy known as the "query method" for enhancing learning outcomes in computer-based training. The query method involves an embedded guided, sentence generation task requiring elaboration of key concepts in the training material that encourages learners to "stop and…
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.
Quantitative Evaluation of Third Year Medical Students' Perception and Satisfaction from Problem Based Learning in Anatomy: A Pilot Study of the Introduction of Problem Based Learning into the Traditional Didactic Medical Curriculum in Nigeria

ERIC Educational Resources Information Center

Saalu, L. C.; Abraham A. A.; Aina, W. O.

2010-01-01

Problem-based learning (PBL) is a method of teaching that uses hypothetical clinical cases, individual investigation and group process. In recent years, in medical education, problem-based learning (PBL) has increasingly been adopted as the preferred pedagogy in many countries around the world. Controversy, however, still exists as the potential…
Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

NASA Astrophysics Data System (ADS)

Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

2017-12-01

The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3
Argument Based Science Inquiry (ABSI) Learning Model in Voltaic Cell Concept

NASA Astrophysics Data System (ADS)

Subarkah, C. Z.; Fadilah, A.; Aisyah, R.

2017-09-01

Voltaic Cell is a sub-concept of electrochemistry that is considered difficult to be comprehended by learners Voltaic Cell is a sub concept of electrochemistry that is considered difficult to be understood by learners so that impacts on student activity in learning process. Therefore the learning model Argument Based Science Inquiry (ABSI) will be applied to the concept of Voltaic cell. This research aims to describe students’ activities during learning process using ABSI model and to analyze students’ competency to solve ABSI-based worksheets (LK) of Voltaic Cell concept. The method used in this research was the “mix-method-quantitative-embedded” method with subjects of the study: 39 second-semester students of Chemistry Education study program. The student activity is quite good during ABSI learning. The students’ ability to complete worksheet (LK) for every average phase is good. In the phase of exploration of post instruction understanding, it is categorized very good, and in the phase of negotiation shape III: comparing science ideas to textbooks or other printed resources merely reach enough category. Thus, the ABSI learning has improved the student levels of activity and students’ competency to solve the ABSI-based worksheet (LK).
On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.
The Adolescent Mentalization-based Integrative Treatment (AMBIT) approach to outcome evaluation and manualization: adopting a learning organization approach.

PubMed

Fuggle, Peter; Bevington, Dickon; Cracknell, Liz; Hanley, James; Hare, Suzanne; Lincoln, John; Richardson, Garry; Stevens, Nina; Tovey, Heather; Zlotowitz, Sally

2015-07-01

AMBIT (Adolescent Mentalization-Based Integrative Treatment) is a developing team approach to working with hard-to-reach adolescents. The approach applies the principle of mentalization to relationships with clients, team relationships and working across agencies. It places a high priority on the need for locally developed evidence-based practice, and proposes that outcome evaluation needs to be explicitly linked with processes of team learning using a learning organization framework. A number of innovative methods of team learning are incorporated into the AMBIT approach, particularly a system of web-based wiki-formatted AMBIT manuals individualized for each participating team. The paper describes early development work of the model and illustrates ways of establishing explicit links between outcome evaluation, team learning and manualization by describing these methods as applied to two AMBIT-trained teams; one team working with young people on the edge of care (AMASS - the Adolescent Multi-Agency Support Service) and another working with substance use (CASUS - Child and Adolescent Substance Use Service in Cambridgeshire). Measurement of the primary outcomes for each team (which were generally very positive) facilitated team learning and adaptations of methods of practice that were consolidated through manualization. © The Author(s) 2014.
A Multi-Modal Digital Game-Based Learning Environment for Hospitalized Children with Chronic Illnesses.

ERIC Educational Resources Information Center

Chin, Jui-Chih; Tsuei, Mengping

2014-01-01

The aim of this study was to explore the digital game-based learning for children with chronic illnesses in the hospital settings. The design-based research and qualitative methods were applied. Three eight-year-old children with leukemia participated in this study. In the first phase, the multi-user game-based learning system was developed and…
The Effects of Case-Based Team Learning on Students’ Learning, Self Regulation and Self Direction

PubMed Central

Rezaee, Rita; Mosalanejad, Leili

2015-01-01

Introduction: The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. Material & Methods: This is aquasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliablequestionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students’with points of view on educational methods. Results: The Results showed an increase in the students’ self directed learning based on their performance on the post-test. The results showed that the students’ self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students’ learning (p=0.003). Conclusion: This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students’ ’knowledge, but also the advancement of long- life learning skills. PMID:25946918

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.
Computer-Assisted Learning Applications in Health Educational Informatics: A Review.

PubMed

Shaikh, Faiq; Inayat, Faisal; Awan, Omer; Santos, Marlise D; Choudhry, Adnan M; Waheed, Abdul; Kajal, Dilkash; Tuli, Sagun

2017-08-10

Computer-assisted learning (CAL) as a health informatics application is a useful tool for medical students in the era of expansive knowledge bases and the increasing need for and the consumption of automated and interactive systems. As the scope and breadth of medical knowledge expand, the need for additional learning outside of lecture hours is becoming increasingly important. CAL can be an impactful adjunct to conventional methods that currently exist in the halls of learning. There is an increasing body of literature that suggests that CAL should be a commonplace and the recommended method of learning for medical students. Factors such as technical issues that hinder the performance of CAL are also evaluated. We conclude by encouraging the use of CAL by medical students as a highly beneficial method of learning that complements and enhances lectures and provides intuitive, interactive modulation of a self-paced curriculum based on the individual's academic abilities.
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.
Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.
A 3D model retrieval approach based on Bayesian networks lightfield descriptor

NASA Astrophysics Data System (ADS)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.
The effect of web quest and team-based learning on students’ self-regulation

PubMed Central

BADIYEPEYMAIE JAHROMI, ZOHREH; MOSALANEJAD, LEILI; REZAEE, RITA

2016-01-01

Introduction In this study, the authors aimed to examine the effects of cooperative learning methods using Web Quest and team-based learning on students’ self-direction, self-regulation, and academic achievement. Method This is a comparative study of students taking a course in mental health and psychiatric disorders. In two consecutive years, a group of students were trained using the WebQuest approach as a teaching strategy (n = 38), while the other group was taught using team-based learning (n=39). Data gathering was based on Guglielmino’s self-directed learning readiness scale (SDLRS) and Buford’s self-regulation questionnaire. The data were analyzed by descriptive test using M (IQR), Wilcoxon signed-rank test, and the Mann–Whitney U-test in SPSS software, version 13. p<0.05 was considered as the significance level. Results The results of the Mann–Whitney U test showed that the participants’ self- directed (self-management) and self-regulated learning differed between the two groups (p=0.04 and p=0.01, respectively). Wilcoxon test revealed that self-directed learning indices (self-control and self-management) were differed between the two strategies before and after the intervention. However, the scores related to learning (students’ final scores) were higher in the WebQuest approach than in team-based learning. Conclusion By employing modern educational approaches, students are not only more successful in their studies but also acquire the necessary professional skills for future performance. Further research to compare the effects of new methods of teaching is required. PMID:27104202
The effects of team-based learning techniques on nursing students' perception of the psycho-social climate of the classroom.

PubMed

Koohestani, Hamid Reza; Baghcheghi, Nayereh

2016-01-01

Background: Team-based learning is a structured type of cooperative learning that is becoming increasingly more popular in nursing education. This study compares levels of nursing students' perception of the psychosocial climate of the classroom between conventional lecture group and team-based learning group. Methods: In a quasi-experimental study with pretest-posttest design 38 nursing students of second year participated. One half of the 16 sessions of cardiovascular disease nursing course sessions was taught by lectures and the second half with team-based learning. The modified college and university classroom environment inventory (CUCEI) was used to measure the perception of classroom environment. This was completed after the final lecture and TBL sessions. Results: Results revealed a significant difference in the mean scores of psycho-social climate for the TBL method (Mean (SD): 179.8(8.27)) versus the mean score for the lecture method (Mean (SD): 154.213.44)). Also, the results showed significant differences between the two groups in the innovation (p<0.001), student cohesiveness (p=0.01), cooperation (p<0.001) and equity (p= 0.03) sub-scales scores (p<0.05). Conclusion: This study provides evidence that team-based learning does have a positive effect on nursing students' perceptions of their psycho-social climate of the classroom.
A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202
Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

PubMed

Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-03-01

In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on contact prediction. The first method (MULTICOM-NOVEL) uses only traditional features (sequence profile, secondary structure, and solvent accessibility) with deep learning to predict contacts and serves as a baseline. The second method (MULTICOM-CONSTRUCT) uses our new alignment algorithm to generate deep multiple sequence alignment to derive coevolution-based features, which are integrated by a neural network method to predict contacts. The third method (MULTICOM-CLUSTER) is a consensus combination of the predictions of the first two methods. We evaluated our methods on 94 CASP12 domains. On a subset of 38 free-modeling domains, our methods achieved an average precision of up to 41.7% for top L/5 long-range contact predictions. The comparison of the three methods shows that the quality and effective depth of multiple sequence alignments, coevolution-based features, and machine learning integration of coevolution-based features and traditional features drive the quality of predicted protein contacts. On the full CASP12 dataset, the coevolution-based features alone can improve the average precision from 28.4% to 41.6%, and the machine learning integration of all the features further raises the precision to 56.3%, when top L/5 predicted long-range contacts are evaluated. And the correlation between the precision of contact prediction and the logarithm of the number of effective sequences in alignments is 0.66. © 2017 Wiley Periodicals, Inc.
C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

PubMed

Zhang, Baqun; Zhang, Min

2017-12-11

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.
Enhanced learning through design problems - teaching a components-based course through design

NASA Astrophysics Data System (ADS)

Jensen, Bogi Bech; Högberg, Stig; Fløtum Jensen, Frida av; Mijatovic, Nenad

2012-08-01

This paper describes a teaching method used in an electrical machines course, where the students learn about electrical machines by designing them. The aim of the course is not to teach design, albeit this is a side product, but rather to teach the fundamentals and the function of electrical machines through design. The teaching method is evaluated by a student questionnaire, designed to measure the quality and effectiveness of the teaching method. The results of the questionnaire conclusively show that this method labelled 'learning through design' is a very effective way of teaching a components-based course. This teaching method can easily be generalised and used in other courses.
Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

NASA Astrophysics Data System (ADS)

Roberts, Deborah L.

2011-12-01

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

PubMed

Stergiou, Nikolaos; Georgoulakis, Giannis; Margari, Niki; Aninos, Dionisios; Stamataki, Melina; Stergiou, Efi; Pouliakis, Abraam; Karakitsos, Petros

2009-12-01

The evolution of information technologies and telecommunications has made the World Wide Web a low cost and easily accessible tool for the dissemination of information and knowledge. Continuous Medical Education (CME) sites dedicated in cytopathology field are rather poor, they do not succeed in following the constant changes and lack the ability of providing cytopathologists with a dynamic learning environment, adaptable to the development of cytopathology. Learning methods including skills such as decision making, reasoning and problem solving are critical in the development of such a learning environment. The objectives of this study are (1) to demonstrate on the basis of a web-based training system the successful application of traditional learning theories and methods and (2) to effectively evaluate users' perception towards the educational program, using a combination of observers, theories and methods. Trainees are given the opportunity to browse through the educational material, collaborate in synchronous and asynchronous mode, practice their skills through problems and tasks and test their knowledge using the self-evaluation tool. On the other hand, the trainers are responsible for editing learning material, attending students' progress and organizing the problem-based and task-based scenarios. The implementation of the web-based training system is based on the three-tier architecture and uses an Apache Tomcat web server and a MySQL database server. By December 2008, CytoTrainer's learning environment contains two courses in cytopathology: Gynaecological Cytology and Thyroid Cytology offering about 2000 digital images and 20 case sessions. Our evaluation method is a combination of both qualitative and quantitative approaches to explore how the various parts of the system and students' attitudes work together. Trainees approved of the course's content, methodology and learning activities. The triangulation of evaluation methods revealed that the training program is suitable for the continuous distance education in cytopathology and that it has improved the trainees' skills in diagnostic cytopathology. The web-based training system can be successfully involved in the continuous distance education in cytopathology. It provides the opportunity to access learning material from any place at any time and supports the acquisition of diagnostic knowledge.
Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.
Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

PubMed

Ding, Yongxia; Zhang, Peili

2018-06-12

Problem-based learning (PBL) is an effective and highly efficient teaching approach that is extensively applied in education systems across a variety of countries. This study aimed to investigate the effectiveness of web-based PBL teaching pedagogies in large classes. The cluster sampling method was used to separate two college-level nursing student classes (graduating class of 2013) into two groups. The experimental group (n = 162) was taught using a web-based PBL teaching approach, while the control group (n = 166) was taught using conventional teaching methods. We subsequently assessed the satisfaction of the experimental group in relation to the web-based PBL teaching mode. This assessment was performed following comparison of teaching activity outcomes pertaining to exams and self-learning capacity between the two groups. When compared with the control group, the examination scores and self-learning capabilities were significantly higher in the experimental group (P < 0.01) compared with the control group. In addition, 92.6% of students in the experimental group expressed satisfaction with the new web-based PBL teaching approach. In a large class-size teaching environment, the web-based PBL teaching approach appears to be more optimal than traditional teaching methods. These results demonstrate the effectiveness of web-based teaching technologies in problem-based learning. Copyright © 2018. Published by Elsevier Ltd.
Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.
Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.
Active appearance model and deep learning for more accurate prostate segmentation on MRI

NASA Astrophysics Data System (ADS)

Cheng, Ruida; Roth, Holger R.; Lu, Le; Wang, Shijun; Turkbey, Baris; Gandler, William; McCreedy, Evan S.; Agarwal, Harsh K.; Choyke, Peter; Summers, Ronald M.; McAuliffe, Matthew J.

2016-03-01

Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to segment the prostate on MR images. The performance of the segmentation method is evaluated on 20 unseen MR image datasets. The proposed method combining AAM and Deep Learning achieves a mean Dice Similarity Coefficient (DSC) of 0.925 for whole 3D MR images of the prostate using axial cross-sections. The proposed model utilizes the adaptive atlas-based AAM model and Deep Learning to achieve significant segmentation accuracy.

Toward an instructionally oriented theory of example-based learning.

PubMed

Renkl, Alexander

2014-01-01

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (b) phases of skill acquisition, and (c) learning processes. The prescriptive subtheory proposes instructional principles that make full exploitation of the potential of example-based learning possible. Copyright © 2013 Cognitive Science Society, Inc.
Social Networks-Based Adaptive Pairing Strategy for Cooperative Learning

ERIC Educational Resources Information Center

Chuang, Po-Jen; Chiang, Ming-Chao; Yang, Chu-Sing; Tsai, Chun-Wei

2012-01-01

In this paper, we propose a grouping strategy to enhance the learning and testing results of students, called Pairing Strategy (PS). The proposed method stems from the need of interactivity and the desire of cooperation in cooperative learning. Based on the social networks of students, PS provides members of the groups to learn from or mimic…
Educational Data Mining and Problem-Based Learning

ERIC Educational Resources Information Center

Walldén, Sari; Mäkinen, Erkki

2014-01-01

This paper considers the use of log data provided by learning management systems when studying whether students obey the problem-based learning (PBL) method. Log analysis turns out to be a valuable tool in measuring the use of the learning material of interest. It gives reliable figures concerning not only the number of use sessions but also the…
Model-Free Optimal Tracking Control via Critic-Only Q-Learning.

PubMed

Luo, Biao; Liu, Derong; Huang, Tingwen; Wang, Ding

2016-10-01

Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.
Bilevel Model-Based Discriminative Dictionary Learning for Recognition.

PubMed

Zhou, Pan; Zhang, Chao; Lin, Zhouchen

2017-03-01

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l 0 or l 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.
Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

PubMed Central

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images. PMID:24989402
Inquiry Based Learning: A Modified Moore Method Approach To Encourage Student Research

ERIC Educational Resources Information Center

McLoughlin, M. Padraig M. M.

2008-01-01

The author of this paper submits that a mathematics student needs to learn to conjecture and prove or disprove said conjecture. Ergo, the purpose of the paper is to submit the thesis that learning requires doing; only through inquiry is learning achieved, and hence this paper proposes a programme of use of a modified Moore method (MMM) across the…
Learning to rank atlases for multiple-atlas segmentation.

PubMed

Sanroma, Gerard; Wu, Guorong; Gao, Yaozong; Shen, Dinggang

2014-10-01

Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.
Deep learning for neuroimaging: a validation study.

PubMed

Plis, Sergey M; Hjelm, Devon R; Salakhutdinov, Ruslan; Allen, Elena A; Bockholt, Henry J; Long, Jeffrey D; Johnson, Hans J; Paulsen, Jane S; Turner, Jessica A; Calhoun, Vince D

2014-01-01

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.
The development of learning media of acid-base indicator from extract of natural colorant as an alternative media in learning chemistry

NASA Astrophysics Data System (ADS)

Nurhadi, Mukhamad; Wirhanuddin, Erwin, Muflihah, Erika, Farah; Widiyowati, Iis Intan

2017-03-01

The development of learning media of acid base indicator from extract of natural colorants as an alternative media in chemistry learning; acid-base solution by using creative problem solving model at SMA N 10 Samarinda has been done. This research aimed to create and develop the learning media from extract of natural colorants, measure its quality and effectiveness, and measure the quality of student learning outcome in acid-base solution topic by using that media. The development process used Analysis, Design, Development, Implementation, and Evaluation (ADDIE) method. The learning media of acid-base indicator was created in the form of box experiment. Its quality was in the range of very good and it was effectively applied in the learning and gave positive impact on the achievement of learning goals.
Internet-based Interactive Construction Management Learning System.

ERIC Educational Resources Information Center

Sawhney, Anil; Mund, Andre; Koczenasz, Jeremy

2001-01-01

Describes a way to incorporate practical content into the construction engineering and management curricula: the Internet-based Interactive Construction Management Learning System, which uses interactive and adaptive learning environments to train students in the areas of construction methods, equipment and processes using multimedia, databases,…
MISESS: Web-Based Examination, Evaluation, and Guidance

ERIC Educational Resources Information Center

Tanrikulu, Zuhal

2006-01-01

Many universities are reevaluating their traditional educational methods and providing pedagogical material through the Internet. Some Web-based systems offer a constructionist learning environment, for example, where students can learn by designing their own objects. Providing effective, convenient technology to support learning is important, and…
Blended Learning in a Teacher Training Course: Integrated Interactive E-Learning and Contact Learning

ERIC Educational Resources Information Center

Kupetz, Rita; Ziegenmeyer, Brigit

2005-01-01

The paper discusses a blended learning concept for a university teacher training course for prospective teachers of English. The concept aims at purposeful learning using different methods and activities, various traditional and electronic media, learning spaces covering contact and distance learning, and task-based learning modules that begin…
Learning by Doing Approach in the Internet Environment to Improve the Teaching Efficiency of Information Technology

NASA Astrophysics Data System (ADS)

Zhang, X.-S.; Xie, Hua

This paper presents a learning-by-doing method in the Internet environment to enhance the results of information technology education by experimental work in the classroom of colleges. In this research, an practical approach to apply the "learning by doing" paradigm in Internet-based learning, both for higher educational environments and life-long training systems, taking into account available computer and network resources, such as blogging, podcasting, social networks, wiki etc. We first introduce the different phases in the learning process, which aimed at showing to the readers that the importance of the learning by doing paradigm, which is not implemented in many Internet-based educational environments. Secondly, we give the concept of learning by doing in the different perfective. Then, we identify the most important trends in this field, and give a real practical case for the application of this approach. The results show that the attempt methods are much better than traditional teaching methods.
Rethinking the lecture: the application of problem based learning methods to atypical contexts.

PubMed

Rogal, Sonya M M; Snider, Paul D

2008-05-01

Problem based learning is a teaching and learning strategy that uses a problematic stimulus as a means of motivating and directing students to develop and acquire knowledge. Problem based learning is a strategy that is typically used with small groups attending a series of sessions. This article describes the principles of problem based learning and its application in atypical contexts; large groups attending discrete, stand-alone sessions. The principles of problem based learning are based on Socratic teaching, constructivism and group facilitation. To demonstrate the application of problem based learning in an atypical setting, this article focuses on the graduate nurse intake from a teaching hospital. The groups are relatively large and meet for single day sessions. The modified applications of problem based learning to meet the needs of atypical groups are described. This article contains a step by step guide of constructing a problem based learning package for large, single session groups. Nurse educators facing similar groups will find they can modify problem based learning to suit their teaching context.
Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.
Traditional learning and problem-based learning: self-perception of preparedness for internship.

PubMed

Millan, Laís Pereira Bueno; Semer, Beatriz; Rodrigues, José Mauro da Silva; Gianini, Reinaldo José

2012-01-01

This study aims to evaluate Pontificia Universidade Católica de São Paulo (PUC-SP) medical students' perception of their preparedness to attend the internship course by comparing students who entered the internship in 2009, who were taught according to the traditional learning method, and those who entered the internship in 2010, who were taught according to the new method, i.e. problem-based learning (PBL). 50 traditional learning method students answered a standard Lickert scale questionnaire upon entering internship in 2009. In 2010, the process was repeated with PBL students. The questionnaire was based upon the Preparation for Hospital Practice Questionnaire. This questionnaire was evaluated by professors from three medical schools in Brazil regarding its applicability. The original questions were classified according to the importance these professors attributed to them, and less important questions were removed. Scores obtained from the Student's t-test were considered significant with p < 0.05. A significant statistical difference was observed in 16 questions, and the traditional learning method students reported higher average scores. When questions were divided into dimensions, a significant statistical difference appeared in the dimensions " social aspects of health", "medical skills", and "ethical concepts"; traditional learning method students again reported higher scores (p < 0.001 for all dimensions). Higher scores were also reported when the average of the answers to the whole questionnaire was calculated. Traditional learning method students consider themselves to be better prepared for internship activities than PBL students, according to the following three comparative means: by analyzing the answers to each question, by grouping these answers into dimensions, and by calculating the means of answers to the whole questionnaire.
Examining the Influence of Seductive Details in Case-Based Instruction on Pre-Service Teachers' Learning and Learning Perceptions

ERIC Educational Resources Information Center

Abercrombie, Sara

2011-01-01

The case-based instructional method uses fictionalized or actual narratives as instructional tools to support learning, decision-making, and improved transfer to practical settings. Educational theorists and researchers specializing in case-based instruction have suggested that cases can be made more realistic, engaging, and challenging, thus…
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…

The Challenges of Work-Based Learning in the Changing Context of the European Higher Education Area

ERIC Educational Resources Information Center

Schmidt, Reinhard; Gibbs, Paul

2009-01-01

This article discusses the key features of the common European framework for work-based learning (WBL) of the "Developing European Work Based Learning Approaches and Methods" (DEWBLAM) project (2003-2006). It examines the context of recent European initiatives and comments on the potential implications for policy, practice and theory,…
Adapting Cognitive Walkthrough to Support Game Based Learning Design

ERIC Educational Resources Information Center

Farrell, David; Moffat, David C.

2014-01-01

For any given Game Based Learning (GBL) project to be successful, the player must learn something. Designers may base their work on pedagogical research, but actual game design is still largely driven by intuition. People are famously poor at unsupported methodical thinking and relying so much on instinct is an obvious weak point in GBL design…
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…
Blending toward Competency. Early Patterns of Blended Learning and Competency-Based Education in New Hampshire

ERIC Educational Resources Information Center

Freeland, Julia

2014-01-01

As the education field strives to differentiate and personalize learning to cater to each student, two related movements are gaining attention: competency-based education and blended learning. In competency-based models, students advance on the basis of mastery, rather than according to the traditional methods of counting progress in terms of time…
Student Perceptions of Learning in a Web-Based Tutorial.

ERIC Educational Resources Information Center

Brescia, William; McAuley, Sean

This case study used both quantitative and qualitative methods to investigate students' perceptions of learning using a Web-based tutorial. Students participated in a Web-based tutorial to learn basic HTML as part of a graduate-level Web design course. Four of five students agreed to participate in the survey and interviews. After completing the…
Evaluating Listening and Speaking Skills in a Mobile Game-Based Learning Environment with Situational Contexts

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Shih, Timothy K.; Ma, Zhao-Heng; Shadiev, Rustam; Chen, Shu-Yu

2016-01-01

Game-based learning activities that facilitate students' listening and speaking skills were designed in this study. To participate in learning activities, students in the control group used traditional methods, while students in the experimental group used a mobile system. In our study, we looked into the feasibility of mobile game-based learning…
Web-Based Interactive 3D Visualization as a Tool for Improved Anatomy Learning

ERIC Educational Resources Information Center

Petersson, Helge; Sinkvist, David; Wang, Chunliang; Smedby, Orjan

2009-01-01

Despite a long tradition, conventional anatomy education based on dissection is declining. This study tested a new virtual reality (VR) technique for anatomy learning based on virtual contrast injection. The aim was to assess whether students value this new three-dimensional (3D) visualization method as a learning tool and what value they gain…
Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

PubMed

Li, Yang; Yang, Jianyi

2017-04-24

The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pearson correlation coefficient to about 0.8 on the core set of the PDBbind 2007 database, which is significantly higher than the performance of any conventional scoring function on the same benchmark. A few studies have been made to discuss the performance of machine-learning-based methods, but the reason for this improvement remains unclear. In this study, by systemically controlling the structural and sequence similarity between the training and test proteins of the PDBbind benchmark, we demonstrate that protein structural and sequence similarity makes a significant impact on machine-learning-based methods. After removal of training proteins that are highly similar to the test proteins identified by structure alignment and sequence alignment, machine-learning-based methods trained on the new training sets do not outperform the conventional scoring functions any more. On the contrary, the performance of conventional functions like X-Score is relatively stable no matter what training data are used to fit the weights of its energy terms.
The scientific learning approach using multimedia-based maze game to improve learning outcomes

NASA Astrophysics Data System (ADS)

Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

2016-02-01

The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).
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.
Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.
Paradigms for machine learning

NASA Technical Reports Server (NTRS)

Schlimmer, Jeffrey C.; Langley, Pat

1991-01-01

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.
Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

PubMed

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.
Learning from Simple Ebooks, Online Cases or Classroom Teaching When Acquiring Complex Knowledge. A Randomized Controlled Trial in Respiratory Physiology and Pulmonology

PubMed Central

Worm, Bjarne Skjødt

2013-01-01

Background and Aims E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. Methods 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. Results For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). Conclusions E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method. PMID:24039917
Development of a Computer-Based Visualised Quantitative Learning System for Playing Violin Vibrato

ERIC Educational Resources Information Center

Ho, Tracy Kwei-Liang; Lin, Huann-shyang; Chen, Ching-Kong; Tsai, Jih-Long

2015-01-01

Traditional methods of teaching music are largely subjective, with the lack of objectivity being particularly challenging for violin students learning vibrato because of the existence of conflicting theories. By using a computer-based analysis method, this study found that maintaining temporal coincidence between the intensity peak and the target…
Effects of Problem-Based Learning on Attitude: A Meta-Analysis Study

ERIC Educational Resources Information Center

Demirel, Melek; Dagyar, Miray

2016-01-01

To date, researchers have frequently investigated students' attitudes toward courses supported by problem-based learning. There are several studies with different results in the literature. It is necessary to combine and interpret the findings of these studies through a meta-analysis method. This method aims to combine different results of similar…
Scaffolding Wiki-Supported Collaborative Learning for Small-Group Projects and Whole-Class Collaborative Knowledge Building

ERIC Educational Resources Information Center

Lin, C-Y.; Reigeluth, C. M.

2016-01-01

While educators value wikis' potential, wikis may fail to support collaborative constructive learning without careful scaffolding. This article proposes literature-based instructional methods, revised based on two expert instructors' input, presents the collected empirical evidence on the effects of these methods and proposes directions for future…
Determining e-Portfolio Elements in Learning Process Using Fuzzy Delphi Analysis

ERIC Educational Resources Information Center

Mohamad, Syamsul Nor Azlan; Embi, Mohamad Amin; Nordin, Norazah

2015-01-01

The present article introduces the Fuzzy Delphi method results obtained in the study on determining e-Portfolio elements in learning process for art and design context. This method bases on qualified experts that assure the validity of the collected information. In particular, the confirmation of elements is based on experts' opinion and…
Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

ERIC Educational Resources Information Center

Helmreich, James E.; Krog, K. Peter

2018-01-01

We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…
Using Inquiry-Based Strategies for Enhancing Students' STEM Education Learning

ERIC Educational Resources Information Center

Lai, Ching-San

2018-01-01

The major purpose of this study was to investigate whether or not the inquiry-based method is effective in improving students' learning in STEM (Science, Technology, Engineering, and Mathematics) education. Both quantitative and qualitative methods were used. A total of 73 college students studying Information Technology (IT) were chosen as…

Teaching Teamwork and Problem Solving Concurrently

ERIC Educational Resources Information Center

Goltz, Sonia M.; Hietapelto, Amy B.; Reinsch, Roger W.; Tyrell, Sharon K.

2008-01-01

Teamwork and problem-solving skills have frequently been identified by business leaders as being key competencies; thus, teaching methods such as problem-based learning and team-based learning have been developed. However, the focus of these methods has been on teaching one skill or the other. A key argument for teaching the skills concurrently is…
Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

PubMed Central

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147
Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

PubMed

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.
Studying the Positive Influence of the Use of Video in Teaching & Learning Environments, Focusing on Registration of the Directions Where It Improves the PBL Effectiveness: A Systematic Literature Review

ERIC Educational Resources Information Center

Aronis, Alexis

2016-01-01

Previous studies report the involvement of the use of video in the frameworks of problem-based learning (PBL), case-based learning, and project-based learning. This systematic literature review, through two research questions, explores the positive influence of the use of video in those instructional methods, and, while focusing on PBL, identifies…
Design of Intelligent Robot as A Tool for Teaching Media Based on Computer Interactive Learning and Computer Assisted Learning to Improve the Skill of University Student

NASA Astrophysics Data System (ADS)

Zuhrie, M. S.; Basuki, I.; Asto B, I. G. P.; Anifah, L.

2018-01-01

The focus of the research is the teaching module which incorporates manufacturing, planning mechanical designing, controlling system through microprocessor technology and maneuverability of the robot. Computer interactive and computer-assisted learning is strategies that emphasize the use of computers and learning aids (computer assisted learning) in teaching and learning activity. This research applied the 4-D model research and development. The model is suggested by Thiagarajan, et.al (1974). 4-D Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with an objective to produce a tool of learning in the form of intelligent robot modules and kit based on Computer Interactive Learning and Computer Assisted Learning. From the data of the Indonesia Robot Contest during the period of 2009-2015, it can be seen that the modules that have been developed confirm the fourth stage of the research methods of development; disseminate method. The modules which have been developed for students guide students to produce Intelligent Robot Tool for Teaching Based on Computer Interactive Learning and Computer Assisted Learning. Results of students’ responses also showed a positive feedback to relate to the module of robotics and computer-based interactive learning.
Problem-Based Learning in Accounting

ERIC Educational Resources Information Center

Dockter, DuWayne L.

2012-01-01

Seasoned educators use an assortment of student-centered methods and tools to enhance their student's learning environment. In respects to methodologies used in accounting, educators have utilized and created new forms of problem-based learning exercises, including case studies, simulations, and other projects, to help students become more active…
Narratives of Experiential Learning: Students' Engagement in a Physical Activity-Based Service-Learning Course

ERIC Educational Resources Information Center

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

2017-01-01

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

PubMed

Usaha, Wipawee; Barria, Javier A

2007-06-01

In this paper, we develop and assess online decision-making algorithms for call admission and routing for low Earth orbit (LEO) satellite networks. It has been shown in a recent paper that, in a LEO satellite system, a semi-Markov decision process formulation of the call admission and routing problem can achieve better performance in terms of an average revenue function than existing routing methods. However, the conventional dynamic programming (DP) numerical solution becomes prohibited as the problem size increases. In this paper, two solution methods based on reinforcement learning (RL) are proposed in order to circumvent the computational burden of DP. The first method is based on an actor-critic method with temporal-difference (TD) learning. The second method is based on a critic-only method, called optimistic TD learning. The algorithms enhance performance in terms of requirements in storage, computational complexity and computational time, and in terms of an overall long-term average revenue function that penalizes blocked calls. Numerical studies are carried out, and the results obtained show that the RL framework can achieve up to 56% higher average revenue over existing routing methods used in LEO satellite networks with reasonable storage and computational requirements.
Using Trained Pixel Classifiers to Select Images of Interest

NASA Technical Reports Server (NTRS)

Mazzoni, D.; Wagstaff, K.; Castano, R.

2004-01-01

We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.
Effectiveness of Case-Based Learning Instruction on Epistemological Beliefs and Attitudes Toward Chemistry

NASA Astrophysics Data System (ADS)

Çam, Aylin; Geban, Ömer

2011-02-01

The purpose of the study was to investigate the effectiveness of case-based learning instruction over traditionally designed chemistry instruction on eleventh grade students' epistemological beliefs and their attitudes toward chemistry as a school subject. The subjects of this study consisted of 63 eleventh grade students from two intact classes of an urban high school instructed with same teacher. Each teaching method was randomly assigned to one class. The experimental group received case-based learning and the control group received traditional instruction. At the experimental group, life cases were presented with small group format; at the control group, lecturing and discussion was carried out. The results showed that there was a significant difference between the experimental and control group with respect to their epistemological beliefs and attitudes toward chemistry as a school subject in favor of case-based learning method group. Thus, case base learning is helpful for development of students' epistemological beliefs and attitudes toward chemistry.
The impact of internet and simulation-based training on transoesophageal echocardiography learning in anaesthetic trainees: a prospective randomised study.

PubMed

Sharma, V; Chamos, C; Valencia, O; Meineri, M; Fletcher, S N

2013-06-01

With the increasing role of transoesophageal echocardiography in clinical fields other than cardiac surgery, we decided to assess the efficacy of multi-modular echocardiography learning in echo-naïve anaesthetic trainees. Twenty-eight trainees undertook a pre-test to ascertain basic echocardiography knowledge, following which the study subjects were randomly assigned to two groups: learning via traditional methods such as review of guidelines and other literature (non-internet group); and learning via an internet-based echocardiography resource (internet group). After this, subjects in both groups underwent simulation-based echocardiography training. More tests were then conducted after a review of the respective educational resources and simulation sessions. Mean (SD) scores of subjects in the non-internet group were 28 (10)%, 44 (10)% and 63 (5)% in the pre-test, post-intervention test and post-simulation test, respectively, whereas those in the internet group scored 29 (8)%, 59 (10)%, (p = 0.001) and 72 (8)%, p = 0.005, respectively. The use of internet- and simulation-based learning methods led to a significant improvement in knowledge of transoesophageal echocardiography by anaesthetic trainees. The impact of simulation-based training was greater in the group who did not use the internet-based resource. We conclude that internet- and simulation-based learning methods both improve transoesophageal echocardiography knowledge in echo-naïve anaesthetic trainees. Anaesthesia © 2013 The Association of Anaesthetists of Great Britain and Ireland.
The introduction and effectiveness of simulation-based learning in medical education.

PubMed

Nara, Nobuo; Beppu, Masashi; Tohda, Shuji; Suzuki, Toshiya

2009-01-01

To contribute to reforming the medical education system in Japan, we visited overseas medical schools and observed the methods utilized in medical education. We visited 28 medical schools and five institutes in the United States, Europe, Australia and Asia in 2008. We met deans and specialists in medical affairs and observed the medical schools' facilities. Among the several effective educational methods used in overseas medical schools, simulation-based learning was being used in all that we visited. Simulation-based learning is used to promote medical students' mastery of communication skills, medical interviewing, physical examination and basic clinical procedures. Students and tutors both recognize the effectiveness of simulation-based learning in medical education. In contrast to overseas medical schools, simulation-based learning is not common in Japan. There remain many barriers to introduce simulation-based education in Japan, such as a shortage of medical tutors, staff, mannequins and budget. However, enhancing the motivation of tutors is likely the most important factor to facilitate simulation-based education in Japanese medical schools to become common place.
Lexical and semantic representations in the acquisition of L2 cognate and non-cognate words: evidence from two learning methods in children.

PubMed

Comesaña, Montserrat; Soares, Ana Paula; Sánchez-Casas, Rosa; Lima, Cátia

2012-08-01

How bilinguals represent words in two languages and which mechanisms are responsible for second language acquisition are important questions in the bilingual and vocabulary acquisition literature. This study aims to analyse the effect of two learning methods (picture- vs. word-based method) and two types of words (cognates and non-cognates) in early stages of children's L2 acquisition. Forty-eight native speakers of European Portuguese, all sixth graders (mean age = 10.87 years; SD= 0.85), participated in the study. None of them had prior knowledge of Basque (the L2 in this study). After a learning phase in which L2 words were learned either by a picture- or a word-based method, children were tested in a backward-word translation recognition task at two times (immediately vs. one week later). Results showed that the participants made more errors when rejecting semantically related than semantically unrelated words as correct translations (semantic interference effect). The magnitude of this effect was higher in the delayed test condition regardless of the learning method. Moreover, the overall performance of participants from the word-based method was better than the performance of participants from the picture-word method. Results were discussed concerning the most significant bilingual lexical processing models. ©2011 The British Psychological Society.
Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.

PubMed

Tong, Tong; Wolz, Robin; Coupé, Pierrick; Hajnal, Joseph V; Rueckert, Daniel

2013-08-01

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods. Copyright © 2013 Elsevier Inc. All rights reserved.
Web-Based Learning Environment Based on Students’ Needs

NASA Astrophysics Data System (ADS)

Hamzah, N.; Ariffin, A.; Hamid, H.

2017-08-01

Traditional learning needs to be improved since it does not involve active learning among students. Therefore, in the twenty-first century, the development of internet technology in the learning environment has become the main needs of each student. One of the learning environments to meet the needs of the teaching and learning process is a web-based learning environment. This study aims to identify the characteristics of a web-based learning environment that supports students’ learning needs. The study involved 542 students from fifteen faculties in a public higher education institution in Malaysia. A quantitative method was used to collect the data via a questionnaire survey by randomly. The findings indicate that the characteristics of a web-based learning environment that support students’ needs in the process of learning are online discussion forum, lecture notes, assignments, portfolio, and chat. In conclusion, the students overwhelmingly agreed that online discussion forum is the highest requirement because the tool can provide a space for students and teachers to share knowledge and experiences related to teaching and learning.
Blending Face-to-Face and Distance Learning Methods in Adult and Career-Technical Education. Practice Application Brief No. 23.

ERIC Educational Resources Information Center

Wonacott, Michael E.

Both face-to-face and distance learning methods are currently being used in adult education and career and technical education. In theory, the advantages of face-to-face and distance learning methods complement each other. In practice, however, both face-to-face and information and communications technology (ICT)-based distance programs often rely…
Comparison of computer-assisted instruction (CAI) versus traditional textbook methods for training in abdominal examination (Japanese experience).

PubMed

Qayumi, A K; Kurihara, Y; Imai, M; Pachev, G; Seo, H; Hoshino, Y; Cheifetz, R; Matsuura, K; Momoi, M; Saleem, M; Lara-Guerra, H; Miki, Y; Kariya, Y

2004-10-01

This study aimed to compare the effects of computer-assisted, text-based and computer-and-text learning conditions on the performances of 3 groups of medical students in the pre-clinical years of their programme, taking into account their academic achievement to date. A fourth group of students served as a control (no-study) group. Participants were recruited from the pre-clinical years of the training programmes in 2 medical schools in Japan, Jichi Medical School near Tokyo and Kochi Medical School near Osaka. Participants were randomly assigned to 4 learning conditions and tested before and after the study on their knowledge of and skill in performing an abdominal examination, in a multiple-choice test and an objective structured clinical examination (OSCE), respectively. Information about performance in the programme was collected from school records and students were classified as average, good or excellent. Student and faculty evaluations of their experience in the study were explored by means of a short evaluation survey. Compared to the control group, all 3 study groups exhibited significant gains in performance on knowledge and performance measures. For the knowledge measure, the gains of the computer-assisted and computer-assisted plus text-based learning groups were significantly greater than the gains of the text-based learning group. The performances of the 3 groups did not differ on the OSCE measure. Analyses of gains by performance level revealed that high achieving students' learning was independent of study method. Lower achieving students performed better after using computer-based learning methods. The results suggest that computer-assisted learning methods will be of greater help to students who do not find the traditional methods effective. Explorations of the factors behind this are a matter for future research.
Influence of Strategy of Learning and Achievement Motivation of Learning Achievement Class VIII Students of State Junior High School in District Blitar

ERIC Educational Resources Information Center

Ayundawati, Dyah; Setyosari, Punaji; Susilo, Herawati; Sihkabuden

2016-01-01

This study aims for know influence of problem-based learning strategies and achievement motivation on learning achievement. The method used in this research is quantitative method. The instrument used in this study is two fold instruments to measure moderator variable (achievement motivation) and instruments to measure the dependent variable (the…
Using a Mixed Methods Research Design in a Study Investigating the "Heads of e-Learning" Perspective towards Technology Enhanced Learning

ERIC Educational Resources Information Center

Almpanis, Timos

2016-01-01

This paper outlines the research design, methodology and methods employed in research conducted in the context of Higher Education Institutions (HEIs) and focuses on the Heads of e-Learning (HeLs) perspective about Technology Enhanced Learning (TEL) by campus-based UK institutions. This paper aims to expand on the research design and the research…
Using Formal Game Design Methods to Embed Learning Outcomes into Game Mechanics and Avoid Emergent Behaviour

ERIC Educational Resources Information Center

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

2017-01-01

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

Gendered Socialization with an Embodied Agent: Creating a Social and Affable Mathematics Learning Environment for Middle-Grade Females

ERIC Educational Resources Information Center

Kim, Yanghee; Lim, Jae Hoon

2013-01-01

This study examined whether or not embodied-agent-based learning would help middle-grade females have more positive mathematics learning experiences. The study used an explanatory mixed methods research design. First, a classroom-based experiment was conducted with one hundred twenty 9th graders learning introductory algebra (53% male and 47%…
A Model of Small-Group Problem-Based Learning in Pharmacy Education: Teaching in the Clinical Environment

ERIC Educational Resources Information Center

Khumsikiew, Jeerisuda; Donsamak, Sisira; Saeteaw, Manit

2015-01-01

Problem-based Learning (PBL) is an alternate method of instruction that incorporates basic elements of cognitive learning theory. Colleges of pharmacy use PBL to aid anticipated learning outcomes and practice competencies for pharmacy student. The purpose of this study were to implement and evaluate a model of small group PBL for 5th year pharmacy…
The Effectiveness of the Chemistry Problem Based Learning (PBL) via FB among Pre-University Students

ERIC Educational Resources Information Center

Sunar, Mohd Shahir Mohamed; Shaari, Ahmad Jelani

2017-01-01

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

ERIC Educational Resources Information Center

Filippatou, Diamanto; Kaldi, Stavroula

2010-01-01

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

NASA Astrophysics Data System (ADS)

Lee, Sihyoung; Yang, Seungji; Ro, Yong Man; Kim, Hyoung Joong

2008-02-01

As the Internet and multimedia technology is becoming advanced, the number of digital multimedia contents is also becoming abundant in learning area. In order to facilitate the access of digital knowledge and to meet the need of a lifelong learning, e-learning could be the helpful alternative way to the conventional learning paradigms. E-learning is known as a unifying term to express online, web-based and technology-delivered learning. Mobile-learning (m-learning) is defined as e-learning through mobile devices using wireless transmission. In a survey, more than half of the people remarked that the re-consumption was one of the convenient features in e-learning. However, it is not easy to find user's preferred segmentation from a full version of lengthy e-learning content. Especially in m-learning, a content-summarization method is strongly required because mobile devices are limited to low processing power and battery capacity. In this paper, we propose a new user preference model for re-consumption to construct personalized summarization for re-consumption. The user preference for re-consumption is modeled based on user actions with statistical model. Based on the user preference model for re-consumption with personalized user actions, our method discriminates preferred parts over the entire content. Experimental results demonstrated successful personalized summarization.
Exploring student learning profiles in algebra-based studio physics: A person-centered approach

NASA Astrophysics Data System (ADS)

Pond, Jarrad W. T.; Chini, Jacquelyn J.

2017-06-01

In this study, we explore the strategic self-regulatory and motivational characteristics of students in studio-mode physics courses at three universities with varying student populations and varying levels of success in their studio-mode courses. We survey students using questions compiled from several existing questionnaires designed to measure students' study strategies, attitudes toward and motivations for learning physics, organization of scientific knowledge, experiences outside the classroom, and demographics. Using a person-centered approach, we utilize cluster analysis methods to group students into learning profiles based on their individual responses to better understand the strategies and motives of algebra-based studio physics students. Previous studies have identified five distinct learning profiles across several student populations using similar methods. We present results from first-semester and second-semester studio-mode introductory physics courses across three universities. We identify these five distinct learning profiles found in previous studies to be present within our population of introductory physics students. In addition, we investigate interactions between these learning profiles and student demographics. We find significant interactions between a student's learning profile and their experience with high school physics, major, gender, grade expectation, and institution. Ultimately, we aim to use this method of analysis to take the characteristics of students into account in the investigation of successful strategies for using studio methods of physics instruction within and across institutions.
Problem-Based Learning Model Used to Scientific Approach Based Worksheet for Physics to Develop Senior High School Students Characters

NASA Astrophysics Data System (ADS)

Yulianti, D.

2017-04-01

The purpose of this study is to explore the application of Problem Based Learning(PBL) model aided withscientific approach and character integrated physics worksheets (LKS). Another purpose is to investigate the increase in cognitive and psychomotor learning outcomes and to know the character development of students. The method used in this study was the quasi-experiment. The instruments were observation and cognitive test. Worksheets can improve students’ cognitive, psychomotor learning outcomes. Improvements in cognitive learning results of students who have learned using worksheets are higher than students who received learning without worksheets. LKS can also develop the students’ character.
Special Focus: Effective Instruction in Reading. Strategies for Vocabulary Instruction.

ERIC Educational Resources Information Center

Peters, Ellen, Ed.; Dixon, Robert

1987-01-01

Research based suggestions are presented for effective vocabulary instruction strategies, including: learning new labels; learning concepts; and learning to learn meanings. Regardless of the method chosen, it is crucial that students: demonstrate generalization abilities; be given time to learn new material; periodically review what they learn;…
E-learning and nursing assessment skills and knowledge - An integrative review.

PubMed

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

2018-07-01

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

PubMed

Zhang, Kunpeng; Xie, Yusheng; Yang, Yi; Sun, Aaron; Liu, Hengchang; Choudhary, Alok

2014-10-01

Many machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on conditional random fields to incorporate sentence structure and context information in addition to syntactic information for improving sentiment identification. We also investigate how human interaction affects the accuracy of sentiment labeling using limited training data. We propose and evaluate two different active learning strategies for labeling sentiment data. Our experiments with the proposed approach demonstrate a 5%-15% improvement in accuracy on Amazon customer reviews compared to existing supervised learning and rule-based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.
Research-based active-learning instruction in physics

NASA Astrophysics Data System (ADS)

Meltzer, David E.; Thornton, Ronald K.

2013-04-01

The development of research-based active-learning instructional methods in physics has significantly altered the landscape of U.S. physics education during the past 20 years. Based on a recent review [D.E. Meltzer and R.K. Thornton, Am. J. Phys. 80, 478 (2012)], we define these methods as those (1) explicitly based on research in the learning and teaching of physics, (2) that incorporate classroom and/or laboratory activities that require students to express their thinking through speaking, writing, or other actions that go beyond listening and the copying of notes, or execution of prescribed procedures, and (3) that have been tested repeatedly in actual classroom settings and have yielded objective evidence of improved student learning. We describe some key features common to methods in current use. These features focus on (a) recognizing and addressing students' physics ideas, and (b) guiding students to solve problems in realistic physical settings, in novel and diverse contexts, and to justify or explain the reasoning they have used.
Enhancing students’ mathematical representation and selfefficacy through situation-based learning assisted by geometer’s sketchpad program

NASA Astrophysics Data System (ADS)

Sowanto; Kusumah, Y. S.

2018-05-01

This research was conducted based on the problem of a lack of students’ mathematical representation ability as well as self-efficacy in accomplishing mathematical tasks. To overcome this problem, this research used situation-based learning (SBL) assisted by geometer’s sketchpad program (GSP). This research investigated students’ improvement of mathematical representation ability who were taught under situation-based learning (SBL) assisted by geometer’s sketchpad program (GSP) and regular method that viewed from the whole students’ prior knowledge (high, average, and low level). In addition, this research investigated the difference of students’ self-efficacy after learning was given. This research belongs to quasi experiment research using non-equivalent control group design with purposive sampling. The result of this research showed that students’ enhancement in their mathematical representation ability taught under SBL assisted by GSP was better than the regular method. Also, there was no interaction between learning methods and students prior knowledge in student’ enhancement of mathematical representation ability. There was significant difference of students’ enhancement of mathematical representation ability taught under SBL assisted by GSP viewed from students’ prior knowledge. Furthermore, there was no significant difference in terms of self-efficacy between those who were taught by SBL assisted by GSP with the regular method.
Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

NASA Astrophysics Data System (ADS)

Armstrong, David J.; Günther, Maximilian N.; McCormac, James; Smith, Alexis M. S.; Bayliss, Daniel; Bouchy, François; Burleigh, Matthew R.; Casewell, Sarah; Eigmüller, Philipp; Gillen, Edward; Goad, Michael R.; Hodgkin, Simon T.; Jenkins, James S.; Louden, Tom; Metrailler, Lionel; Pollacco, Don; Poppenhaeger, Katja; Queloz, Didier; Raynard, Liam; Rauer, Heike; Udry, Stéphane; Walker, Simon R.; Watson, Christopher A.; West, Richard G.; Wheatley, Peter J.

2018-05-01

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.
Comparing student achievement in the problem-based learning classroom and traditional teaching methods classroom

NASA Astrophysics Data System (ADS)

Dobbs, Vicki

Significant numbers of students fail high school chemistry, preventing them from graduating. Starting in the 2013-2014 school year, 100% of the students must pass a science assessment for schools to meet Adequate Yearly Progress (AYP) in accordance to No Child Left Behind (NCLB). Failure to meet AYP results in sanctions, such as state management or closure of a school or replacing a school staff. The purpose of this study was to determine whether the teaching strategy, Problem Based Learning (PBL), will improve student achievement in high school chemistry to a greater degree than traditional teaching methods. PBL is a student-centered, inquiry-based teaching method based on the constructivist learning theory. The research question looked at whether there was a difference in student achievement between students a high school chemistry classroom using PBL and students in a classroom using traditional teaching methods as measured by scores on a 20-question quiz. The research study used a quasi-experimental pretest/posttest control group design. An independent samples t-test compared gains scores between the pretest and posttest. Analysis of quiz scores indicated that there was not a significant difference (t(171) = 1.001, p = .318) in student achievement between the teaching methods. Because there was not a significant difference, each teacher can decide which teaching method best suites the subject matter and the learning styles of the students. This study adds research based data to help teachers and schools choose one teaching method over another so that students may gain knowledge, develop problem-solving skills, and life-long learning skills that will bring about social change in the form of a higher quality of life for the students and community as a whole.
Comparing the Long-Term Retention of a Physiology Course for Medical Students with the Traditional and Problem-Based Learning

ERIC Educational Resources Information Center

Pourshanazari, A. A.; Roohbakhsh, A.; Khazaei, M.; Tajadini, H.

2013-01-01

The rapid improvements in medical sciences and the ever-increasing related data, however, require novel methods of instruction. One such method, which has been given less than due attention in Iran, is problem-based learning (PBL). In this study, we aimed to evaluate the impact of study skills and the PBL methods on short and long-term retention…
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.
Comparing problem-based learning and lecture as methods to teach whole-systems design to engineering students

NASA Astrophysics Data System (ADS)

Dukes, Michael Dickey

The objective of this research is to compare problem-based learning and lecture as methods to teach whole-systems design to engineering students. A case study, Appendix A, exemplifying successful whole-systems design was developed and written by the author in partnership with the Rocky Mountain Institute. Concepts to be tested were then determined, and a questionnaire was developed to test students' preconceptions. A control group of students was taught using traditional lecture methods, and a sample group of students was taught using problem-based learning methods. After several weeks, the students were given the same questionnaire as prior to the instruction, and the data was analyzed to determine if the teaching methods were effective in correcting misconceptions. A statistically significant change in the students' preconceptions was observed in both groups on the topic of cost related to the design process. There was no statistically significant change in the students' preconceptions concerning the design process, technical ability within five years, and the possibility of drastic efficiency gains with current technologies. However, the results were inconclusive in determining that problem-based learning is more effective than lecture as a method for teaching the concept of whole-systems design, or vice versa.
MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

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

Xu, Q; Stanford University School of Medicine, Stanford, CA; Liu, H

Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channelmore » and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral CT. This work is partially supported by the National Natural Science Foundation of China (No. 61302136), and the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2014JQ8317).« less
Higher order thinking skills: using e-portfolio in project-based learning

NASA Astrophysics Data System (ADS)

Lukitasari, M.; Handhika, J.; Murtafiah, W.

2018-03-01

The purpose of this research is to describe students' higher-order thinking skills through project-based learning using e-portfolio. The method used in this research is descriptive qualitative method. The research instruments used were test, unstructured interview, and documentation. Research subjects were students of mathematics, physics and biology education department who take the Basics Physics course. The result shows that through project-based learning using e-portfolio the students’ ability to: analyze (medium category, N-Gain 0.67), evaluate (medium category, N-Gain 0.51), and create (medium Category, N-Gain 0.44) are improved.
Digital Games: Changing Education, One Raid at a Time

ERIC Educational Resources Information Center

Pivec, Paul; Pivec, Maja

2011-01-01

Digital Games are becoming a new form of interactive content and game playing provides an interactive and collaborative platform for learning purposes. Collaborative learning allows participants to produce new ideas as well as to exchange information, simplify problems, and resolve the tasks. Context based collaborative learning method is based on…

Exploring the Effects of Project-Based Learning in Secondary Mathematics Education

ERIC Educational Resources Information Center

Holmes, Vicki-Lynn; Hwang, Yooyeun

2016-01-01

This mixed-method, longitudinal study investigated the benefits of project-based learning (PBL) on secondary-mathematics students' academic skill development and motivated strategies for learning (i.e., cognitive, social, and motivational). The focus of this study was academic skill development (algebra- and geometry-assessment scores) and other…
Discussion Based Fish Bowl Strategy in Learning Psychology

ERIC Educational Resources Information Center

Singaravelu, G.

2007-01-01

The present study investigates the learning problems in psychology at Master of Education(M.Ed.,) in Bharathiar University and finds the effectiveness of Discussion Based Fish Bowl Strategy in learning psychology. Single group Experimental method was adopted for the study. Both qualitative and quantitative approaches were adopted for this study.…
Enhancing Teacher Education Students' Generic Skills through Problem-Based Learning

ERIC Educational Resources Information Center

Murray-Harvey, Rosalind; Curtis, David D.; Cattley, Georgina; Slee, Phillip T.

2005-01-01

Claims made for the value of problem-based learning (PBL) as an effective method for professional education programmes draw on constructivist principles of teaching and learning to achieve essential content knowledge, higher order thinking skills, and a team approach to problem-solving through the interdisciplinary, student-directed study of…
Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.
Qualitative Analysis of Student Perceptions Comparing Team-based Learning and Traditional Lecture in a Pharmacotherapeutics Course.

PubMed

Remington, Tami L; Bleske, Barry E; Bartholomew, Tracy; Dorsch, Michael P; Guthrie, Sally K; Klein, Kristin C; Tingen, Jeffrey M; Wells, Trisha D

2017-04-01

Objective. To qualitatively compare students' attitudes and perceptions regarding team-based learning (TBL) and lecture. Design. Students were exposed to TBL and lecture in an elective pharmacotherapeutics course in a randomized, prospective, cross-over design. After completing the course, students provided their attitudes and perceptions through a written self-reflection and narrative questions on the end-of-course evaluation. Student responses were reviewed using a grounded theory coding method. Assessment. Students' responses yielded five major themes: impact of TBL on learning, perceptions about TBL learning methods, changes in approaches to learning, building skills for professional practice, and enduring challenges. Overall, students report TBL enhances their learning of course content (knowledge and application), teamwork skills, and lifelong learning skills. Conclusion. Students' attitudes and perceptions support TBL as a viable pedagogy for teaching pharmacotherapeutics.
Qualitative Analysis of Student Perceptions Comparing Team-based Learning and Traditional Lecture in a Pharmacotherapeutics Course

PubMed Central

Bleske, Barry E.; Bartholomew, Tracy; Dorsch, Michael P.; Guthrie, Sally K.; Klein, Kristin C.; Tingen, Jeffrey M.; Wells, Trisha D.

2017-01-01

Objective. To qualitatively compare students’ attitudes and perceptions regarding team-based learning (TBL) and lecture. Design. Students were exposed to TBL and lecture in an elective pharmacotherapeutics course in a randomized, prospective, cross-over design. After completing the course, students provided their attitudes and perceptions through a written self-reflection and narrative questions on the end-of-course evaluation. Student responses were reviewed using a grounded theory coding method. Assessment. Students’ responses yielded five major themes: impact of TBL on learning, perceptions about TBL learning methods, changes in approaches to learning, building skills for professional practice, and enduring challenges. Overall, students report TBL enhances their learning of course content (knowledge and application), teamwork skills, and lifelong learning skills. Conclusion. Students’ attitudes and perceptions support TBL as a viable pedagogy for teaching pharmacotherapeutics. PMID:28496275
Automatic labeling of MR brain images through extensible learning and atlas forests.

PubMed

Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

2017-12-01

Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic atlas datasets and obtain accurate results. © 2017 American Association of Physicists in Medicine.
The Effect of Animation in Multimedia Computer-Based Learning and Learning Style to the Learning Results

ERIC Educational Resources Information Center

Rusli, Muhammad; Negara, I. Komang Rinartha Yasa

2017-01-01

The effectiveness of a learning depends on four main elements, they are content, desired learning outcome, instructional method and the delivery media. The integration of those four elements can be manifested into a learning module which is called multimedia learning or learning by using multimedia. In learning context by using computer-based…
Teaching Power Electronics with a Design-Oriented, Project-Based Learning Method at the Technical University of Denmark

ERIC Educational Resources Information Center

Zhang, Zhe; Hansen, Claus Thorp; Andersen, Michael A. E.

2016-01-01

Power electronics is a fast-developing technology within the electrical engineering field. This paper presents the results and experiences gained from applying design-oriented project-based learning to switch-mode power supply design in a power electronics course at the Technical University of Denmark (DTU). Project-based learning (PBL) is known…
Hoping to Teach Someday? Inquire Within: Examining Inquiry-Based Learning with First-Semester Undergrads

ERIC Educational Resources Information Center

Byker, Erik Jon; Coffey, Heather; Harden, Susan; Good, Amy; Heafner, Tina L.; Brown, Katie E.; Holzberg, Debra

2017-01-01

Using case study method, this study examines the impact of an inquiry-based learning program among a cohort of first-semester undergraduates (n = 104) at a large public university in the southeastern United States who are aspiring to become teachers. The Boyer Commission (1999) asserted that inquiry-based learning should be the foundation of…
Engaged Learning: Impact of PBL and PjBL with Elementary and Middle Grade Students

ERIC Educational Resources Information Center

Dole, Sharon; Bloom, Lisa; Doss, Kristy K.

2017-01-01

This study used structured online interviews with teachers to examine the impact that inquiry-based teaching methods had on their students. The research question was the following: What are the effects on student learning and motivation as a result of teachers using problem-based and project-based learning? Interviews were conducted with 36…
Investigating the Effect of the Activities Based on Explanation Assisted REACT Strategy on Learning Impulse, Momentum and Collisions Topics

ERIC Educational Resources Information Center

Ültay, Eser; Alev, Nedim

2017-01-01

The purpose of this study was to investigate the effect of explanation assisted REACT strategy which was based on context-based learning approach on prospective science teachers' (PSTs) learning in impulse, momentum and collisions topics. The sequential explanatory strategy within mixed methods design was employed in this study. The first phase of…
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

PubMed

Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

PubMed Central

Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768
Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.
The Rivalry between Simulation and Problem-Based Learning: A Study of Learning Transfer in Physician Assistant Students

ERIC Educational Resources Information Center

Meyer, Kimberly E.

2010-01-01

The purpose of this dissertation was to evaluate learning transfer achieved by physician assistant students comparing two instructional methods, human patient simulation and electronic clinical case studies. This prospective, randomized, mixed-methods study utilized first and second-year physician assistant student volunteers taking a pretest and…
Blended Learning Applied to the Study of Mechanical Couplings in Engineering

ERIC Educational Resources Information Center

Cortizo, J. L.; Rodriguez, E.; Vijande, R.; Sierra, J. M.; Noriega, A.

2010-01-01

This paper begins with a brief introduction to blended learning (BL), recognising the important contributions to learning that can be obtained from the use of methods which combine the New Information and Communications Technologies (NICT) with more traditional methods. The paper goes onto describe the bases, tasks and methodology for developing…
Effects of Instruction-Supported Learning with Worked Examples in Quantitative Method Training

ERIC Educational Resources Information Center

Wagner, Kai; Klein, Martin; Klopp, Eric; Puhl, Thomas; Stark, Robin

2013-01-01

An experimental field study at a German university was conducted in order to test the effectiveness of an integrated learning environment to improve the acquisition of knowledge about empirical research methods. The integrated learning environment was based on the combination of instruction-oriented and problem-oriented design principles and…
Learning to Understand Inequality and Diversity: Getting Students Past Ideologies

ERIC Educational Resources Information Center

Goldsmith, Pat Antonio

2006-01-01

In this paper I present a pedagogical method called Writing Answers to Learn (WAL) which combines Problem-Based Learning (PBL) and Exploratory Writing to address the interrelated pedagogical problems of misconceptions, resistance, retention, and transfer. I analyze the use of this combined method in a course on racial and ethnic relations and…
Implementing Collaborative Learning across the Engineering Curriculum

ERIC Educational Resources Information Center

Ralston, Patricia A. S.; Tretter, Thomas R.; Kendall-Brown, Marie

2017-01-01

Active and collaborative teaching methods increase student learning, and it is broadly accepted that almost any active or collaborative approach will improve learning outcomes as compared to lecture. Yet, large numbers of faculty have not embraced these methods. Thus, the challenge to encourage evidence-based change in teaching is not only how to…

Does Project-Based Learning Enhance Iranian EFL Learners' Vocabulary Recall and Retention?

ERIC Educational Resources Information Center

Shafaei, Azadeh; Rahim, Hajar Abdul

2015-01-01

Vocabulary knowledge is an integral part of second/foreign language learning. Thus, using teaching methods that can help learners retain and expand their vocabulary knowledge is necessary to facilitate the language learning process. The current research investigated the effectiveness of an interactive classroom method, known as Project-Based…
Reflection-Based Learning for Professional Ethical Formation.

PubMed

Branch, William T; George, Maura

2017-04-01

One way practitioners learn ethics is by reflecting on experience. They may reflect in the moment (reflection-in-action) or afterwards (reflection-on-action). We illustrate how a teaching clinician may transform relationships with patients and teach person-centered care through reflective learning. We discuss reflective learning pedagogies and present two case examples of our preferred method, guided group reflection using narratives. This method fosters moral development alongside professional identity formation in students and advanced learners. Our method for reflective learning addresses and enables processing of the most pressing ethical issues that learners encounter in practice. © 2017 American Medical Association. All Rights Reserved.
Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.
Effects of Mobile Phone-Based App Learning Compared to Computer-Based Web Learning on Nursing Students: Pilot Randomized Controlled Trial

PubMed Central

2015-01-01

Objectives This study aimed to determine the effect of mobile-based discussion versus computer-based discussion on self-directed learning readiness, academic motivation, learner-interface interaction, and flow state. Methods This randomized controlled trial was conducted at one university. Eighty-six nursing students who were able to use a computer, had home Internet access, and used a mobile phone were recruited. Participants were randomly assigned to either the mobile phone app-based discussion group (n = 45) or a computer web-based discussion group (n = 41). The effect was measured at before and after an online discussion via self-reported surveys that addressed academic motivation, self-directed learning readiness, time distortion, learner-learner interaction, learner-interface interaction, and flow state. Results The change in extrinsic motivation on identified regulation in the academic motivation (p = 0.011) as well as independence and ability to use basic study (p = 0.047) and positive orientation to the future in self-directed learning readiness (p = 0.021) from pre-intervention to post-intervention was significantly more positive in the mobile phone app-based group compared to the computer web-based discussion group. Interaction between learner and interface (p = 0.002), having clear goals (p = 0.012), and giving and receiving unambiguous feedback (p = 0.049) in flow state was significantly higher in the mobile phone app-based discussion group than it was in the computer web-based discussion group at post-test. Conclusions The mobile phone might offer more valuable learning opportunities for discussion teaching and learning methods in terms of self-directed learning readiness, academic motivation, learner-interface interaction, and the flow state of the learning process compared to the computer. PMID:25995965
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.
Automated Decomposition of Model-based Learning Problems

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Millar, Bill

1996-01-01

A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.
Human resource recommendation algorithm based on ensemble learning and Spark

NASA Astrophysics Data System (ADS)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.
Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.

PubMed

Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong; Seong, Joon-Kyung

2014-01-01

Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.
Osteoporosis risk prediction using machine learning and conventional methods.

PubMed

Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

2013-01-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
Applying problem-based learning to otolaryngology teaching.

PubMed

Abou-Elhamd, K A; Rashad, U M; Al-Sultan, A I

2011-02-01

Undergraduate medical education requires ongoing improvement in order to keep pace with the changing demands of twenty-first century medical practice. Problem-based learning is increasingly being adopted in medical schools worldwide. We review its application in the specialty of ENT, and we present our experience of using this approach combined with more traditional methods. We introduced problem-based learning techniques into the ENT course taught to fifth-year medical students at Al-Ahsa College of Medicine, King Faisal University, Saudi Arabia. As a result, the teaching schedule included both clinical and theoretical activities. Six clinical teaching days were allowed for history-taking, examination techniques and clinical scenario discussion. Case scenarios were discussed in small group teaching sessions. Conventional methods were employed to teach audiology and ENT radiology (one three-hour session each); a three-hour simulation laboratory session and three-hour student presentation were also scheduled. In addition, students attended out-patient clinics for three days, and used multimedia facilities to learn about various otolaryngology diseases (in another three-hour session). This input was supplemented with didactic teaching in the form of 16 instructional lectures per semester (one hour per week). From our teaching experience, we believe that the application of problem-based learning to ENT teaching has resulted in a substantial increase in students' knowledge. Furthermore, students have given encouraging feedback on their experience of combined problem-based learning and conventional teaching methods.
The effects of team-based learning techniques on nursing students’ perception of the psycho-social climate of the classroom

PubMed Central

Koohestani, Hamid Reza; Baghcheghi, Nayereh

2016-01-01

Background: Team-based learning is a structured type of cooperative learning that is becoming increasingly more popular in nursing education. This study compares levels of nursing students’ perception of the psychosocial climate of the classroom between conventional lecture group and team-based learning group. Methods: In a quasi-experimental study with pretest-posttest design 38 nursing students of second year participated. One half of the 16 sessions of cardiovascular disease nursing course sessions was taught by lectures and the second half with team-based learning. The modified college and university classroom environment inventory (CUCEI) was used to measure the perception of classroom environment. This was completed after the final lecture and TBL sessions. Results: Results revealed a significant difference in the mean scores of psycho-social climate for the TBL method (Mean (SD): 179.8(8.27)) versus the mean score for the lecture method (Mean (SD): 154.213.44)). Also, the results showed significant differences between the two groups in the innovation (p<0.001), student cohesiveness (p=0.01), cooperation (p<0.001) and equity (p= 0.03) sub-scales scores (p<0.05). Conclusion: This study provides evidence that team-based learning does have a positive effect on nursing students’ perceptions of their psycho-social climate of the classroom. PMID:28210602
Safe semi-supervised learning based on weighted likelihood.

PubMed

Kawakita, Masanori; Takeuchi, Jun'ichi

2014-05-01

We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')
Mathematical Critical Thinking and Curiosity Attitude in Problem Based Learning and Cognitive Conflict Strategy: A Study in Number Theory Course

ERIC Educational Resources Information Center

Zetriuslita; Wahyudin; Jarnawi

2017-01-01

This research aims to describe and analyze result of applying Problem-Based Learning and Cognitive Conflict Strategy (PBLCCS) in increasing students' Mathematical Critical Thinking (MCT) ability and Mathematical Curiosity Attitude (MCA). Adopting a quasi-experimental method with pretest-posttest control group design and using mixed method with…
Teaching Gases through Problem-Based Learning

ERIC Educational Resources Information Center

Baran, Mukadder

2016-01-01

The purpose of this study was to investigate not only the applicability of the method of Problem-Based Learning (PBL) to the lesson subject of "Gasses" within the scope of the 9th grade course of Chemistry in Hakkari Gazi High School but also the influence of this method on the students' achievement levels in chemistry and on their…
Just-in-Time Teaching, Just-in-Need Learning: Designing towards Optimized Pedagogical Outcomes

ERIC Educational Resources Information Center

Killi, Steinar; Morrison, Andrew

2015-01-01

Teaching methods are constantly being changed, new ones are developed and old methods have undergone a renaissance. Two main approaches to teaching prevail: a) lecture-based and project-based and b) an argumentative approach to known knowledge or learning by exploration. Today, there is a balance between these two approaches, and they are more…
The Effects of Jigsaw Technique Based on Cooperative Learning on Prospective Science Teachers' Science Process Skill

ERIC Educational Resources Information Center

Karacop, Ataman; Diken, Emine Hatun

2017-01-01

The purpose of this study is to investigate the effects of laboratory approach based on jigsaw method with cooperative learning and confirmatory laboratory approach on university students' cognitive process development in Science teaching laboratory applications, and to determine the opinions of the students on applied laboratory methods. The…
Small-Group Problem-Based Learning as a Complex Adaptive System

ERIC Educational Resources Information Center

Mennin, Stewart

2007-01-01

Small-group problem-based learning (PBL) is widely embraced as a method of study in health professions schools and at many different levels of education. Complexity science provides a different lens with which to view and understand the application of this method. It presents new concepts and vocabulary that may be unfamiliar to practitioners of…
Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

2015-01-01

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.
Promoting clinical competence: using scaffolded instruction for practice-based learning.

PubMed

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

2007-01-01

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

PubMed

Ombelli, Julien; Pasche, Olivier; Sohrmann, Marc; Monti, Matteo

2015-05-13

INTERMED training implies a three week course, integrated in the "primary care module" for medical students in the first master year at the school of medicine in Lausanne. INTERMED uses an innovative teaching method based on repetitive sequences of e-learning-based individual learning followed by collaborative learning activities in teams, named Team-based learning (TBL). The e-learning takes place in a web-based virtual learning environment using a series of interactive multimedia virtual patients. By using INTERMED students go through a complete medical encounter applying clinical reasoning and choosing the diagnostic and therapeutic approach. INTERMED offers an authentic experience in an engaging and safe environment where errors are allowed and without consequences.

Integrating the ACR Appropriateness Criteria Into the Radiology Clerkship: Comparison of Didactic Format and Group-Based Learning.

PubMed

Stein, Marjorie W; Frank, Susan J; Roberts, Jeffrey H; Finkelstein, Malka; Heo, Moonseong

2016-05-01

The aim of this study was to determine whether group-based or didactic teaching is more effective to teach ACR Appropriateness Criteria to medical students. An identical pretest, posttest, and delayed multiple-choice test was used to evaluate the efficacy of the two teaching methods. Descriptive statistics comparing test scores were obtained. On the posttest, the didactic group gained 12.5 points (P < .0001), and the group-based learning students gained 16.3 points (P < .0001). On the delayed test, the didactic group gained 14.4 points (P < .0001), and the group-based learning students gained 11.8 points (P < .001). The gains in scores on both tests were statistically significant for both groups. However, the differences in scores were not statistically significant comparing the two educational methods. Compared with didactic lectures, group-based learning is more enjoyable, time efficient, and equally efficacious. The choice of educational method can be individualized for each institution on the basis of group size, time constraints, and faculty availability. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.
Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

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

Guo, Yanrong; Shao, Yeqin; Gao, Yaozong

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integratemore » the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.« less
The Effect of Instructional Method on Cardiopulmonary Resuscitation Skill Performance: A Comparison Between Instructor-Led Basic Life Support and Computer-Based Basic Life Support With Voice-Activated Manikin.

PubMed

Wilson-Sands, Cathy; Brahn, Pamela; Graves, Kristal

2015-01-01

Validating participants' ability to correctly perform cardiopulmonary resuscitation (CPR) skills during basic life support courses can be a challenge for nursing professional development specialists. This study compares two methods of basic life support training, instructor-led and computer-based learning with voice-activated manikins, to identify if one method is more effective for performance of CPR skills. The findings suggest that a computer-based learning course with voice-activated manikins is a more effective method of training for improved CPR performance.
Case-Based Learning in Endocrine Physiology: An Approach toward Self-Directed Learning and the Development of Soft Skills in Medical Students

ERIC Educational Resources Information Center

Gade, Shubhada; Chari, Suresh

2013-01-01

The Medical Council of India, in the recent "Vision 2015" document, recommended curricular reforms for undergraduates. Case-based learning (CBL) is one method where students are motivated toward self-directed learning and to develop analytic and problem-solving skills. An overview of thyroid physiology was given in a didactic lecture. A…
The Effects of Mobile Natural-Science Learning Based on the 5E Learning Cycle: A Case Study

ERIC Educational Resources Information Center

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

2009-01-01

This study has three major purposes, including designing mobile natural-science learning activities that rest on the 5E Learning Cycle, examining the effects of these learning activities on students' performances of learning aquatic plants, and exploring students' perceptions toward these learning activities. A case-study method is utilized and…
Patterns of Cognitive Strengths and Weaknesses: Identification Rates, Agreement, and Validity for Learning Disabilities Identification

ERIC Educational Resources Information Center

Miciak, Jeremy; Fletcher, Jack M.; Stuebing, Karla K.; Vaughn, Sharon; Tolar, Tammy D.

2014-01-01

Few empirical investigations have evaluated learning disabilities (LD) identification methods based on a pattern of cognitive strengths and weaknesses (PSW). This study investigated the reliability and validity of two proposed PSW methods: the concordance/discordance method (C/DM) and cross battery assessment (XBA) method. Cognitive assessment…
Radiography Students' Learning: A Literature Review.

PubMed

Holmström, Anneli; Ahonen, Sanna-Mari

2016-01-01

To describe research methodology and findings concerning radiography students' learning. Health sciences databases were searched to perform a traditional narrative literature review. Thirty-five peer-reviewed articles published between 2000 and 2014 were analyzed using thematic analysis. Specific methods of learning were found to be of the most interest. The studies focused primarily on the use and usability of a method or the students' general experiences of it. The most commonly studied methods were e-learning and interprofessional learning, which students perceived as positive methods for theoretical studies and clinical training. Students' learning regarding research was the focus of only one article reporting a wide variety of students' research interests. Most studies reported quantitative research gathered from questionnaires and surveys. Additional research, especially from a qualitative point of view, is needed to deepen the evidence-based knowledge of radiography student learning.
Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

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

ERIC Educational Resources Information Center

Yang, Ya-Fei; Lee, Chien-I; Chang, Chih-Kai

2016-01-01

Collaborative learning is an activity in which two or more students learn something together. Many studies have found that collaborative learning improve students' memory retention and motivation to learn. Peer Instruction (PI) is one of the most successful evidence-based collaborative learning methods. This article investigates issues of student…
Moving Beyond the Training Room: Fostering Workplace Learning through Online Journaling

ERIC Educational Resources Information Center

Cyboran, Vincent L.

2005-01-01

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

ERIC Educational Resources Information Center

Khalil, Mahmood

2007-01-01

In this study, a learning unit on microorganisms for ninth-grade students was developed based on the Science-Technology-Environment-Society (STES) approach. The learning unit contained 15 learning tasks, which were performed in individual and cooperative learning settings, using a variety of teaching/learning methods with an emphasis on the…
Effects of case-based learning on communication skills, problem-solving ability, and learning motivation in nursing students.

PubMed

Yoo, Moon-Sook; Park, Hyung-Ran

2015-06-01

The purpose of this study was to explore the effects of case-based learning on communication skills, problem-solving ability, and learning motivation in sophomore nursing students. In this prospective, quasi-experimental study, we compared the pretest and post-test scores of an experimental group and a nonequivalent, nonsynchronized control group. Both groups were selected using convenience sampling, and consisted of students enrolled in a health communication course in the fall semesters of 2011 (control group) and 2012 (experimental group) at a nursing college in Suwon, South Korea. The two courses covered the same material, but in 2011 the course was lecture-based, while in 2012, lectures were replaced by case-based learning comprising five authentic cases of patient-nurse communication. At post-test, the case-based learning group showed significantly greater communication skills, problem-solving ability, and learning motivation than the lecture-based learning group. This finding suggests that case-based learning is an effective learning and teaching method. © 2014 Wiley Publishing Asia Pty Ltd.
Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.
Efficient model learning methods for actor-critic control.

PubMed

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.
A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

PubMed

Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei

2017-05-18

The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.
Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.
Inquiry based learning: a student centered learning to develop mathematical habits of mind

NASA Astrophysics Data System (ADS)

Handayani, A. D.; Herman, T.; Fatimah, S.; Setyowidodo, I.; Katminingsih, Y.

2018-05-01

Inquiry based learning is learning that based on understanding constructivist mathematics learning. Learning based on constructivism is the Student centered learning. In constructivism, students are trained and guided to be able to construct their own knowledge on the basis of the initial knowledge that they have before. This paper explained that inquiry based learning can be used to developing student’s Mathematical habits of mind. There are sixteen criteria Mathematical Habits of mind, among which are diligent, able to manage time well, have metacognition ability, meticulous, etc. This research method is qualitative descriptive. The result of this research is that the instruments that have been developed to measure mathematical habits of mind are validated by the expert. The conclusion is the instrument of mathematical habits of mind are valid and it can be used to measure student’s mathematical habits of mind.
Application of Model Project Based Learning on Integrated Science in Water Pollution

NASA Astrophysics Data System (ADS)

Yamin, Y.; Permanasari, A.; Redjeki, S.; Sopandi, W.

2017-09-01

The function of this research was to analyze the influence model Project Based Learning (PjBl) on integrated science about the concept mastery for junior high school students. Method used for this research constitutes the quasi of experiment method. Population and sample for this research are the students junior high school in Bandung as many as two classes to be experiment and control class. The instrument that used for this research is the test concept mastery, assessment questionnaire of product and the questionnaire responses of the student about learning integrated science. Based on the result of this research get some data that with accomplishment the model of PjBl. Learning authority of integrated science can increase the concept mastery for junior high school students. The highest increase in the theme of pollution water is in the concept of mixtures and the separation method. The students give a positive response in learning of integrated science for the theme of pollution of the water used model PjBL with questionnaire of the opinion aspect in amount of 83.5%, the anxiety of the students in amount of 95.5%, the profit learning model of PjBL in amount of 96.25% and profit learning of integrated science in amount of 95.75%.
Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

PubMed

Niegowski, Maciej; Zivanovic, Miroslav

2016-03-01

We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.
Inventing Motivates and Prepares Student Teachers for Computer-Based Learning

ERIC Educational Resources Information Center

Glogger-Frey, I.; Kappich, J.; Schwonke, R.; Holzäpfel, L.; Nückles, M.; Renkl, A.

2015-01-01

A brief, problem-oriented phase such as an inventing activity is one potential instructional method for preparing learners not only cognitively but also motivationally for learning. Student teachers often need to overcome motivational barriers in order to use computer-based learning opportunities. In a preliminary experiment, we found that student…

Project-Based Learning in Primary Schools: Effects on Pupils' Learning and Attitudes

ERIC Educational Resources Information Center

Kaldi, Stavroula; Filippatou, Diamanto; Govaris, Christos

2011-01-01

This study focuses upon the effectiveness of project-based learning on primary school pupils regarding their content knowledge and attitudes towards self-efficacy, task value, group work, teaching methods applied and peers from diverse ethnic backgrounds. A cross-curricular project was implemented within the curriculum area of environmental…
The Problem of Constructive Misalignment in International Business Education: A Three-Stage Integrated Approach to Enhancing Teaching and Learning

ERIC Educational Resources Information Center

Zhao, Shasha

2016-01-01

Past evidence suggests that constructive misalignment is particularly problematic in International Business (IB) education, though this paradigm has received limited research attention. Building on the literature of three independent teaching methods (threshold concept, problem-based learning, and technology-based learning), this study contributes…
Information Activities and Appropriation in Teacher Trainees' Digital, Group-Based Learning

ERIC Educational Resources Information Center

Hanell, Fredrik

2016-01-01

Introduction: This paper reports results from an ethnographic study of teacher trainees' information activities in digital, group-based learning and their relation to the interplay between use and appropriation of digital tools and the learning environment. Method: The participants in the present study are 249 pre-school teacher trainees in…
Undergraduate Social Work Students' Perceptions of a Team-Based Learning Approach to Exploring Adult Development

ERIC Educational Resources Information Center

Watkins, Karen; Forge, Nicholas; Lewinson, Terri; Garner, Brittany; Carter, Larance D.; Greenwald, Lindsay

2018-01-01

Social work educators are challenged to adopt innovative instructional methods and pedagogies to prepare students to meet the contemporary needs of diverse client populations. A team-based learning (TBL) approach is a pedagogical strategy that utilizes cooperative and collaborative learning principles to inspire academic, professional, and…
Theme: The 21st Century Adult Learner

ERIC Educational Resources Information Center

Lopez Brown, P.

2017-01-01

Problem-based learning is an innovative educational approach that is gaining prominence in higher education using "real world" problems or situations as a context for learning. The present study explored the use of problem-based learning with teacher trainees of the University of Belize. Using a concurrent mixed method design with 74…
Computer-Based Learning of Neuroanatomy: A Longitudinal Study of Learning, Transfer, and Retention

ERIC Educational Resources Information Center

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

2011-01-01

A longitudinal experiment was conducted to evaluate the effectiveness of new methods for learning neuroanatomy with computer-based instruction. Using a three-dimensional graphical model of the human brain and sections derived from the model, tools for exploring neuroanatomy were developed to encourage "adaptive exploration". This is an…
Efficacy of Group Based Learning in Learning Moral Value

ERIC Educational Resources Information Center

Singaravelu, G.

2008-01-01

The present study highlights the efficacy of Group Based Learning on cultivating moral value of the students at Standard VIII. Parallel group Experimental method was adopted in the study. Eighty students (control group = 40 students + experimental = 40 students) were selected as sample for the study. Researcher self-made achievement tool was…
Schema-Based Instruction on Learning English Polysemous Words: Effects of Instruction and Learners' Perceptions

ERIC Educational Resources Information Center

Mitsugi, Makoto

2017-01-01

The purpose of this study is to investigate the effectiveness of two instruction methods for teaching polysemous English prepositions ("at, in, on") and to explore learners' perception on learning tools used in the instruction when learning polysemous words. The first study investigated the effectiveness of schema-based instruction…
Developing Results-Based Leadership Attributes and Team Cohesiveness through Action Learning

ERIC Educational Resources Information Center

Troupe, David

2010-01-01

Those who develop leaders in manufacturing settings have little data that describe the usefulness of action learning as a method of developing leaders' abilities to improve results-based leadership attributes or perceptions about their team's cohesiveness. The two purposes of this study were to evaluate an action learning program with regards to…
Cooperation, Technology, and Performance: A Case Study.

ERIC Educational Resources Information Center

Cavanagh, Thomas; Dickenson, Sabrina; Brandt, Suzanne

1999-01-01

Describes the CTP (Cooperation, Technology, and Performance) model and explains how it is used by the Department of Veterans Affairs-Veteran's Benefit Administration (VBA) for training. Discusses task analysis; computer-based training; cooperative-based learning environments; technology-based learning; performance-assessment methods; courseware…
Improvement in Generic Problem-Solving Abilities of Students by Use of Tutor-Less Problem-Based Learning in a Large Classroom Setting

ERIC Educational Resources Information Center

Klegeris, Andis; Bahniwal, Manpreet; Hurren, Heather

2013-01-01

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

ERIC Educational Resources Information Center

Diegelman-Parente, Amy

2011-01-01

Mastery learning is an instructional method based on the idea that students learn best if they fully understand, or master, one concept before moving on to the next and has been shown to be extremely effective in math and science curricula. Competency-based grading is an evaluative tool that allows the faculty member to determine the level of…
A Comparison of Classroom and Online Asynchronous Problem-Based Learning for Students Undertaking Statistics Training as Part of a Public Health Masters Degree

ERIC Educational Resources Information Center

de Jong, N.; Verstegen, D. M. L.; Tan, F. E. S.; O'Connor, S. J.

2013-01-01

This case-study compared traditional, face-to-face classroom-based teaching with asynchronous online learning and teaching methods in two sets of students undertaking a problem-based learning module in the multilevel and exploratory factor analysis of longitudinal data as part of a Masters degree in Public Health at Maastricht University. Students…
Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

PubMed

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.
Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.
A comparison of in-class learner engagement across lecture, problem-based learning, and team learning using the STROBE classroom observation tool.

PubMed

Kelly, P Adam; Haidet, Paul; Schneider, Virginia; Searle, Nancy; Seidel, Charles L; Richards, Boyd F

2005-01-01

Having recently introduced team learning into the preclinical medical curriculum, evidence of the relative impact of this instructional method on in-class learner engagement was sought. To compare patterns of engagement behaviors among learners in class sessions across 3 distinct instructional methods: lecture, problem-based learning (PBL), and team learning. Trained observers used the STROBE classroom observation tool to measure learner engagement in 7 lecture, 4 PBL, and 3 team learning classrooms over a 12-month period. Proportions of different types of engagement behaviors were compared using chi-square. In PBL and team learning, the amount of learner-to-learner engagement was similar and much greater than in lecture, where most engagement was of the learner-to-instructor and self-engagement types. Also, learner-to-instructor engagement appeared greater in team learning than in PBL. Observed engagement behaviors confirm the potential of team learning to foster engagement similar to PBL, but with greater faculty input.
Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.
Multiple-instance ensemble learning for hyperspectral images

NASA Astrophysics Data System (ADS)

Ergul, Ugur; Bilgin, Gokhan

2017-10-01

An ensemble framework for multiple-instance (MI) learning (MIL) is introduced for use in hyperspectral images (HSIs) by inspiring the bagging (bootstrap aggregation) method in ensemble learning. Ensemble-based bagging is performed by a small percentage of training samples, and MI bags are formed by a local windowing process with variable window sizes on selected instances. In addition to bootstrap aggregation, random subspace is another method used to diversify base classifiers. The proposed method is implemented using four MIL classification algorithms. The classifier model learning phase is carried out with MI bags, and the estimation phase is performed over single-test instances. In the experimental part of the study, two different HSIs that have ground-truth information are used, and comparative results are demonstrated with state-of-the-art classification methods. In general, the MI ensemble approach produces more compact results in terms of both diversity and error compared to equipollent non-MIL algorithms.
Machine learning methods in chemoinformatics

PubMed Central

Mitchell, John B O

2014-01-01

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

PubMed Central

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Background: Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. Materials and Methods: A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Results: Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Conclusions: Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills. PMID:29861761

Indirect learning control for nonlinear dynamical systems

NASA Technical Reports Server (NTRS)

Ryu, Yeong Soon; Longman, Richard W.

1993-01-01

In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.
Laboratory 3.0: Manufacturing Technologies Laboratory Virtualization with a Student-Centred Methodology

ERIC Educational Resources Information Center

Fabregat-Sanjuan, Albert; Pàmies-Vilà, Rosa; Ferrando Piera, Francesc; De la Flor López, Silvia

2017-01-01

This paper presents a blended-learning strategy for improving the teaching method applied in the laboratory subject Manufacturing Technologies. The teaching method has been changed from a predominantly teacher-centred to an active learning system with a student-centred focus and e-learning activities. In face-to-face classes, a game-based learning…
Suggestive, Accelerative Learning and Teaching: A Manual of Classroom Procedures Based on the Lozanov Method.

ERIC Educational Resources Information Center

Schuster, Donald H.; And Others

The Suggestive Accelerative Learning and Teaching Method uses aspects of suggestion and unusual styles of presenting material to accelerate classroom learning. The essence of this technique is the use of a combination of physical relaxation exercises, mental concentration and suggestive principles to strengthen a person's ego and expand his memory…
Supporting Informal Learning by Traders in Investment Banks

ERIC Educational Resources Information Center

Chivers, Geoffrey

2011-01-01

Purpose: The main aims of this paper are to determine the extent to which experienced traders in investment banks based in London are learning by informal methods, which methods are to the fore, and whether HRD staff are providing support for informal learning. It also seeks to find evidence that such investment banks were attempting to become…
Transmedia Teaching Framework: From Group Projects to Curriculum Development

ERIC Educational Resources Information Center

Reid, James; Gilardi, Filippo

2016-01-01

This paper describes an innovative project-based learning framework theoretically based on the ideas of Transmedia Storytelling, Participatory Cultures and Multiple intelligences that can be integrated into the f?lipped classroom method, and practically addressed using Content- Based Instruction (CBI) and Project-Based Learning (PBL) approaches.…
Problem-based learning using patient-simulated videos showing daily life for a comprehensive clinical approach

PubMed Central

Ohira, Yoshiyuki; Uehara, Takanori; Noda, Kazutaka; Suzuki, Shingo; Shikino, Kiyoshi; Kajiwara, Hideki; Kondo, Takeshi; Hirota, Yusuke; Ikusaka, Masatomi

2017-01-01

Objectives We examined whether problem-based learning tutorials using patient-simulated videos showing daily life are more practical for clinical learning, compared with traditional paper-based problem-based learning, for the consideration rate of psychosocial issues and the recall rate for experienced learning. Methods Twenty-two groups with 120 fifth-year students were each assigned paper-based problem-based learning and video-based problem-based learning using patient-simulated videos. We compared target achievement rates in questionnaires using the Wilcoxon signed-rank test and discussion contents diversity using the Mann-Whitney U test. A follow-up survey used a chi-square test to measure students’ recall of cases in three categories: video, paper, and non-experienced. Results Video-based problem-based learning displayed significantly higher achievement rates for imagining authentic patients (p=0.001), incorporating a comprehensive approach including psychosocial aspects (p<0.001), and satisfaction with sessions (p=0.001). No significant differences existed in the discussion contents diversity regarding the International Classification of Primary Care Second Edition codes and chapter types or in the rate of psychological codes. In a follow-up survey comparing video and paper groups to non-experienced groups, the rates were higher for video (χ2=24.319, p<0.001) and paper (χ2=11.134, p=0.001). Although the video rate tended to be higher than the paper rate, no significant difference was found between the two. Conclusions Patient-simulated videos showing daily life facilitate imagining true patients and support a comprehensive approach that fosters better memory. The clinical patient-simulated video method is more practical and clinical problem-based tutorials can be implemented if we create patient-simulated videos for each symptom as teaching materials. PMID:28245193
Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.
A Meta-analysis Method to Advance Design of Technology-Based Learning Tool: Combining Qualitative and Quantitative Research to Understand Learning in Relation to Different Technology Features

NASA Astrophysics Data System (ADS)

Zhang, Lin

2014-02-01

Educators design and create various technology tools to scaffold students' learning. As more and more technology designs are incorporated into learning, growing attention has been paid to the study of technology-based learning tool. This paper discusses the emerging issues, such as how can learning effectiveness be understood in relation to different technology features? And how can pieces of qualitative and quantitative results be integrated to achieve a broader understanding of technology designs? To address these issues, this paper proposes a meta-analysis method. Detailed explanations about the structure of the methodology and its scientific mechanism are provided for discussions and suggestions. This paper ends with an in-depth discussion on the concerns and questions that educational researchers might raise, such as how this methodology takes care of learning contexts.
Elements of Scenario-Based Learning on Suicidal Patient Care Using Real-Time Video.

PubMed

Lu, Chuehfen; Lee, Hueying; Hsu, Shuhui; Shu, Inmei

2016-01-01

This study aims understanding of students' learning experiences when receiving scenario-based learning combined with real-time video. Videos that recorded student nurses intervention with a suicidal standardized patient (SP) were replayed immediately as teaching materials. Videos clips and field notes from ten classes were analysed. Investigators and method triangulation were used to boost the robustness of the study. Three key elements, emotional involvement, concretizing of the teaching material and substitute learning were identified. Emotions were evoked among the SP, the student performer and the students who were observing, thus facilitating a learning effect. Concretizing of the teaching material refers to students were able to focus on the discussions using visual and verbal information. Substitute learning occurred when the students watching the videos, both the strengths and weaknesses represented were similar to those that would be likely to occur. These key elements explicate their learning experience and suggested a strategic teaching method.
Blended Learning

ERIC Educational Resources Information Center

Halan, Deepak

2005-01-01

Blended learning basically refers to using several methods for teaching. It can be thought to be a learning program where more than one delivery mode is being used with the ultimate goal of optimizing the learning result and cost of program delivery. Examples of blended learning could be the combination of technology-based resources and…
The Development of Interactive Mathematics Learning Material Based on Local Wisdom with .swf Format

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

PubMed

Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

2015-08-13

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.
Students concept understanding of fluid static based on the types of teaching

NASA Astrophysics Data System (ADS)

Rahmawati, I. D.; Suparmi; Sunarno, W.

2018-03-01

This research aims to know the concept understanding of student are taught by guided inquiry based learning and conventional based learning. Subjects in this study are high school students as much as 2 classes and each class consists of 32 students, both classes are homogen. The data was collected by conceptual test in the multiple choice form with the students argumentation of the answer. The data analysis used is qualitative descriptive method. The results of the study showed that the average of class that was using guided inquiry based learning is 78.44 while the class with use conventional based learning is 65.16. Based on these data, the guided inquiry model is an effective learning model used to improve students concept understanding.
A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.
DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615
Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning.

PubMed

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

2016-12-01

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

PubMed

Nakamura, Yutaka; Mori, Takeshi; Sato, Masa-aki; Ishii, Shin

2007-08-01

Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the "CPG-actor-critic" method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.
Designing Low Carbon Higher Education Systems: Environmental Impacts of Campus and Distance Learning Systems

ERIC Educational Resources Information Center

Roy, Robin; Potter, Stephen; Yarrow, Karen

2008-01-01

Purpose: This paper aims to summarise the methods and main findings of a study of the environmental impacts of providing higher education (HE) courses by campus-based and distance/open-learning methods. Design/methodology/approach: The approach takes the form of an environmental audit, with data from surveys of 20 UK courses--13 campus-based,…
Project-Based Learning versus Textbook/Lecture Learning in Middle School Science

ERIC Educational Resources Information Center

Main, Sindy

2015-01-01

As schools continue to become more diverse, it is important to look at science teaching methods that will meet the needs of all students. In this study, 172 students in a middle school in Northwestern Illinois were taught using two methods of teaching science. Half of the students were taught using project-based science (PBS) and the other half of…
Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos.

PubMed

André, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2011-01-01

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available groundtruth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

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

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

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

NASA Astrophysics Data System (ADS)

Charitas Indra Prahmana, Rully; Kusumah, Yaya S.; Darhim

2017-10-01

This study aims to describe the role of research-based learning in design a learning trajectory of research in mathematics education to enhance research and academic writing skills for pre-service mathematics teachers. The method used is a design research with three stages, namely the preliminary design, teaching experiment, and retrospective analysis. The research subjects are pre-service mathematics teacher class of 2012 from one higher education institution in Tangerang - Indonesia. The use of research-based learning in designing learning trajectory of research in mathematics education plays a crucial role as a trigger to enhancing math department preservice teachers research and academic writing skills. Also, this study also describes the design principles and characteristics of the learning trajectory namely didactic trajectory generated by the role of research-based learning syntax.
Machine learning-based dual-energy CT parametric mapping

NASA Astrophysics Data System (ADS)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W.; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Helo, Rose Al; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C.; Rassouli, Negin; Gilkeson, Robert C.; Traughber, Bryan J.; Cheng, Chee-Wai; Muzic, Raymond F., Jr.

2018-06-01

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.
Machine learning-based dual-energy CT parametric mapping.

PubMed

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-06-08

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z eff ), relative electron density (ρ e ), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.
Prediction of essential proteins based on gene expression programming.

PubMed

Zhong, Jiancheng; Wang, Jianxin; Peng, Wei; Zhang, Zhen; Pan, Yi

2013-01-01

Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.
Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

PubMed

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.
Integration of Experience API Into CDET’s E-Learning

DTIC Science & Technology

2016-06-01

based on customers ’ actual usage on a transaction basis. Value-based Penetration Pricing • Market segments where buyers have high price...they need to accomplish the course learning objectives (see Figure 6). This method allows each student to customize their own learning experiences ... EXPERIENCE API INTO CDET’S E- LEARNING by Clayton C. MacAloney June 2016 Thesis Advisor: Man-Tak Shing Co-Advisor: Arijit Das THIS PAGE
Algorithm for personal identification in distance learning system based on registration of keyboard rhythm

NASA Astrophysics Data System (ADS)

Nikitin, P. V.; Savinov, A. N.; Bazhenov, R. I.; Sivandaev, S. V.

2018-05-01

The article describes the method of identifying a person in distance learning systems based on a keyboard rhythm. An algorithm for the organization of access control is proposed, which implements authentication, identification and verification of a person using the keyboard rhythm. Authentication methods based on biometric personal parameters, including those based on the keyboard rhythm, due to the inexistence of biometric characteristics without a particular person, are able to provide an advanced accuracy and inability to refuse authorship and convenience for operators of automated systems, in comparison with other methods of conformity checking. Methods of permanent hidden keyboard monitoring allow detecting the substitution of a student and blocking the key system.
Formation enthalpies for transition metal alloys using machine learning

NASA Astrophysics Data System (ADS)

Ubaru, Shashanka; Miedlar, Agnieszka; Saad, Yousef; Chelikowsky, James R.

2017-06-01

The enthalpy of formation is an important thermodynamic property. Developing fast and accurate methods for its prediction is of practical interest in a variety of applications. Material informatics techniques based on machine learning have recently been introduced in the literature as an inexpensive means of exploiting materials data, and can be used to examine a variety of thermodynamics properties. We investigate the use of such machine learning tools for predicting the formation enthalpies of binary intermetallic compounds that contain at least one transition metal. We consider certain easily available properties of the constituting elements complemented by some basic properties of the compounds, to predict the formation enthalpies. We show how choosing these properties (input features) based on a literature study (using prior physics knowledge) seems to outperform machine learning based feature selection methods such as sensitivity analysis and LASSO (least absolute shrinkage and selection operator) based methods. A nonlinear kernel based support vector regression method is employed to perform the predictions. The predictive ability of our model is illustrated via several experiments on a dataset containing 648 binary alloys. We train and validate the model using the formation enthalpies calculated using a model by Miedema, which is a popular semiempirical model used for the prediction of formation enthalpies of metal alloys.
Learning and dynamics in social systems. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Dolfin, Marina

2016-03-01

The interesting novelty of the paper by Burini et al. [1] is that the authors present a survey and a new approach of collective learning based on suitable development of methods of the kinetic theory [2] and theoretical tools of evolutionary game theory [3]. Methods of statistical dynamics and kinetic theory lead naturally to stochastic and collective dynamics. Indeed, the authors propose the use of games where the state of the interacting entities is delivered by probability distributions.
Statistical Mechanics of the Delayed Reward-Based Learning with Node Perturbation

NASA Astrophysics Data System (ADS)

Hiroshi Saito,; Kentaro Katahira,; Kazuo Okanoya,; Masato Okada,

2010-06-01

In reward-based learning, reward is typically given with some delay after a behavior that causes the reward. In machine learning literature, the framework of the eligibility trace has been used as one of the solutions to handle the delayed reward in reinforcement learning. In recent studies, the eligibility trace is implied to be important for difficult neuroscience problem known as the “distal reward problem”. Node perturbation is one of the stochastic gradient methods from among many kinds of reinforcement learning implementations, and it searches the approximate gradient by introducing perturbation to a network. Since the stochastic gradient method does not require a objective function differential, it is expected to be able to account for the learning mechanism of a complex system, like a brain. We study the node perturbation with the eligibility trace as a specific example of delayed reward-based learning, and analyzed it using a statistical mechanics approach. As a result, we show the optimal time constant of the eligibility trace respect to the reward delay and the existence of unlearnable parameter configurations.
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

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

2017-12-04

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

PubMed

Chan, Zenobia C Y

2013-08-01

The implementation of art education in nursing is said to have positive effects on nursing students. Most studies applied visual art dialogues or object design, whereas the effectiveness of drawing as a teaching and learning method is rarely examined and discussed. This paper aimed to discuss the potential and effectiveness of drawing as a learning and teaching method. Four drawings which were created by Hong Kong nursing students are demonstrated and the students' perspectives of how drawing enhanced learning are shown in this paper. Topics on drawing as a fun teaching and learning method and the way it can enhance critical thinking and creativity are also discussed. In conclusion, the activity was a great success, and students enjoyed the learning process and reflected positive comments. However, we cannot conclude that drawing is an effective teaching and learning method based on a single paper, therefore more similar studies should be conducted to investigate this method. Copyright © 2012 Elsevier Ltd. All rights reserved.
Prior Learning Assessment: How Institutions Use Portfolio Assessments

ERIC Educational Resources Information Center

Klein-Collins, Becky; Hain, Patrick

2009-01-01

The term Prior Learning Assessment (PLA) refers not to a single kind of assessment but rather an entire family of assessment methods that can be used by institutions. Some of these methods are exam-based. In addition, there are other methods of PLA. One of the more innovative methods of offering PLA, however, is through the development of student…
A comparison of classroom and online asynchronous problem-based learning for students undertaking statistics training as part of a Public Health Masters degree.

PubMed

de Jong, N; Verstegen, D M L; Tan, F E S; O'Connor, S J

2013-05-01

This case-study compared traditional, face-to-face classroom-based teaching with asynchronous online learning and teaching methods in two sets of students undertaking a problem-based learning module in the multilevel and exploratory factor analysis of longitudinal data as part of a Masters degree in Public Health at Maastricht University. Students were allocated to one of the two study variants on the basis of their enrolment status as full-time or part-time students. Full-time students (n = 11) followed the classroom-based variant and part-time students (n = 12) followed the online asynchronous variant which included video recorded lectures and a series of asynchronous online group or individual SPSS activities with synchronous tutor feedback. A validated student motivation questionnaire was administered to both groups of students at the start of the study and a second questionnaire was administered at the end of the module. This elicited data about student satisfaction with the module content, teaching and learning methods, and tutor feedback. The module coordinator and problem-based learning tutor were also interviewed about their experience of delivering the experimental online variant and asked to evaluate its success in relation to student attainment of the module's learning outcomes. Student examination results were also compared between the two groups. Asynchronous online teaching and learning methods proved to be an acceptable alternative to classroom-based teaching for both students and staff. Educational outcomes were similar for both groups, but importantly, there was no evidence that the asynchronous online delivery of module content disadvantaged part-time students in comparison to their full-time counterparts.
Blended Learning Approach for Enhancing Students' Learning Experiences in a Knowledge Society

ERIC Educational Resources Information Center

Suprabha, K.; Subramonian, G.

2015-01-01

Blended learning which, its name suggests, blends online learning with traditional methods of learning and development. It is a new instructional strategy, based on the non-linear and interactive features of the digital learning and instruction through the web. Exploring the literature review, the purpose of the study was to get a deeper…
A Mixed-Methods Investigation of Clicker Implementation Styles in STEM.

PubMed

Solomon, Erin D; Repice, Michelle D; Mutambuki, Jacinta M; Leonard, Denise A; Cohen, Cheryl A; Luo, Jia; Frey, Regina F

2018-06-01

Active learning with clickers is a common approach in high-enrollment, lecture-based courses in science, technology, engineering, and mathematics. In this study, we describe the procedures that faculty at one institution used when implementing clicker-based active learning, and how they situated these activities in their class sessions. Using a mixed-methods approach, we categorized faculty into four implementation styles based on quantitative observation data and conducted qualitative interviews to further understand why faculty used these styles. We found that faculty tended to use similar procedures when implementing a clicker activity, but differed on how they situated the clicker-based active learning into their courses. These variations were attributed to different faculty goals for using clicker-based active learning, with some using it to engage students at specific time points throughout their class sessions and others who selected it as the best way to teach a concept from several possible teaching techniques. Future research should continue to investigate and describe how active-learning strategies from literature may differ from what is being implemented.
Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning.

PubMed

Hagita, Katsumi; Higuchi, Takeshi; Jinnai, Hiroshi

2018-04-12

Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image. Here, we propose a new approach based on an image-processing or deep-learning-based method for super-resolution of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. The deep-learning-based method learns from high-resolution sub-images obtained via SEM and recovers low-resolution sub-images parallel to the FIB milling direction. The 3D morphologies of polymeric nano-composites are used as test images, which are subjected to the deep-learning-based method as well as conventional methods. We find that the former yields superior restoration, particularly as the asymmetric resolution is increased. Our super-resolution approach for images having asymmetric resolution enables observation time reduction.
Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

PubMed

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.
Nursing students' perceptions of effective problem-based learning tutors.

PubMed

Matthew-Maich, Nancy; Martin, Lynn; Hammond, Cynthia; Palma, Amy; Pavkovic, Maria; Sheremet, Darlene; Roche, Carmen

2016-11-16

Aim To explore baccalaureate nursing students' perceptions of what makes an effective tutor in problem-based learning courses, and the influence of effective teaching on students' learning and experience. Method Students enrolled in all four years of a baccalaureate nursing programme completed online surveys (n=511) and participated in focus groups (n=19). Data were analysed and combined using content analysis. Findings The data were summarised using five themes, the '5 Ps' of effective teaching in problem-based learning. Nursing students perceived effective problem-based learning tutors to be prepared with knowledge and facilitation skills, person-centred, passionate, professional and able to prepare students for success in the nursing programme. Effective tutors adjusted their approaches to students throughout the four years of the nursing programme. Conclusion Effective teaching in problem-based learning is essential and has significant effects on nursing students' learning, motivation and experience. Important attributes, skills and strategies of effective problem-based learning tutors were identified and may be used to enhance teaching and plan professional development initiatives.

The effect of web quest and team-based learning on students' self-regulation.

PubMed

Badiyepeymaie Jahromi, Zohreh; Mosalanejad, Leili; Rezaee, Rita

2016-04-01

In this study, the authors aimed to examine the effects of cooperative learning methods using Web Quest and team-based learning on students' self-direction, self-regulation, and academic achievement. This is a comparative study of students taking a course in mental health and psychiatric disorders. In two consecutive years, a group of students were trained using the WebQuest approach as a teaching strategy (n = 38), while the other group was taught using team-based learning (n=39). Data gathering was based on Guglielmino's self-directed learning readiness scale (SDLRS) and Buford's self-regulation questionnaire. The data were analyzed by descriptive test using M (IQR), Wilcoxon signed-rank test, and the Mann-Whitney U-test in SPSS software, version 13. p<0.05 was considered as the significance level. The results of the Mann-Whitney U test showed that the participants' self- directed (self-management) and self-regulated learning differed between the two groups (p=0.04 and p=0.01, respectively). Wilcoxon test revealed that self-directed learning indices (self-control and self-management) were differed between the two strategies before and after the intervention. However, the scores related to learning (students' final scores) were higher in the WebQuest approach than in team-based learning. By employing modern educational approaches, students are not only more successful in their studies but also acquire the necessary professional skills for future performance. Further research to compare the effects of new methods of teaching is required.
Strengths and weaknesses of Problem Based Learning from the professional perspective of registered nurses 1

PubMed Central

Cónsul-Giribet, María; Medina-Moya, José Luis

2014-01-01

OBJECTIVE: to identify competency strengths and weaknesses as perceived by nursing professionals who graduated with a integrated curriculum and competency-based through Problem Based Learning in small groups. METHOD: an intrinsic case study method was used, which analyzes this innovation through former students (from the first class) with three years of professional experience. The data were collected through a questionnaire and discussion groups. RESULTS: the results show that their competency level is valued in a very satisfactory manner. This level paradoxically contrasts with the lack of theoretical knowledge they perceived at the end of their education, when they started working in clinical practice. CONCLUSIONS: the teaching strategy was key to motivate an in-depth study and arouse the desire to know. In addition, Problem Based Learning favors and reinforces the decision to learn, which is that necessary in the course of professional life. PMID:25493666
Drilling Students’ Communication Skill through Science, Environment, Technology, and Society (SETS)-Based Learning

NASA Astrophysics Data System (ADS)

Al-Farisi, B. L.; Tjandrakirana; Agustini, R.

2018-01-01

Student’s communication skill paid less attention in learning activity at school, even though communication skill is needed by students in the 21st century based on the demands of new curriculum in Indonesia (K13). This study focuses on drilling students’ communication skill through science, environment, technology, and society (SETS)-based learning. The research is a pre-experimental design with a one-shot case study model involving 10 students of ninth-grader of SMPN 2 Manyar, Gresik. The research data were collected through observation method using communication observation sheet. The data were analyzed using the descriptive qualitative method. The result showed that students’ communication skill reached the completeness of skills decided both individually and classically in the curriculum. The fundamental result of this research that SETS-based learning can be used to drill students’ communication skill in K13 context.
Training, Simulation, the Learning Curve, and How to Reduce Complications in Urology.

PubMed

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

2016-04-01

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

ERIC Educational Resources Information Center

Silm, Gerli; Tiitsaar, Kai; Pedaste, Margus; Zacharia, Zacharias C.; Papaevripidou, Marios

2017-01-01

The use of inquiry-based learning (IBL) is encouraged in schools, as it has been shown to be an effective method for raising students' motivation in STEM subjects and increasing their understanding of scientific concepts. Nevertheless, IBL is not very often used in classrooms by teachers due to different (perceived) obstacles. Within the Ark of…
Teaching Community-Based Learning Course in Retailing Management

ERIC Educational Resources Information Center

Rhee, Eddie

2018-01-01

This study outlines the use of a community-based learning (CBL) applied to a Retailing Management course conducted in a 16-week semester in a private institution in the East Coast. The study addresses the case method of teaching and its potential weaknesses, and discusses experiential learning for a real-world application. It further addresses CBL…
Problem-Based Learning--Buginese Cultural Knowledge Model--Case Study: Teaching Mathematics at Junior High School

ERIC Educational Resources Information Center

Cheriani, Cheriani; Mahmud, Alimuddin; Tahmir, Suradi; Manda, Darman; Dirawan, Gufran Darma

2015-01-01

This study aims to determine the differences in learning output by using Problem Based Model combines with the "Buginese" Local Cultural Knowledge (PBL-Culture). It is also explores the students activities in learning mathematics subject by using PBL-Culture Models. This research is using Mixed Methods approach that combined quantitative…
Team-Based Learning Reduces Attrition in a First-Semester General Chemistry Course

ERIC Educational Resources Information Center

Comeford, Lorrie

2016-01-01

Team-based learning (TBL) is an instructional method that has been shown to reduce attrition and increase student learning in a number of disciplines. TBL was implemented in a first-semester general chemistry course, and its effect on attrition was assessed. Attrition from sections before implementing TBL (fall 2008 to fall 2009) was compared with…
Reconceptualizing Pedagogical Usability of and Teachers' Roles in Computer Game-Based Learning in School

ERIC Educational Resources Information Center

Tzuo, Pei-Wen; Ling, Jennifer Isabelle Ong Pei; Yang, Chien-Hui; Chen, Vivian Hsueh-Hua

2012-01-01

At present, methods for the optimal use of two approaches to computer game-based learning in school to enhance students' learning, namely, computer game play and game design, are obscure because past research has been devoted more to designing rather than evaluating the implementation of these approaches in school. In addition, most studies…
Learning from Experience: From Case-Based Teaching to Experience-Based Learning

ERIC Educational Resources Information Center

van der Steen, Martijn; Van Twist, Mark; Frissen, Paul

2017-01-01

Senior-level civil servants can learn a lot from methods such as theory-lectures and case-teaching, but there is another resource of knowledge and insight that can be utilized more for teaching public administration: the professional experience of participants in training programmes. This paper argues that it is possible to use the professional…
Influence Based Learning Program Scientific Learning Approach to Science Students Generic Skills

ERIC Educational Resources Information Center

Wahyuni, Ida; Amdani, Khairul

2016-01-01

This study aims to determine the influence of scientific approach based learning program (P2BPS) against generic science skills of students. The method used in this research is "quasi experiment" with "two-group pretest posttest" design.The population in this study were all students who take courses in general physics II at the…
Active Learning in a Math for Liberal Arts Classroom

ERIC Educational Resources Information Center

Lenz, Laurie

2015-01-01

Inquiry-based learning is a topic of growing interest in the mathematical community. Much of the focus has been on using these methods in calculus and higher-level classes. This article describes the design and implementation of a set of inquiry-based learning activities in a Math for Liberal Arts course at a small, private, Catholic college.…
Project-Based Learning in Education: Integrating Business Needs and Student Learning

ERIC Educational Resources Information Center

Cho, Yonjoo; Brown, Catherine

2013-01-01

Purpose: The purpose of this case study was to investigate how project-based learning (PBL) is being practiced in Columbus Signature Academy (CSA), a high school located in Columbus, Indiana, USA. Design/methodology/approach: The authors used the case study method to provide qualitative details about CSA's use of PBL that is being practiced in a…
Developing and Evaluating Gamifying Learning System by Using Flow-Based Model

ERIC Educational Resources Information Center

Su, Chung-Ho; Hsaio, Kai-Chong

2015-01-01

Game-based learning is an effective learning method, whose performance depends on the quality of the educational game. Due to versatile game environments with complex backgrounds, evaluations are not easy to implement. Consequently, it is difficult for educators to determine to what degree a game may be qualified. This study proposes a novel,…
The Effects of an Experiential Service-Learning Project on Residential Interior Design Students' Attitudes toward Design and Community

ERIC Educational Resources Information Center

Gomez-Lanier, Lilia

2016-01-01

This mixed research methods study explores whether project-based service-learning projects promote greater learning than standard project-based projects and whether introduced earlier into the curriculum promotes a greater student understanding of the world issues affecting their community. The present study focused on comparing sophomore and…
Cooperative Education Guidelines for Administration: How to Comply with Federal and State Laws and Regulations

ERIC Educational Resources Information Center

Pennsylvania Department of Education, 2007

2007-01-01

Cooperative education is a method of instruction that enables students to combine academic classroom instruction (school-based learning component) with occupational instruction through learning on the job (work-based learning component) in a career area of choice. Emphasis is placed on the students' education and employability skills. Co-op is a…
Fifth Graders' Flow Experience in a Digital Game-Based Science Learning Environment

ERIC Educational Resources Information Center

Zheng, Meixun

2012-01-01

This mixed methods study examined the flow experience of 5th graders in the CRYSTAL ISLAND game-based science learning environment. Participants were 73 5th graders from a suburban public school in the southeastern US. Quantitative data about students' science content learning and attitudes towards science was collected via pre-and post surveys.…
Teaching with Game-Based Learning Management Systems: Exploring a Pedagogical Dungeon

ERIC Educational Resources Information Center

Carron, Thibault; Marty, Jean-Charles; Heraud, Jean-Mathias

2008-01-01

The work reported here takes place in the educational domain. The authors propose a learning environment based on a graphical representation of a course. The emergence of online multiplayer games led the authors to apply the following metaphor to the digital work environments: The method of acquiring knowledge during a learning session is similar…
Case-Based Instruction and Learning: An Interdisciplinary Project.

ERIC Educational Resources Information Center

Alvarez, Marino C.; And Others

Case-based learning is one method that can be used to foster critical thinking and schema construction. Students need to be provided with problem solving lessons in meaningful learning contexts for critical thinking to take place. In order for schema construction to occur, a framework needs to be provided that helps readers to elaborate upon new…
A Qualitative Study Using Project-Based Learning in a Mainstream Middle School

ERIC Educational Resources Information Center

Wurdinger, Scott; Haar, Jean; Hugg, Robert; Bezon, Jennifer

2007-01-01

Project-based learning taps into students' interests by allowing them to create projects that result in meaningful learning experiences. The method requires teachers to identify projects that challenge students to work individually or in groups to create plans, solve problems they encounter, test their ideas, and present their projects to peers.…

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