Navigating complex decision spaces: Problems and paradigms in sequential choice

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2015-01-01

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192

Computer-Mediated Assessment of Higher-Order Thinking Development

ERIC Educational Resources Information Center

Tilchin, Oleg; Raiyn, Jamal

2015-01-01

Solving complicated problems in a contemporary knowledge-based society requires higher-order thinking (HOT). The most productive way to encourage development of HOT in students is through use of the Problem-based Learning (PBL) model. This model organizes learning by solving corresponding problems relative to study courses. Students are directed…

Systematizing Scaffolding for Problem-Based Learning: A View from Case-Based Reasoning

ERIC Educational Resources Information Center

Tawfik, Andrew A.; Kolodner, Janet L.

2016-01-01

Current theories and models of education often argue that instruction is best administered when knowledge is situated within a context. Problem-based learning (PBL) provides an approach to education that has particularly powerful affordances for learning disciplinary content and practices by solving authentic problems within a discipline. However,…

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…

Conceptualization of an R&D Based Learning-to-Innovate Model for Science Education

ERIC Educational Resources Information Center

Lai, Oiki Sylvia

2013-01-01

The purpose of this research was to conceptualize an R & D based learning-to-innovate (LTI) model. The problem to be addressed was the lack of a theoretical L TI model, which would inform science pedagogy. The absorptive capacity (ACAP) lens was adopted to untangle the R & D LTI phenomenon into four learning processes: problem-solving via…

Problem based learning with scaffolding technique on geometry

NASA Astrophysics Data System (ADS)

Bayuningsih, A. S.; Usodo, B.; Subanti, S.

2018-05-01

Geometry as one of the branches of mathematics has an important role in the study of mathematics. This research aims to explore the effectiveness of Problem Based Learning (PBL) with scaffolding technique viewed from self-regulation learning toward students’ achievement learning in mathematics. The research data obtained through mathematics learning achievement test and self-regulated learning (SRL) questionnaire. This research employed quasi-experimental research. The subjects of this research are students of the junior high school in Banyumas Central Java. The result of the research showed that problem-based learning model with scaffolding technique is more effective to generate students’ mathematics learning achievement than direct learning (DL). This is because in PBL model students are more able to think actively and creatively. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

Problem-Based Learning in Web-Based Science Classroom.

ERIC Educational Resources Information Center

Kim, Heeyoung; Chung, Ji-Sook; Kim, Younghoon

The purpose of this paper is to discuss how general problem-based learning (PBL) models and social-constructivist perspectives are applied to the design and development of a Web-based science program, which emphasizes inquiry-based learning for fifth grade students. The paper also deals with the general features and learning process of a Web-based…

Feedback and Feed-Forward for Promoting Problem-Based Learning in Online Learning Environments

ERIC Educational Resources Information Center

Webb, Ashley; Moallem, Mahnaz

2016-01-01

Purpose: The study aimed to (1) review the literature to construct conceptual models that could guide instructional designers in developing problem/project-based learning environments while applying effective feedback strategies, (2) use the models to design, develop, and implement an online graduate course, and (3) assess the efficiency of the…

Video Analysis of a Plucked String: An Example of Problem-based Learning

NASA Astrophysics Data System (ADS)

Wentworth, Christopher D.; Buse, Eric

2009-11-01

Problem-based learning is a teaching methodology that grounds learning within the context of solving a real problem. Typically the problem initiates learning of concepts rather than simply being an application of the concept, and students take the lead in identifying what must be developed to solve the problem. Problem-based learning in upper-level physics courses can be challenging, because of the time and financial requirements necessary to generate real data. Here, we present a problem that motivates learning about partial differential equations and their solution in a mathematical methods for physics course. Students study a plucked elastic cord using high speed digital video. After creating video clips of the cord motion under different tensions they are asked to create a mathematical model. Ultimately, students develop and solve a model that includes damping effects that are clearly visible in the videos. The digital video files used in this project are available on the web at http://physics.doane.edu .

Development of Learning Management Model Based on Constructivist Theory and Reasoning Strategies for Enhancing the Critical Thinking of Secondary Students

ERIC Educational Resources Information Center

Chaipichit, Dudduan; Jantharajit, Nirat; Chookhampaeng, Sumalee

2015-01-01

The objectives of this research were to study issues around the management of science learning, problems that are encountered, and to develop a learning management model to address those problems. The development of that model and the findings of its study were based on Constructivist Theory and literature on reasoning strategies for enhancing…

Students’ Mathematical Problem-Solving Abilities Through The Application of Learning Models Problem Based Learning

NASA Astrophysics Data System (ADS)

Nasution, M. L.; Yerizon, Y.; Gusmiyanti, R.

2018-04-01

One of the purpose mathematic learning is to develop problem solving abilities. Problem solving is obtained through experience in questioning non-routine. Improving students’ mathematical problem-solving abilities required an appropriate strategy in learning activities one of them is models problem based learning (PBL). Thus, the purpose of this research is to determine whether the problem solving abilities of mathematical students’ who learn to use PBL better than on the ability of students’ mathematical problem solving by applying conventional learning. This research included quasi experiment with static group design and population is students class XI MIA SMAN 1 Lubuk Alung. Class experiment in the class XI MIA 5 and class control in the class XI MIA 6. The instrument of final test students’ mathematical problem solving used essay form. The result of data final test in analyzed with t-test. The result is students’ mathematical problem solving abilities with PBL better then on the ability of students’ mathematical problem solving by applying conventional learning. It’s seen from the high percentage achieved by the group of students who learn to use PBL for each indicator of students’ mathematical problem solving.

The Spiritual and Social Attitudes of Students towards Integrated Problem Based Learning Models

ERIC Educational Resources Information Center

Bachtiar, Suhaedir; Zubaidah, Siti; Corebima, Aloysius Duran; Indriwati, Sri Endah

2018-01-01

This research aimed to investigate the spiritual and social attitudes of students with different academic abilities towards four educational models: problem based learning (PBL); numbered heads together (NHT); integrated PBL and NHT; and multi-strategies model. This quasi-experimental investigation employed a pretest-posttest non-equivalent…

Effectiveness of discovery learning model on mathematical problem solving

NASA Astrophysics Data System (ADS)

Herdiana, Yunita; Wahyudin, Sispiyati, Ririn

2017-08-01

This research is aimed to describe the effectiveness of discovery learning model on mathematical problem solving. This research investigate the students' problem solving competency before and after learned by using discovery learning model. The population used in this research was student in grade VII in one of junior high school in West Bandung Regency. From nine classes, class VII B were randomly selected as the sample of experiment class, and class VII C as control class, which consist of 35 students every class. The method in this research was quasi experiment. The instrument in this research is pre-test, worksheet and post-test about problem solving of mathematics. Based on the research, it can be conclude that the qualification of problem solving competency of students who gets discovery learning model on level 80%, including in medium category and it show that discovery learning model effective to improve mathematical problem solving.

Integrating Computers into the Problem-Solving Process.

ERIC Educational Resources Information Center

Lowther, Deborah L.; Morrison, Gary R.

2003-01-01

Asserts that within the context of problem-based learning environments, professors can encourage students to use computers as problem-solving tools. The ten-step Integrating Technology for InQuiry (NteQ) model guides professors through the process of integrating computers into problem-based learning activities. (SWM)

Problem-based learning through field investigation: Boosting questioning skill, biological literacy, and academic achievement

NASA Astrophysics Data System (ADS)

Suwono, Hadi; Wibowo, Agung

2018-01-01

Biology learning emphasizes problem-based learning as a learning strategy to develop students ability in identifying and solving problems in the surrounding environment. Problem identification skills are closely correlated with questioning skills. By holding this skill, students tend to deliver a procedural question instead of the descriptive one. Problem-based learning through field investigation is an instruction model which directly exposes the students to problems or phenomena that occur in the environment, and then the students design the field investigation activities to solve these problems. The purpose of this research was to describe the improvement of undergraduate biology students on questioning skills, biological literacy, and academic achievement through problem-based learning through field investigation (PBFI) compared with the lecture-based instruction (LBI). This research was a time series quasi-experimental design. The research was conducted on August - December 2015 and involved 26 undergraduate biology students at the State University of Malang on the Freshwater Ecology course. The data were collected during the learning with LBI and PBFI, in which questioning skills, biological literacy, and academic achievement were collected 3 times in each learning model. The data showed that the procedural correlative and causal types of questions are produced by the students to guide them in conducting investigations and problem-solving in PBFI. The biological literacy and academic achievement of the students at PBFI are significantly higher than those at LBI. The results show that PBFI increases the questioning skill, biological literacy, and the academic achievement of undergraduate biology students.

Implementation of Problem Based Learning Model in Concept Learning Mushroom as a Result of Student Learning Improvement Efforts Guidelines for Teachers

ERIC Educational Resources Information Center

Rubiah, Musriadi

2016-01-01

Problem based learning is a training strategy, students work together in groups, and take responsibility for solving problems in a professional manner. Instructional materials such as textbooks become the main reference of students in study of mushrooms, especially the material is considered less effective in responding to the information needs of…

Teacher in a Problem-Based Learning Environment--Jack of All Trades?

ERIC Educational Resources Information Center

Dahms, Mona Lisa; Spliid, Claus Monrad; Nielsen, Jens Frederik Dalsgaard

2017-01-01

Problem-based learning (PBL) is one among several approaches to active learning. Being a teacher in a PBL environment can, however, be a challenge because of the need to support students' learning within a broad "landscape of learning". In this article we will analyse the landscape of learning by use of the study activity model (SAM)…

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.

Validity of Students Worksheet Based Problem-Based Learning for 9th Grade Junior High School in living organism Inheritance and Food Biotechnology.

NASA Astrophysics Data System (ADS)

Jefriadi, J.; Ahda, Y.; Sumarmin, R.

2018-04-01

Based on preliminary research of students worksheet used by teachers has several disadvantages such as students worksheet arranged directly drove learners conduct an investigation without preceded by directing learners to a problem or provide stimulation, student's worksheet not provide a concrete imageand presentation activities on the students worksheet not refer to any one learning models curicullum recommended. To address problems Reviews these students then developed a worksheet based on problem-based learning. This is a research development that using Ploom models. The phases are preliminary research, development and assessment. The instruments used in data collection that includes pieces of observation/interviews, instrument self-evaluation, instruments validity. The results of the validation expert on student worksheets get a valid result the average value 80,1%. Validity of students worksheet based problem-based learning for 9th grade junior high school in living organism inheritance and food biotechnology get valid category.

Problem Solving Model for Science Learning

NASA Astrophysics Data System (ADS)

Alberida, H.; Lufri; Festiyed; Barlian, E.

2018-04-01

This research aims to develop problem solving model for science learning in junior high school. The learning model was developed using the ADDIE model. An analysis phase includes curriculum analysis, analysis of students of SMP Kota Padang, analysis of SMP science teachers, learning analysis, as well as the literature review. The design phase includes product planning a science-learning problem-solving model, which consists of syntax, reaction principle, social system, support system, instructional impact and support. Implementation of problem-solving model in science learning to improve students' science process skills. The development stage consists of three steps: a) designing a prototype, b) performing a formative evaluation and c) a prototype revision. Implementation stage is done through a limited trial. A limited trial was conducted on 24 and 26 August 2015 in Class VII 2 SMPN 12 Padang. The evaluation phase was conducted in the form of experiments at SMPN 1 Padang, SMPN 12 Padang and SMP National Padang. Based on the development research done, the syntax model problem solving for science learning at junior high school consists of the introduction, observation, initial problems, data collection, data organization, data analysis/generalization, and communicating.

Teaching Problem-Solving and Critical-Thinking Skills Online Using Problem-Based Learning

ERIC Educational Resources Information Center

Romero, Liz; Orzechowski, Agnes; Rahatka, Ola

2014-01-01

The availability of technological tools is promoting a shift toward more student-centered online instruction. This article describes the implementation of a Problem-Based Learning (PBL) model and the technological tools used to meet the expectations of the model as well as the needs of the students. The end product is a hybrid course with eight…

Tracking student progress in a game-like physics learning environment with a Monte Carlo Bayesian knowledge tracing model

NASA Astrophysics Data System (ADS)

Gweon, Gey-Hong; Lee, Hee-Sun; Dorsey, Chad; Tinker, Robert; Finzer, William; Damelin, Daniel

2015-03-01

In tracking student learning in on-line learning systems, the Bayesian knowledge tracing (BKT) model is a popular model. However, the model has well-known problems such as the identifiability problem or the empirical degeneracy problem. Understanding of these problems remain unclear and solutions to them remain subjective. Here, we analyze the log data from an online physics learning program with our new model, a Monte Carlo BKT model. With our new approach, we are able to perform a completely unbiased analysis, which can then be used for classifying student learning patterns and performances. Furthermore, a theoretical analysis of the BKT model and our computational work shed new light on the nature of the aforementioned problems. This material is based upon work supported by the National Science Foundation under Grant REC-1147621 and REC-1435470.

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.

Effects of Online Problem-Based Learning on Teachers' Technology Perceptions and Planning

ERIC Educational Resources Information Center

Nelson, Erik T.

2007-01-01

The purpose of this qualitative study was to examine the ways in which the experience of learning through an online problem-based learning (PBL) model affect teachers' perceptions of integrating technology. Participant reflections were collected and analyzed to identify the pros, cons, and challenges of learning technology integration through this…

Problem-Based Learning: Modifying the Medical School Model for Teaching High School Economics.

ERIC Educational Resources Information Center

Maxwell, Nan L.; Bellisimo, Yolanda; Mergendoller, John

2001-01-01

Provides background information on the problem-based learning (PBL) model used in medical education that was adapted for high school economics. Describes the high school economics curriculum and outline the stages of the PBL model using examples from a unit called "The High School Food Court." Discusses the design considerations. (CMK)

Integrated and Contextual Basic Science Instruction in Preclinical Education: Problem-Based Learning Experience Enriched with Brain/Mind Learning Principles

ERIC Educational Resources Information Center

Gülpinar, Mehmet Ali; Isoglu-Alkaç, Ümmühan; Yegen, Berrak Çaglayan

2015-01-01

Recently, integrated and contextual learning models such as problem-based learning (PBL) and brain/mind learning (BML) have become prominent. The present study aimed to develop and evaluate a PBL program enriched with BML principles. In this study, participants were 295 first-year medical students. The study used both quantitative and qualitative…

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students.

PubMed

Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah

2015-01-01

Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Working Towards a Scalable Model of Problem-Based Learning Instruction in Undergraduate Engineering Education

ERIC Educational Resources Information Center

Mantri, Archana

2014-01-01

The intent of the study presented in this paper is to show that the model of problem-based learning (PBL) can be made scalable by designing curriculum around a set of open-ended problems (OEPs). The detailed statistical analysis of the data collected to measure the effects of traditional and PBL instructions for three courses in Electronics and…

Motivational Influences of Using Peer Evaluation in Problem-Based Learning in Medical Education

ERIC Educational Resources Information Center

Abercrombie, Sara; Parkes, Jay; McCarty, Teresita

2015-01-01

This study investigates the ways in which medical students' achievement goal orientations (AGO) affect their perceptions of learning and actual learning from an online problem-based learning environment, Calibrated Peer Review™. First, the tenability of a four-factor model (Elliot & McGregor, 2001) of AGO was tested with data collected from…

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…

The Development of Learning Model Based on Problem Solving to Construct High-Order Thinking Skill on the Learning Mathematics of 11th Grade in SMA/MA

ERIC Educational Resources Information Center

Syahputra, Edi; Surya, Edy

2017-01-01

This paper is a summary study of team Postgraduate on 11th grade. The objective of this study is to develop a learning model based on problem solving which can construct high-order thinking on the learning mathematics in SMA/MA. The subject of dissemination consists of Students of 11th grade in SMA/MA in 3 kabupaten/kota in North Sumatera, namely:…

Group problems in problem-based learning.

PubMed

Hendry, Graham D; Ryan, Greg; Harris, Jennifer

2003-11-01

Successful small-group learning in problem-based learning (PBL) educational programmes relies on functional group processes. However, there has been limited research on PBL group problems, and no studies have been conducted on problems as perceived by both students and tutors in the same educational context. The authors investigated PBL group problems in a graduate-entry medical programme, and report the most common group problems, and those that hinder students' learning the most. The possible causes of individual quietness and dominant behaviour, and potential influences that group problems may have on the tutorial process are summarized in an exploratory model of PBL group dysfunction that could be used to guide further research. Specifically, there is a need for further evidence on which to base guidelines for tutors and students to effectively manage group problems.

New Ideas on the Design of the Web-Based Learning System Oriented to Problem Solving from the Perspective of Question Chain and Learning Community

ERIC Educational Resources Information Center

Zhang, Yin; Chu, Samuel K. W.

2016-01-01

In recent years, a number of models concerning problem solving systems have been put forward. However, many of them stress on technology and neglect the research of problem solving itself, especially the learning mechanism related to problem solving. In this paper, we analyze the learning mechanism of problem solving, and propose that when…

Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

NASA Astrophysics Data System (ADS)

Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

2017-08-01

Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7

Development of syntax of intuition-based learning model in solving mathematics problems

NASA Astrophysics Data System (ADS)

Yeni Heryaningsih, Nok; Khusna, Hikmatul

2018-01-01

The aim of the research was to produce syntax of Intuition Based Learning (IBL) model in solving mathematics problem for improving mathematics students’ achievement that valid, practical and effective. The subject of the research were 2 classes in grade XI students of SMAN 2 Sragen, Central Java. The type of the research was a Research and Development (R&D). Development process adopted Plomp and Borg & Gall development model, they were preliminary investigation step, design step, realization step, evaluation and revision step. Development steps were as follow: (1) Collected the information and studied of theories in Preliminary Investigation step, studied about intuition, learning model development, students condition, and topic analysis, (2) Designed syntax that could bring up intuition in solving mathematics problem and then designed research instruments. They were several phases that could bring up intuition, Preparation phase, Incubation phase, Illumination phase and Verification phase, (3) Realized syntax of Intuition Based Learning model that has been designed to be the first draft, (4) Did validation of the first draft to the validator, (5) Tested the syntax of Intuition Based Learning model in the classrooms to know the effectiveness of the syntax, (6) Conducted Focus Group Discussion (FGD) to evaluate the result of syntax model testing in the classrooms, and then did the revision on syntax IBL model. The results of the research were produced syntax of IBL model in solving mathematics problems that valid, practical and effective. The syntax of IBL model in the classroom were, (1) Opening with apperception, motivations and build students’ positive perceptions, (2) Teacher explains the material generally, (3) Group discussion about the material, (4) Teacher gives students mathematics problems, (5) Doing exercises individually to solve mathematics problems with steps that could bring up students’ intuition: Preparations, Incubation, Illumination, and Verification, (6) Closure with the review of students have learned or giving homework.

Problem Based Learning and the scientific process

NASA Astrophysics Data System (ADS)

Schuchardt, Daniel Shaner

This research project was developed to inspire students to constructively use problem based learning and the scientific process to learn middle school science content. The student population in this study consisted of male and female seventh grade students. Students were presented with authentic problems that are connected to physical and chemical properties of matter. The intent of the study was to have students use the scientific process of looking at existing knowledge, generating learning issues or questions about the problems, and then developing a course of action to research and design experiments to model resolutions to the authentic problems. It was expected that students would improve their ability to actively engage with others in a problem solving process to achieve a deeper understanding of Michigan's 7th Grade Level Content Expectations, the Next Generation Science Standards, and a scientific process. Problem based learning was statistically effective in students' learning of the scientific process. Students statistically showed improvement on pre to posttest scores. The teaching method of Problem Based Learning was effective for seventh grade science students at Dowagiac Middle School.

How Levels of Interactivity in Tutorials Affect Students' Learning of Modeling Transportation Problems in a Spreadsheet

ERIC Educational Resources Information Center

Seal, Kala Chand; Przasnyski, Zbigniew H.; Leon, Linda A.

2010-01-01

Do students learn to model OR/MS problems better by using computer-based interactive tutorials and, if so, does increased interactivity in the tutorials lead to better learning? In order to determine the effect of different levels of interactivity on student learning, we used screen capture technology to design interactive support materials for…

Case Problems for Problem-Based Pedagogical Approaches: A Comparative Analysis

ERIC Educational Resources Information Center

Dabbagh, Nada; Dass, Susan

2013-01-01

A comparative analysis of 51 case problems used in five problem-based pedagogical models was conducted to examine whether there are differences in their characteristics and the implications of such differences on the selection and generation of ill-structured case problems. The five pedagogical models were: situated learning, goal-based scenario,…

A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

NASA Astrophysics Data System (ADS)

Pata, Kai; Sarapuu, Tago

2006-09-01

This study investigated the possible activation of different types of model-based reasoning processes in two learning settings, and the influence of various terms of reasoning on the learners’ problem representation development. Changes in 53 students’ problem representations about genetic issue were analysed while they worked with different modelling tools in a synchronous network-based environment. The discussion log-files were used for the “microgenetic” analysis of reasoning types. For studying the stages of students’ problem representation development, individual pre-essays and post-essays and their utterances during two reasoning phases were used. An approach for mapping problem representations was developed. Characterizing the elements of mental models and their reasoning level enabled the description of five hierarchical categories of problem representations. Learning in exploratory and experimental settings was registered as the shift towards more complex stages of problem representations in genetics. The effect of different types of reasoning could be observed as the divergent development of problem representations within hierarchical categories.

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Effects of Simulation With Problem-Based Learning Program on Metacognition, Team Efficacy, and Learning Attitude in Nursing Students: Nursing Care With Increased Intracranial Pressure Patient.

PubMed

Lee, Myung-Nam; Nam, Kyung-Dong; Kim, Hyeon-Young

2017-03-01

Nursing care for patients with central nervous system problems requires advanced professional knowledge and care skills. Nursing students are more likely to have difficulty in dealing with adult patients who have severe neurological problems in clinical practice. This study investigated the effect on the metacognition, team efficacy, and learning attitude of nursing students after an integrated simulation and problem-based learning program. A real scenario of a patient with increased intracranial pressure was simulated for the students. The results showed that this method was effective in improving the metacognitive ability of the students. Furthermore, we used this comprehensive model of simulation with problem-based learning in order to assess the consequences of student satisfaction with the nursing major, interpersonal relationships, and importance of simulation-based education in relation to the effectiveness of the integrated simulation with problem-based learning. The results can be used to improve the design of clinical practicum and nursing education.

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…

The effectiveness of collaborative problem based physics learning (CPBPL) model to improve student’s self-confidence on physics learning

NASA Astrophysics Data System (ADS)

Prahani, B. K.; Suprapto, N.; Suliyanah; Lestari, N. A.; Jauhariyah, M. N. R.; Admoko, S.; Wahyuni, S.

2018-03-01

In the previous research, Collaborative Problem Based Physic Learning (CPBPL) model has been developed to improve student’s science process skills, collaborative problem solving, and self-confidence on physics learning. This research is aimed to analyze the effectiveness of CPBPL model towards the improvement of student’s self-confidence on physics learning. This research implemented quasi experimental design on 140 senior high school students who were divided into 4 groups. Data collection was conducted through questionnaire, observation, and interview. Self-confidence measurement was conducted through Self-Confidence Evaluation Sheet (SCES). The data was analyzed using Wilcoxon test, n-gain, and Kruskal Wallis test. Result shows that: (1) There is a significant score improvement on student’s self-confidence on physics learning (α=5%), (2) n-gain value student’s self-confidence on physics learning is high, and (3) n-gain average student’s self-confidence on physics learning was consistent throughout all groups. It can be concluded that CPBPL model is effective to improve student’s self-confidence on physics learning.

The effectiveness of clinical problem-based learning model of medico-jurisprudence education on general law knowledge for Obstetrics/Gynecological interns.

PubMed

Chang, Hui-Chin; Wang, Ning-Yen; Ko, Wen-Ru; Yu, You-Tsz; Lin, Long-Yau; Tsai, Hui-Fang

2017-06-01

The effective education method of medico-jurisprudence for medical students is unclear. The study was designed to evaluate the effectiveness of problem-based learning (PBL) model teaching medico-jurisprudence in clinical setting on General Law Knowledge (GLK) for medical students. Senior medical students attending either campus-based law curriculum or Obstetrics/Gynecology (Ob/Gyn) clinical setting morning meeting from February to July in 2015 were enrolled. A validated questionnaire comprising 45 questions were completed before and after the law education. The interns attending clinical setting small group improvisation medico-jurisprudence problem-based learning education had significantly better GLK scores than the GLK of students attending campus-based medical law education course after the period studied. PBL teaching model of medico-jurisprudence is an ideal alternative pedagogy model in medical law education curriculum. Copyright © 2017. Published by Elsevier B.V.

Scaffolding Teachers Integrate Social Media into a Problem-Based Learning Approach?

ERIC Educational Resources Information Center

Buus, Lillian

2012-01-01

At Aalborg University (AAU) we are known to work with problem-based learning (PBL) in a particular way designated "The Aalborg PBL model." In PBL the focus is on participant control, knowledge sharing, collaboration among participants, which makes it interesting to consider the integration of social media in the learning that takes…

Measuring Teachers' Learning from a Problem-Based Learning Approach to Professional Development in Science Education

ERIC Educational Resources Information Center

Weizman, Ayelet; Covitt, Beth A.; Koehler, Matthew J.; Lundeberg, Mary A.; Oslund, Joy A.; Low, Mark R.; Eberhardt, Janet; Urban-Lurain, Mark

2008-01-01

In this study we measured changes in science teachers' conceptual science understanding (content knowledge) and pedagogical content knowledge (PCK) while participating in a problem-based learning (PBL) model of professional development. Teachers participated in a two-week long workshop followed by nine monthly meetings during one academic year…

Zoology Students' Experiences of Collaborative Enquiry in Problem-Based Learning

ERIC Educational Resources Information Center

Harland, Tony

2002-01-01

This paper presents an action-research case study that focuses on experiences of collaboration in a problem-based learning (PBL) course in Zoology. Our PBL model was developed as a research activity in partnership with a commercial organisation. Consequently, learning was grounded in genuine situations of practice in which a high degree of…

PBL-SEE: An Authentic Assessment Model for PBL-Based Software Engineering Education

ERIC Educational Resources Information Center

dos Santos, Simone C.

2017-01-01

The problem-based learning (PBL) approach has been successfully applied to teaching software engineering thanks to its principles of group work, learning by solving real problems, and learning environments that match the market realities. However, the lack of well-defined methodologies and processes for implementing the PBL approach represents a…

Deep Learning towards Expertise Development in a Visualization-Based Learning Environment

ERIC Educational Resources Information Center

Yuan, Bei; Wang, Minhong; Kushniruk, Andre W.; Peng, Jun

2017-01-01

With limited problem-solving capability and practical experience, novices have difficulties developing expert-like performance. It is important to make the complex problem-solving process visible to learners and provide them with necessary help throughout the process. This study explores the design and effects of a model-based learning approach…

Problem-based learning: effects on student’s scientific reasoning skills in science

NASA Astrophysics Data System (ADS)

Wulandari, F. E.; Shofiyah, N.

2018-04-01

This research aimed to develop instructional package of problem-based learning to enhance student’s scientific reasoning from concrete to formal reasoning skills level. The instructional package was developed using the Dick and Carey Model. Subject of this study was instructional package of problem-based learning which was consisting of lesson plan, handout, student’s worksheet, and scientific reasoning test. The instructional package was tried out on 4th semester science education students of Universitas Muhammadiyah Sidoarjo by using the one-group pre-test post-test design. The data of scientific reasoning skills was collected by making use of the test. The findings showed that the developed instructional package reflecting problem-based learning was feasible to be implemented in classroom. Furthermore, through applying the problem-based learning, students could dominate formal scientific reasoning skills in terms of functionality and proportional reasoning, control variables, and theoretical reasoning.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students

PubMed Central

Saadati, Farzaneh; Ahmad Tarmizi, Rohani

2015-01-01

Because students’ ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is ‘value added’ because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students’ problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students. PMID:26132553

Polarimetric SAR image classification based on discriminative dictionary learning model

NASA Astrophysics Data System (ADS)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Using the Constructivist Tridimensional Design Model for Online Continuing Education for Health Care Clinical Faculty

ERIC Educational Resources Information Center

Seo, Kay Kyeong-Ju; Engelhard, Chalee

2014-01-01

This article presents a new paradigm for continuing education of Clinical Instructors (CIs): the Constructivist Tridimensional (CTD) model for the design of an online curriculum. Based on problem-based learning, self-regulated learning, and adult learning theory, the CTD model was designed to facilitate interactive, collaborative, and authentic…

Integrating Blended and Problem-Based Learning into an Architectural Housing Design Studio: A Case Study

ERIC Educational Resources Information Center

Bregger, Yasemin Alkiser

2017-01-01

This paper presents how a blended learning pedagogic model is integrated into an architectural design studio by adapting the problem-based learning process and housing issues in Istanbul Technical University (ITU), during fall 2015 and spring 2016 semesters for fourth and sixth level students. These studios collaborated with the "Introduction…

The Views of Preservice Teachers for Problem Based Learning Model Supported by Geocaching in Environmental Education

ERIC Educational Resources Information Center

Adanali, Rukiye; Alim, Mete

2017-01-01

The purpose of this study is to investigate the usability of Problem-Based Learning model supported by Instructional Geocaching Game (PBL-IGG). The study was conducted in Turkey, in 2015-2016 spring term with 19 geography teacher candidates who chosen by convenience sampling method. In this study, within Educational Geocaching Game (IGG) which is…

Summer Teacher Enhancement Institute for Science, Mathematics, and Technology Using the Problem-Based Learning Model

NASA Technical Reports Server (NTRS)

Petersen, Richard H.

1997-01-01

The objectives of the Institute were: (a) increase participants' content knowledge about aeronautics, science, mathematics, and technology, (b) model and promote the use of scientific inquiry through problem-based learning, (c) investigate the use of instructional technologies and their applications to curricula, and (d) encourage the dissemination of TEI experiences to colleagues, students, and parents.

The Effectiveness of Local Culture-Based Mathematical Heuristic-KR Learning towards Enhancing Student's Creative Thinking Skill

ERIC Educational Resources Information Center

Tandiseru, Selvi Rajuaty

2015-01-01

The problem in this research is the lack of creative thinking skills of students. One of the learning models that is expected to enhance student's creative thinking skill is the local culture-based mathematical heuristic-KR learning model (LC-BMHLM). Heuristic-KR is a learning model which was introduced by Krulik and Rudnick (1995) that is the…

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

PubMed

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

2015-02-01

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

PubMed Central

Konovalov, Arkady; Krajbich, Ian

2016-01-01

Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time. PMID:27511383

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.…

Effects of a Problem-based Structure of Physics Contents on Conceptual Learning and the Ability to Solve Problems

NASA Astrophysics Data System (ADS)

Becerra-Labra, Carlos; Gras-Martí, Albert; Martínez Torregrosa, Joaquín

2012-05-01

A model of teaching/learning is proposed based on a 'problem-based structure' of the contents of the course, in combination with a training in paper and pencil problem solving that emphasizes discussion and quantitative analysis, rather than formulae plug-in. The aim is to reverse the high failure and attrition rate among engineering undergraduates taking physics. A number of tests and questionnaires were administered to a group of students following a traditional lecture-based instruction, as well as to another group that was following an instruction scheme based on the proposed approach and the teaching materials developed ad hoc. The results show that students following the new method can develop scientific reasoning habits in problem-solving skills, and show gains in conceptual learning, attitudes and interests, and that the effects of this approach on learning are noticeable several months after the course is over.

Developing learning material of introduction to operation research course based on problem-based learning

NASA Astrophysics Data System (ADS)

Yerizon; Jazwinarti; Yarman

2018-01-01

Students have difficulties experience in the course Introduction to Operational Research (PRO). The purpose of this study is to analyze the requirement of students in the developing lecturing materials PRO based Problem Based Learning which is valid, practice, and effective. Lecture materials are developed based on Plomp’s model. The development process of this device consists of 3 phases: front-end analysis/preliminary research, development/prototype phase and assessment phase. Preliminary analysis was obtained by observation and interview. From the research, it is found that students need the student’s worksheet (LKM) for several reasons: 1) no LKM available, 2) presentation of subject not yet based on real problem, 3) experiencing difficulties from current learning source.

Teaching genetics using hands-on models, problem solving, and inquiry-based methods

NASA Astrophysics Data System (ADS)

Hoppe, Stephanie Ann

Teaching genetics can be challenging because of the difficulty of the content and misconceptions students might hold. This thesis focused on using hands-on model activities, problem solving, and inquiry-based teaching/learning methods in order to increase student understanding in an introductory biology class in the area of genetics. Various activities using these three methods were implemented into the classes to address any misconceptions and increase student learning of the difficult concepts. The activities that were implemented were shown to be successful based on pre-post assessment score comparison. The students were assessed on the subjects of inheritance patterns, meiosis, and protein synthesis and demonstrated growth in all of the areas. It was found that hands-on models, problem solving, and inquiry-based activities were more successful in learning concepts in genetics and the students were more engaged than tradition styles of lecture.

A Systematic Review of Research on the Use of Problem-Based Learning in the Preparation and Development of School Leaders

ERIC Educational Resources Information Center

Hallinger, Philip; Bridges, Edwin M.

2017-01-01

Problem: Problem-based learning (PBL) was introduced into the parlance of educational leadership and management almost 30 years ago. During the ensuing decades, a global community of professors, doctoral students, and curriculum designers has built upon early models with the goal of increasing the impact of school leadership preparation. This…

Influence of Problem Based Learning on Critical Thinking Skills and Competence Class VIII SMPN 1 Gunuang Omeh, 2016/2017

NASA Astrophysics Data System (ADS)

Aswan, D. M.; Lufri, L.; Sumarmin, R.

2018-04-01

This research intends to determine the effect of Problem Based Learning models on students' critical thinking skills and competences. This study was a quasi-experimental research. The population of the study was the students of class VIII SMPN 1 Subdistrict Gunuang Omeh. Random sample selection is done by randomizing the class. Sample class that was chosen VIII3 as an experimental class given that treatment study based on problems and class VIII1 as control class that treatment usually given study. Instrument that used to consist of critical thinking test, cognitive tests, observation sheet of affective and psychomotor. Independent t-test and Mann Whitney U test was used for the analysis. Results showed that there was significant difference (sig <0.05) between control and experimental group. The conclusion of this study was Problem Based Learning models affected the students’ critical thinking skills and competences.

An investigation of meaningful understanding and effectiveness of the implementation of Piagetian and Ausubelian theories in physics instruction

NASA Astrophysics Data System (ADS)

Williams, Karen Ann

One section of college students (N = 25) enrolled in an algebra-based physics course was selected for a Piagetian-based learning cycle (LC) treatment while a second section (N = 25) studied in an Ausubelian-based meaningful verbal reception learning treatment (MVRL). This study examined the students' overall (concept + problem solving + mental model) meaningful understanding of force, density/Archimedes Principle, and heat. Also examined were students' meaningful understanding as measured by conceptual questions, problems, and mental models. In addition, students' learning orientations were examined. There were no significant posttest differences between the LC and MVRL groups for students' meaningful understanding or learning orientation. Piagetian and Ausubelian theories explain meaningful understanding for each treatment. Students from each treatment increased their meaningful understanding. However, neither group altered their learning orientation. The results of meaningful understanding as measured by conceptual questions, problem solving, and mental models were mixed. Differences were attributed to the weaknesses and strengths of each treatment. This research also examined four variables (treatment, reasoning ability, learning orientation, and prior knowledge) to find which best predicted students' overall meaningful understanding of physics concepts. None of these variables were significant predictors at the.05 level. However, when the same variables were used to predict students' specific understanding (i.e. concept, problem solving, or mental model understanding), the results were mixed. For forces and density/Archimedes Principle, prior knowledge and reasoning ability significantly predicted students' conceptual understanding. For heat, however, reasoning ability was the only significant predictor of concept understanding. Reasoning ability and treatment were significant predictors of students' problem solving for heat and forces. For density/Archimedes Principle, treatment was the only significant predictor of students' problem solving. None of the variables were significant predictors of mental model understanding. This research suggested that Piaget and Ausubel used different terminology to describe learning yet these theories are similar. Further research is needed to validate this premise and validate the blending of the two theories.

A Multidirectional Model for Assessing Learning Disabled Students' Intelligence: An Information-Processing Framework.

ERIC Educational Resources Information Center

Swanson, H. Lee

1982-01-01

An information processing approach to the assessment of learning disabled students' intellectual performance is presented. The model is based on the assumption that intelligent behavior is comprised of a variety of problem- solving strategies. An account of child problem solving is explained and illustrated with a "thinking aloud" protocol.…

Assessment of Adaptive PBL's Impact on HOT Development of Computer Science Students

ERIC Educational Resources Information Center

Raiyn, Jamal; Tilchin, Oleg

2015-01-01

Meaningful learning based on PBL is new learning strategy. Compared to traditional learning strategy, the meaningful learning strategy put the student in center of the learning process. The roles of the student in the meaningful learning strategy will be increased. The Problem-based Learning (PBL) model is considered the most productive way to…

Science teacher's perception about science learning experiences as a foundation for teacher training program

NASA Astrophysics Data System (ADS)

Tapilouw, Marisa Christina; Firman, Harry; Redjeki, Sri; Chandra, Didi Teguh

2017-05-01

Teacher training is one form of continuous professional development. Before organizing teacher training (material, time frame), a survey about teacher's need has to be done. Science teacher's perception about science learning in the classroom, the most difficult learning model, difficulties of lesson plan would be a good input for teacher training program. This survey conducted in June 2016. About 23 science teacher filled in the questionnaire. The core of questions are training participation, the most difficult science subject matter, the most difficult learning model, the difficulties of making lesson plan, knowledge of integrated science and problem based learning. Mostly, experienced teacher participated training once a year. Science training is very important to enhance professional competency and to improve the way of teaching. The difficulties of subject matter depend on teacher's education background. The physics subject matter in class VIII and IX are difficult to teach for most respondent because of many formulas and abstract. Respondents found difficulties in making lesson plan, in term of choosing the right learning model for some subject matter. Based on the result, inquiry, cooperative, practice are frequently used in science class. Integrated science is understood as a mix between Biology, Physics and Chemistry concepts. On the other hand, respondents argue that problem based learning was difficult especially in finding contextual problem. All the questionnaire result can be used as an input for teacher training program in order to enhanced teacher's competency. Difficult concepts, integrated science, teaching plan, problem based learning can be shared in teacher training.

The effectiveness of problem-based learning on students’ problem solving ability in vector analysis course

NASA Astrophysics Data System (ADS)

Mushlihuddin, R.; Nurafifah; Irvan

2018-01-01

The student’s low ability in mathematics problem solving proved to the less effective of a learning process in the classroom. Effective learning was a learning that affects student’s math skills, one of which is problem-solving abilities. Problem-solving capability consisted of several stages: understanding the problem, planning the settlement, solving the problem as planned, re-examining the procedure and the outcome. The purpose of this research was to know: (1) was there any influence of PBL model in improving ability Problem solving of student math in a subject of vector analysis?; (2) was the PBL model effective in improving students’ mathematical problem-solving skills in vector analysis courses? This research was a quasi-experiment research. The data analysis techniques performed from the test stages of data description, a prerequisite test is the normality test, and hypothesis test using the ANCOVA test and Gain test. The results showed that: (1) there was an influence of PBL model in improving students’ math problem-solving abilities in vector analysis courses; (2) the PBL model was effective in improving students’ problem-solving skills in vector analysis courses with a medium category.

Adopting Problem-Based Learning Model for AN Electrical Engineering Curriculum

NASA Astrophysics Data System (ADS)

Khan, Mohamed Khan Aftab Ahmed; Sinnadurai, Rajendran; Amudha, M.; Elamvazuthi, I.; Vasant, P.

2010-06-01

The shortage of highly qualified academicians in a knowledge-based economy and potential benefits of Problem-Based Learning (PBL) approach has necessitated the adoption of PBL in many areas of education. This paper discusses a PBL experience for an electrical engineering undergraduate course. Some preliminary experiences of implementing them are described and discussed. It was found that PBL approach seem to be an efficient strategy not only for undergraduate engineering education but also for instilling lifelong learning.

Development of Learning Devices through Problem Based Learning Model Based on the Context of Aceh Cultural to Improve Mathematical Communication Skills and Social Skills of SMPN 1 Muara Batu Students

ERIC Educational Resources Information Center

Aufa, Mahrani; Saragih, Sahat; Minarni, Ani

2016-01-01

The purposes of this study were:1) Developed problem-based on learning tools in the cultural context of Aceh (PBM-BKBA) who meet the criteria are valid, practical and effective; 2) Described the improvement of communication capabilities mathematics and social skills of students using the PBM-BKBA developed; and 3) Described the process of student…

Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Example of Competence-Based Learning: Use of Maxima in Linear Algebra for Engineers

ERIC Educational Resources Information Center

Diaz, Ana; Garcia, Alfonsa; de la Villa, Agustin

2011-01-01

This paper analyses the role of Computer Algebra Systems (CAS) in a model of learning based on competences. The proposal is an e-learning model Linear Algebra course for Engineering, which includes the use of a CAS (Maxima) and focuses on problem solving. A reference model has been taken from the Spanish Open University. The proper use of CAS is…

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

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

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

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

Teaching Psychosomatic Medicine Using Problem-Based Learning and Role-Playing

ERIC Educational Resources Information Center

Heru, Alison M.

2011-01-01

Objective: Problem-based learning (PBL) has been implemented in medical education world-wide. Despite its popularity, it has not been generally considered useful for residency programs. The author presents a model for the implementation of PBL in residency programs. Method: The author presents a description of a PBL curriculum for teaching…

From primary care to public health: using Problem-based Learning and the ecological model to teach public health to first year medical students.

PubMed

Hoover, Cora R; Wong, Candice C; Azzam, Amin

2012-06-01

We investigated whether a public health-oriented Problem-Based Learning case presented to first-year medical students conveyed 12 "Population Health Competencies for Medical Students," as recommended by the Association of American Medical Colleges and the Regional Medicine-Public Health Education Centers. A public health-oriented Problem-Based Learning case guided by the ecological model paradigm was developed and implemented among two groups of 8 students at the University of California, Berkeley-UCSF Joint Medical Program, in the Fall of 2010. Using directed content analysis, student-generated written reports were coded for the presence of the 12 population health content areas. Students generated a total of 29 reports, of which 20 (69%) contained information relevant to at least one of the 12 population health competencies. Each of the 12 content areas was addressed by at least one report. As physicians-in-training prepare to confront the challenges of integrating prevention and population health with clinical practice, Problem-Based Learning is a promising tool to enhance medical students' engagement with public health.

Knowledge acquisition and learning process description in context of e-learning

NASA Astrophysics Data System (ADS)

Kiselev, B. G.; Yakutenko, V. A.; Yuriev, M. A.

2017-01-01

This paper investigates the problem of design of e-learning and MOOC systems. It describes instructional design-based approaches to e-learning systems design: IMS Learning Design, MISA and TELOS. To solve this problem we present Knowledge Field of Educational Environment with Competence boundary conditions - instructional engineering method for self-learning systems design. It is based on the simplified TELOS approach and enables a user to create their individual learning path by choosing prerequisite and target competencies. The paper provides the ontology model for the described instructional engineering method, real life use cases and the classification of the presented model. Ontology model consists of 13 classes and 15 properties. Some of them are inherited from Knowledge Field of Educational Environment and some are new and describe competence boundary conditions and knowledge validation objects. Ontology model uses logical constraints and is described using OWL 2 standard. To give TELOS users better understanding of our approach we list mapping between TELOS and KFEEC.

Assessing the Effectiveness of STAD Model and Problem Based Learning in Mathematics Learning Achievement and Problem Solving Ability

ERIC Educational Resources Information Center

Rattanatumma, Tawachai; Puncreobutr, Vichian

2016-01-01

The objective of this study was to compare the effectiveness of teaching methods in improving Mathematics Learning Achievement and Problem solving ability of students at an international college. This is a Quasi-Experimental Research which was done the study with the first year students who have registered to study Mathematics subject at St.…

Learning in 3-D Multiuser Virtual Environments: Exploring the Use of Unique 3-D Attributes for Online Problem-Based Learning

ERIC Educational Resources Information Center

Omale, Nicholas; Hung, Wei-Chen; Luetkehans, Lara; Cooke-Plagwitz, Jessamine

2009-01-01

The purpose of this article is to present the results of a study conducted to investigate how the attributes of 3-D technology such as avatars, 3-D space, and comic style bubble dialogue boxes affect participants' social, cognitive, and teaching presences in a blended problem-based learning environment. The community of inquiry model was adopted…

[Problem based learning (PBL)--possible adaptation in psychiatry (debate)].

PubMed

Adamowski, Tomasz; Frydecka, Dorota; Kiejna, Andrzej

2007-01-01

Teaching psychiatry concerns mainly education of students studying medicine and clinical psychology, but it also concerns professional training the people specializing in psychiatry and in other fields of medicine. Since the requirements that medical professionals are obliged to meet are ever higher, it is essential to provide highest possible quality of teaching and to do so to use the best possible teaching models. One of the modern educational models is Problem Based Learning (PBL). Barrows' and Dreyfus' research as well as development of andragogy had major impact on the introduction of this model of teaching. There are favourable experiences of using PBL in teaching psychiatry reported, especially in the field of psychosomatics. Problem Based Learning gradually becomes a part of modern curricula in Western Europe. For this reason it is worth keeping in mind PBL's principles and knowingly apply them into practice, all the more the reported educational effects of using this method are very promising.

The Effectiveness of Project Based Learning in Trigonometry

NASA Astrophysics Data System (ADS)

Gerhana, M. T. C.; Mardiyana, M.; Pramudya, I.

2017-09-01

This research aimed to explore the effectiveness of Project-Based Learning (PjBL) with scientific approach viewed from interpersonal intelligence toward students’ achievement learning in mathematics. This research employed quasi experimental research. The subjects of this research were grade X MIPA students in Sleman Yogyakarta. The result of the research showed that project-based learning model is more effective to generate students’ mathematics learning achievement that classical model with scientific approach. This is because in PjBL model students are more able to think actively and creatively. Students are faced with a pleasant atmosphere to solve a problem in everyday life. The use of project-based learning model is expected to be the choice of teachers to improve mathematics education.

Reverse engineering a social agent-based hidden markov model--visage.

PubMed

Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A

2008-12-01

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

Analysis of creative mathematic thinking ability in problem based learning model based on self-regulation learning

NASA Astrophysics Data System (ADS)

Munahefi, D. N.; Waluya, S. B.; Rochmad

2018-03-01

The purpose of this research identified the effectiveness of Problem Based Learning (PBL) models based on Self Regulation Leaning (SRL) on the ability of mathematical creative thinking and analyzed the ability of mathematical creative thinking of high school students in solving mathematical problems. The population of this study was students of grade X SMA N 3 Klaten. The research method used in this research was sequential explanatory. Quantitative stages with simple random sampling technique, where two classes were selected randomly as experimental class was taught with the PBL model based on SRL and control class was taught with expository model. The selection of samples at the qualitative stage was non-probability sampling technique in which each selected 3 students were high, medium, and low academic levels. PBL model with SRL approach effectived to students’ mathematical creative thinking ability. The ability of mathematical creative thinking of low academic level students with PBL model approach of SRL were achieving the aspect of fluency and flexibility. Students of academic level were achieving fluency and flexibility aspects well. But the originality of students at the academic level was not yet well structured. Students of high academic level could reach the aspect of originality.

Promoting students’ mathematical problem-solving skills through 7e learning cycle and hypnoteaching model

NASA Astrophysics Data System (ADS)

Saleh, H.; Suryadi, D.; Dahlan, J. A.

2018-01-01

The aim of this research was to find out whether 7E learning cycle under hypnoteaching model can enhance students’ mathematical problem-solving skill. This research was quasi-experimental study. The design of this study was pretest-posttest control group design. There were two groups of sample used in the study. The experimental group was given 7E learning cycle under hypnoteaching model, while the control group was given conventional model. The population of this study was the student of mathematics education program at one university in Tangerang. The statistical analysis used to test the hypothesis of this study were t-test and Mann-Whitney U. The result of this study show that: (1) The students’ achievement of mathematical problem solving skill who obtained 7E learning cycle under hypnoteaching model are higher than the students who obtained conventional model; (2) There are differences in the students’ enhancement of mathematical problem-solving skill based on students’ prior mathematical knowledge (PMK) category (high, middle, and low).

Instance-Based Ontology Matching for Open and Distance Learning Materials

ERIC Educational Resources Information Center

Cerón-Figueroa, Sergio; López-Yáñez, Itzamá; Villuendas-Rey, Yenny; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Yáñez-Márquez, Cornelio

2017-01-01

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is…

The Ontologies of Complexity and Learning about Complex Systems

ERIC Educational Resources Information Center

Jacobson, Michael J.; Kapur, Manu; So, Hyo-Jeong; Lee, June

2011-01-01

This paper discusses a study of students learning core conceptual perspectives from recent scientific research on complexity using a hypermedia learning environment in which different types of scaffolding were provided. Three comparison groups used a hypermedia system with agent-based models and scaffolds for problem-based learning activities that…

The Experimental Research on E-Learning Instructional Design Model Based on Cognitive Flexibility Theory

NASA Astrophysics Data System (ADS)

Cao, Xianzhong; Wang, Feng; Zheng, Zhongmei

The paper reports an educational experiment on the e-Learning instructional design model based on Cognitive Flexibility Theory, the experiment were made to explore the feasibility and effectiveness of the model in promoting the learning quality in ill-structured domain. The study performed the experiment on two groups of students: one group learned through the system designed by the model and the other learned by the traditional method. The results of the experiment indicate that the e-Learning designed through the model is helpful to promote the intrinsic motivation, learning quality in ill-structured domains, ability to resolve ill-structured problem and creative thinking ability of the students.

Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732

Introduction of the Notion of Differential Equations by Modelling Based Teaching

ERIC Educational Resources Information Center

Budinski, Natalija; Takaci, Djurdjica

2011-01-01

This paper proposes modelling based learning as a tool for learning and teaching mathematics. The example of modelling real world problems leading to the exponential function as the solution of differential equations is described, as well as the observations about students' activities during the process. The students were acquainted with the…

Training Australian General Practitioners in Rural Public Health: Impact, Desirability and Adaptability of Hybrid Problem-Based Learning

ERIC Educational Resources Information Center

Gladman, Justin; Perkins, David

2013-01-01

Context and Objective: Australian rural general practitioners (GPs) require public health knowledge. This study explored the suitability of teaching complex public health issues related to Aboriginal health by way of a hybrid problem-based learning (PBL) model within an intensive training retreat for GP registrars, when numerous trainees have no…

Integrating Problem- and Project-Based Learning Opportunities: Assessing Outcomes of a Field Course in Environment and Sustainability

ERIC Educational Resources Information Center

Kricsfalusy, Vladimir; George, Colleen; Reed, Maureen G.

2018-01-01

Improving student competencies to address sustainability challenges has been a subject of significant debate in higher education. Problem- and project-based learning have been widely celebrated as course models that support the development of sustainability competencies. This paper describes a course developed for a professional Master's program…

Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

ERIC Educational Resources Information Center

Manouselis, Nikos; Sampson, Demetrios

This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

Learning and Teaching Mathematics through Real Life Models

ERIC Educational Resources Information Center

Takaci, Djurdjica; Budinski, Natalija

2011-01-01

This paper proposes modelling based learning as a tool for learning and teaching mathematics in high school. We report on an example of modelling real world problems in two high schools in Serbia where students were introduced for the first time to the basic concepts of modelling. Student use of computers and educational software, GeoGebra, was…

Research on Model of Student Engagement in Online Learning

ERIC Educational Resources Information Center

Peng, Wang

2017-01-01

In this study, online learning refers students under the guidance of teachers through the online learning platform for organized learning. Based on the analysis of related research results, considering the existing problems, the main contents of this paper include the following aspects: (1) Analyze and study the current student engagement model.…

Critical social theory as a model for the informatics curriculum for nursing.

PubMed

Wainwright, P; Jones, P G

2000-01-01

It is widely acknowledged that the education and training of nurses in information management and technology is problematic. Drawing from recent research this paper presents a theoretical framework within which the nature of the problems faced by nurses in the use of information may be analyzed. This framework, based on the critical social theory of Habermas, also provides a model for the informatics curriculum. The advantages of problem based learning and multi-media web-based technologies for the delivery of learning materials within this area are also discussed.

Using enquiry in learning: from vision to reality in higher education.

PubMed

Horne, Maria; Woodhead, Kath; Morgan, Liz; Smithies, Lynda; Megson, Denise; Lyte, Geraldine

2007-02-01

This paper reports on the contribution of six nurse educators to embed enquiry-led learning in a pre-registration nursing programme. Their focus was to evaluate student and facilitator perspectives of a hybrid model of problem-based learning, a form of enquiry-based learning and to focus on facilitators' perceptions of its longer-term utility with large student groups. Problem-based learning is an established learning strategy in healthcare internationally; however, insufficient evidence of its effectiveness with large groups of pre-registration students exists. Fourth Generation Evaluation was used, applying the Nominal Group Technique and Focus Group interviews, for data collection. In total, four groups representing different branches of pre-registration students (n = 121) and 15 facilitators participated. Students identified seven strengths and six areas for development related to problem-based learning. Equally, analysis of facilitators' discussions revealed several themes related to strengths and challenges. The consensus was that using enquiry aided the development of independent learning and encouraged deeper exploration of nursing and allied subject material. However, problems and frustrations were identified in relation to large numbers of groups, group dynamics, room and library resources and personal development. The implications of these findings for longer-term utility with large student groups are discussed.

Structuring Historic Site-Based History Laboratories for Teacher Education

ERIC Educational Resources Information Center

Baron, Christine

2014-01-01

Providing training for pre-service teachers at historic sites necessitates a reorientation for historic site-based teacher education programs away from strict content learning towards programs that emphasize the modeling of disciplinary problem solving and transfer learning. Outlined here is a History Lab model for teacher education that uses the…

An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification

PubMed Central

Naeini, Mahdi Pakdaman; Batal, Iyad; Liu, Zitao; Hong, CharmGil; Hauskrecht, Milos

2015-01-01

This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent and one cannot decompose the problem into a set of independent classification problems. To address the problem and properly represent label dependencies we propose and study a pairwise conditional random Field (CRF) model. We develop a new approach for learning the structure and parameters of the CRF from data. The approach maximizes the pseudo likelihood of observed labels and relies on the fast proximal gradient descend for learning the structure and limited memory BFGS for learning the parameters of the model. Empirical results on several datasets show that our approach outperforms several multi-label classification baselines, including recently published state-of-the-art methods. PMID:25927015

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.

Towards a Pragmatic Model for Group-Based, Technology-Mediated, Project-Oriented Learning - An Overview of the B2C Model

NASA Astrophysics Data System (ADS)

Lawlor, John; Conneely, Claire; Tangney, Brendan

The poor assimilation of ICT in formal education is firmly rooted in models of learning prevalent in the classroom which are largely teacher-led, individualistic and reproductive, with little connection between theory and practice and poor linkages across the curriculum. A new model of classroom practice is required to allow for creativity, peer-learning, thematic learning, collaboration and problem solving, i.e. the skills commonly deemed necessary for the knowledge-based society of the 21st century. This paper describes the B2C model for group-based, technology-mediated, project-oriented learning which, while being developed as part of an out of school programme, offers a pragmatic alternative to traditional classroom pedagogy.

Mathematics Literacy on Problem Based Learning with Indonesian Realistic Mathematics Education Approach Assisted E-Learning Edmodo

NASA Astrophysics Data System (ADS)

Wardono; Waluya, S. B.; Mariani, Scolastika; Candra D, S.

2016-02-01

This study aims to find out that there are differences in mathematical literacy ability in content Change and Relationship class VII Junior High School 19, Semarang by Problem Based Learning (PBL) model with an Indonesian Realistic Mathematics Education (called Pendidikan Matematika Realistik Indonesia or PMRI in Indonesia) approach assisted Elearning Edmodo, PBL with a PMRI approach, and expository; to know whether the group of students with learning PBL models with PMRI approach and assisted E-learning Edmodo can improve mathematics literacy; to know that the quality of learning PBL models with a PMRI approach assisted E-learning Edmodo has a good category; to describe the difficulties of students in working the problems of mathematical literacy ability oriented PISA. This research is a mixed methods study. The population was seventh grade students of Junior High School 19, Semarang Indonesia. Sample selection is done by random sampling so that the selected experimental class 1, class 2 and the control experiment. Data collected by the methods of documentation, tests and interviews. From the results of this study showed average mathematics literacy ability of students in the group PBL models with a PMRI approach assisted E-learning Edmodo better than average mathematics literacy ability of students in the group PBL models with a PMRI approach and better than average mathematics literacy ability of students in the expository models; Mathematics literacy ability in the class using the PBL model with a PMRI approach assisted E-learning Edmodo have increased and the improvement of mathematics literacy ability is higher than the improvement of mathematics literacy ability of class that uses the model of PBL learning with PMRI approach and is higher than the improvement of mathematics literacy ability of class that uses the expository models; The quality of learning using PBL models with a PMRI approach assisted E-learning Edmodo have very good category.

Bridging STEM in a Real World Problem

ERIC Educational Resources Information Center

English, Lyn D.; Mousoulides, Nicholas G.

2015-01-01

Engineering-based modeling activities provide a rich source of meaningful situations that capitalize on and extend students' routine learning. By integrating such activities within existing curricula, students better appreciate how their school learning in mathematics and science applies to problems in the outside world. Furthermore, modeling…

Influence of Problem-Based Learning Model of Learning to the Mathematical Communication Ability of Students of Grade XI IPA SMAN 14 Padang

NASA Astrophysics Data System (ADS)

Nisa, I. M.

2018-04-01

The ability of mathematical communication is one of the goals of learning mathematics expected to be mastered by students. However, reality in the field found that the ability of mathematical communication the students of grade XI IPA SMA Negeri 14 Padang have not developed optimally. This is evident from the low test results of communication skills mathematically done. One of the factors that causes this happens is learning that has not been fully able to facilitate students to develop mathematical communication skills well. By therefore, to improve students' mathematical communication skills required a model in the learning activities. One of the models learning that can be used is Problem Based learning model Learning (PBL). The purpose of this study is to see whether the ability the students' mathematical communication using the PBL model better than the students' mathematical communication skills of the learning using conventional learning in Class XI IPA SMAN 14 Padang. This research type is quasi experiment with design Randomized Group Only Design. Population in this research that is student of class XI IPA SMAN 14 Padang with sample class XI IPA 3 and class XI IPA 4. Data retrieval is done by using communication skill test mathematically shaped essay. To test the hypothesis used U-Mann test Whitney. Based on the results of data analysis, it can be concluded that the ability mathematical communication of students whose learning apply more PBL model better than the students' mathematical communication skills of their learning apply conventional learning in class XI IPA SMA 14 Padang at α = 0.05. This indicates that the PBL learning model effect on students' mathematical communication ability.

A Group Decision Approach to Developing Concept-Effect Models for Diagnosing Student Learning Problems in Mathematics

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Panjaburee, Patcharin; Triampo, Wannapong; Shih, Bo-Ying

2013-01-01

Diagnosing student learning barriers has been recognized as the most fundamental and important issue for improving the learning achievements of students. In the past decade, several learning diagnosis approaches have been proposed based on the concept-effect relationship (CER) model. However, past studies have shown that the effectiveness of this…

Scaffolding in geometry based on self regulated learning

NASA Astrophysics Data System (ADS)

Bayuningsih, A. S.; Usodo, B.; Subanti, S.

2017-12-01

This research aim to know the influence of problem based learning model by scaffolding technique on junior high school student’s learning achievement. This research took location on the junior high school in Banyumas. The research data obtained through mathematic learning achievement test and self-regulated learning (SRL) questioner. Then, the data analysis used two ways ANOVA. The results showed that scaffolding has positive effect to the mathematic learning achievement. The mathematic learning achievement use PBL-Scaffolding model is better than use PBL. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

Teacher in a problem-based learning environment - Jack of all trades?

NASA Astrophysics Data System (ADS)

Dahms, Mona Lisa; Spliid, Claus Monrad; Nielsen, Jens Frederik Dalsgaard

2017-11-01

Problem-based learning (PBL) is one among several approaches to active learning. Being a teacher in a PBL environment can, however, be a challenge because of the need to support students' learning within a broad 'landscape of learning'. In this article we will analyse the landscape of learning by use of the study activity model (SAM) developed by the Danish University Colleges, with the aim of investigating to which extent this may lead to explication and clarification concerning the challenges faced by teachers in a PBL environment. In the case study, the SAM is applied to the first semester of an engineering programme at Aalborg University, a university setting where the PBL approach to teaching and learning is dominant. The results of the analysis are presented and discussed, and the conclusion is that the model, in spite of some shortcomings, is useful in clarifying the role of the teacher in a PBL environment.

Integrating on campus problem based learning and practice based learning: issues and challenges in using computer mediated communication.

PubMed

Conway, J; Sharkey, R

2002-10-01

The Faculty of Nursing, University of Newcastle, Australia, has been keen to initiate strategies that enhance student learning and nursing practice. Two strategies are problem based learning (PBL) and clinical practice. The Faculty has maintained a comparatively high proportion of the undergraduate hours in the clinical setting in times when financial constraints suggest that simulations and on campus laboratory experiences may be less expensive.Increasingly, computer based technologies are becoming sufficiently refined to support the exploration of nursing practice in a non-traditional lecture/tutorial environment. In 1998, a group of faculty members proposed that computer mediated instruction would provide an opportunity for partnership between students, academics and clinicians that would promote more positive outcomes for all and maintain the integrity of the PBL approach. This paper discusses the similarities between problem based and practice based learning and presents the findings of an evaluative study of the implementation of a practice based learning model that uses computer mediated communication to promote integration of practice experiences with the broader goals of the undergraduate curriculum.

When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition

ERIC Educational Resources Information Center

Janssen, Christian P.; Gray, Wayne D.

2012-01-01

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other…

Development of innovative problem based learning model with PMRI-scientific approach using ICT to increase mathematics literacy and independence-character of junior high school students

NASA Astrophysics Data System (ADS)

Wardono; Waluya, B.; Kartono; Mulyono; Mariani, S.

2018-03-01

This research is very urgent in relation to the national issue of human development and the nation's competitiveness because of the ability of Indonesian Junior High School students' mathematics literacy results of the Programme for International Student Assessment (PISA) by OECD field of Mathematics is still very low compared to other countries. Curriculum 2013 launched one of them reflect the results of PISA which is still far from the expectations of the Indonesian nation and to produce a better quality of education, PISA ratings that reflect the nation's better competitiveness need to be developed innovative, interactive learning models such as innovative interactive learning Problem Based Learning (PBL) based on the approach of Indonesian Realistic Mathematics Education (PMRI) and the Scientific approach using Information and Communication Technology (ICT).The research was designed using Research and Development (R&D), research that followed up the development and dissemination of a product/model. The result of the research shows the innovative interactive learning PBL model based on PMRI-Scientific using ICT that developed valid, practical and effective and can improve the ability of mathematics literacy and independence-character of junior high school students. While the quality of innovative interactive learning PBL model based on PMRI-Scientific using ICT meet the good category.

Evaluation of students' experience with Problem-based Learning (PBL) applied at the College of Medicine, Al-Jouf University, Saudi Arabia.

PubMed

Alduraywish, Abdulrahman Abdulwahab; Mohager, Mazin Omer; Alenezi, Mohammed Jayed; Nail, Abdelsalam Mohammed; Aljafari, Alfatih Saifudinn

2017-12-01

To evaluate the students' experience with problem-based learning. This cross-sectional, qualitative study was conducted at the College of Medicine, Al Jouf University, Sakakah, Saudi Arabia, in October 2015, and comprised medical students of the 1st to 5th levels. Interviews were conducted using Students' Course Experience Questionnaire. The questionnaire contained 37 questions covering six evaluative categories: appropriate assessment, appropriate workload, clear goals and standards, generic skills, good teaching, and overall satisfaction. The questionnaire follows the Likert's scale model. Mean values were interpreted as: >2.5= at least disagree, 2.5->3= neither/nor (uncertain), and 3 or more= at least agree. Of the 170 respondents, 72(42.7%) agreed that there was an appropriate assessment accompanied with the problem-based learning. Also, 107(63.13%) students agreed that there was a heavy workload on them. The goal and standards of the course were clear for 71(42.35%) students, 104(61.3%) agreed that problem-based learning improved their generic skills, 65(38.07%) agreed the teaching was good and 82(48.08%) students showed overall satisfaction. The students were satisfied with their experience with the problem-based learning.

Analysis of Learning Tools in the study of Developmental of Interactive Multimedia Based Physic Learning Charged in Problem Solving

NASA Astrophysics Data System (ADS)

Manurung, Sondang; Demonta Pangabean, Deo

2017-05-01

The main purpose of this study is to produce needs analysis, literature review, and learning tools in the study of developmental of interactive multimedia based physic learning charged in problem solving to improve thinking ability of physic prospective student. The first-year result of the study is: result of the draft based on a needs analysis of the facts on the ground, the conditions of existing learning and literature studies. Following the design of devices and instruments performed as well the development of media. Result of the second study is physics learning device -based interactive multimedia charged problem solving in the form of textbooks and scientific publications. Previous learning models tested in a limited sample, then in the evaluation and repair. Besides, the product of research has an economic value on the grounds: (1) a virtual laboratory to offer this research provides a solution purchases physics laboratory equipment is expensive; (2) address the shortage of teachers of physics in remote areas as a learning tool can be accessed offline and online; (3). reducing material or consumables as tutorials can be done online; Targeted research is the first year: i.e story board learning physics that have been scanned in a web form CD (compact disk) and the interactive multimedia of gas Kinetic Theory concept. This draft is based on a needs analysis of the facts on the ground, the existing learning conditions, and literature studies. Previous learning models tested in a limited sample, then in the evaluation and repair.

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.

Analysis of factors affecting satisfaction level on problem based learning approach using structural equation modeling

NASA Astrophysics Data System (ADS)

Hussain, Nur Farahin Mee; Zahid, Zalina

2014-12-01

Nowadays, in the job market demand, graduates are expected not only to have higher performance in academic but they must also be excellent in soft skill. Problem-Based Learning (PBL) has a number of distinct advantages as a learning method as it can deliver graduates that will be highly prized by industry. This study attempts to determine the satisfaction level of engineering students on the PBL Approach and to evaluate their determinant factors. The Structural Equation Modeling (SEM) was used to investigate how the factors of Good Teaching Scale, Clear Goals, Student Assessment and Levels of Workload affected the student satisfaction towards PBL approach.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

PubMed Central

Smith, J. David; Ell, Shawn W.

2015-01-01

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so. PMID:26332587

An Instructional Model to Support Problem-Based Historical Inquiry: The Persistent Issues in History Network

ERIC Educational Resources Information Center

Brush, Thomas; Saye, John

2014-01-01

For over a decade, we have collaborated with secondary school history teachers in an evolving line of inquiry that applies research-based propositions to the design and testing of a problem-based learning framework and a set of wise practices that represent a professional teaching knowledge base for implementing a particular model of instruction,…

Service-based Solutions.

ERIC Educational Resources Information Center

Cummings, Lynda; Winston, Michael

1998-01-01

Describes the Solutions model used at Shelley High School in Idaho which gives students the opportunity to gain practical experience while tackling community problems. This approach is built on the three fundamentals of an integrated curriculum, a problem-solving focus, and service-based learning. Sample problems include increasing certain trout…

Research in the Real World: Improving Adult Learners Web Search and Evaluation Skills through Motivational Design and Problem-Based Learning

ERIC Educational Resources Information Center

Roberts, Lindsay

2017-01-01

How can we better engage adult learners during information literacy sessions? How do we increase students' perception of the relevance and importance of information literacy skills for academic work and life in the real world? To explore these questions, the ARCS Model of Motivational Design and Problem-Based Learning were used to develop…

The problem with outcomes-based curricula in medical education: insights from educational theory.

PubMed

Rees, Charlotte E

2004-06-01

Educators across the world are charged with the responsibility of producing core learning outcomes for medical curricula. However, much educational theory exists which deliberates the value of learning outcomes in education. This paper aims to discuss the problems surrounding outcomes-based curricula in medical education, using insights from educational theory. The paper begins with a discussion of the traditions, values and ideologies of medical curricula. It continues by analysing the issue of control within the curriculum and argues that curriculum designers and teachers control product-orientated curricula, leading to student disempowerment. The paper debates outcomes-based curricula from an ideological perspective and argues that learning outcomes cannot specify exactly what is to be achieved as a result of learning. The paper argues that medical schools should adopt a model for co-operative control of the curriculum, thus empowering learners. The paper also suggests that medical educators should determine the value of precise learning outcomes before blindly adopting an outcomes-based model.

Preparing new nurses with complexity science and problem-based learning.

PubMed

Hodges, Helen F

2011-01-01

Successful nurses function effectively with adaptability, improvability, and interconnectedness, and can see emerging and unpredictable complex problems. Preparing new nurses for complexity requires a significant change in prevalent but dated nursing education models for rising graduates. The science of complexity coupled with problem-based learning and peer review contributes a feasible framework for a constructivist learning environment to examine real-time systems data; explore uncertainty, inherent patterns, and ambiguity; and develop skills for unstructured problem solving. This article describes a pilot study of a problem-based learning strategy guided by principles of complexity science in a community clinical nursing course. Thirty-five senior nursing students participated during a 3-year period. Assessments included peer review, a final project paper, reflection, and a satisfaction survey. Results were higher than expected levels of student satisfaction, increased breadth and analysis of complex data, acknowledgment of community as complex adaptive systems, and overall higher level thinking skills than in previous years. 2011, SLACK Incorporated.

The Aalborg University PO-PBL Model from a Socio-Cultural Learning Perspective

ERIC Educational Resources Information Center

Hernandez, Carola; Ravn, Ole; Valero, Paola

2015-01-01

Since the 1970s, Aalborg University has been developing a new pedagogical model in higher education: The Project Oriented-Problem Based Learning (PO-PBL). In particular, the Faculty of Engineering and Science has developed a pedagogical proposal that introduces students to a different type of learning. One of the theoretical frameworks…

Pedagogy of the logic model: teaching undergraduates to work together to change their communities.

PubMed

Zimmerman, Lindsey; Kamal, Zohra; Kim, Hannah

2013-01-01

Undergraduate community psychology courses can empower students to address challenging problems in their local communities. Creating a logic model is an experiential way to learn course concepts by "doing." Throughout the semester, students work with peers to define a problem, develop an intervention, and plan an evaluation focused on an issue of concern to them. This report provides an overview of how to organize a community psychology course around the creation of a logic model in order for students to develop this applied skill. Two undergraduate student authors report on their experience with the logic model assignment, describing the community problem they chose to address, what they learned from the assignment, what they found challenging, and what they are doing now in their communities based on what they learned.

Working To Learn: A Holistic Approach to Young People's Education and Training.

ERIC Educational Resources Information Center

Senker, Peter; Rainbird, Helen; Evans, Karen; Hodkinson, Phil; Keep, Ewart; Maguire, Malcolm; Raffe, David; Unwin, Lorna

2000-01-01

Highlights deficiencies in current British policies on work-based learning for 16-19 year-olds. Discusses problems arising from employers' voluntary participation. Outlines a holistic approach based on the community of practice model. (SK)

Promoting Post-Formal Thinking in a U.S. History Survey Course: A Problem-Based Approach

ERIC Educational Resources Information Center

Wynn, Charles T.; Mosholder, Richard S.; Larsen, Carolee A.

2016-01-01

This article presents a problem-based learning (PBL) model for teaching a college U.S. history survey course (U.S. history since 1890) designed to promote postformal thinking skills and identify and explain thinking systems inherent in adult complex problem-solving. We also present the results of a study in which the outcomes of the PBL model were…

Learning to read aloud: A neural network approach using sparse distributed memory

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

New Dental Accreditation Standard on Critical Thinking: A Call for Learning Models, Outcomes, Assessments.

PubMed

Johnsen, David C; Williams, John N; Baughman, Pauletta Gay; Roesch, Darren M; Feldman, Cecile A

2015-10-01

This opinion article applauds the recent introduction of a new dental accreditation standard addressing critical thinking and problem-solving, but expresses a need for additional means for dental schools to demonstrate they are meeting the new standard because articulated outcomes, learning models, and assessments of competence are still being developed. Validated, research-based learning models are needed to define reference points against which schools can design and assess the education they provide to their students. This article presents one possible learning model for this purpose and calls for national experts from within and outside dental education to develop models that will help schools define outcomes and assess performance in educating their students to become practitioners who are effective critical thinkers and problem-solvers.

Problem-Posing in Education: Transformation of the Practice of the Health Professional.

ERIC Educational Resources Information Center

Casagrande, L. D. R.; Caron-Ruffino, M.; Rodrigues, R. A. P.; Vendrusculo, D. M. S.; Takayanagui, A. M. M.; Zago, M. M. F.; Mendes, M. D.

1998-01-01

Studied the use of a problem-posing model in health education. The model based on the ideas of Paulo Freire is presented. Four innovative experiences of teaching-learning in environmental and occupational health and patient education are reported. Notes that the problem-posing model has the capability to transform health-education practice.…

Impact of Learning Model Based on Cognitive Conflict toward Student’s Conceptual Understanding

NASA Astrophysics Data System (ADS)

Mufit, F.; Festiyed, F.; Fauzan, A.; Lufri, L.

2018-04-01

The problems that often occur in the learning of physics is a matter of misconception and low understanding of the concept. Misconceptions do not only happen to students, but also happen to college students and teachers. The existing learning model has not had much impact on improving conceptual understanding and remedial efforts of student misconception. This study aims to see the impact of cognitive-based learning model in improving conceptual understanding and remediating student misconceptions. The research method used is Design / Develop Research. The product developed is a cognitive conflict-based learning model along with its components. This article reports on product design results, validity tests, and practicality test. The study resulted in the design of cognitive conflict-based learning model with 4 learning syntaxes, namely (1) preconception activation, (2) presentation of cognitive conflict, (3) discovery of concepts & equations, (4) Reflection. The results of validity tests by some experts on aspects of content, didactic, appearance or language, indicate very valid criteria. Product trial results also show a very practical product to use. Based on pretest and posttest results, cognitive conflict-based learning models have a good impact on improving conceptual understanding and remediating misconceptions, especially in high-ability students.

Service-Learning in a Capstone Modeling Course

ERIC Educational Resources Information Center

Berkove, Ethan

2013-01-01

A capstone course is often synthetic, bringing together many components of a student's educational background. For this reason, a project-based course in mathematical modeling makes a great capstone, as modeling problems often require a broad collection of mathematical tools for their solution. The addition of a service-learning component can…

Investigation of learning environment for arithmetic word problems by problem posing as sentence integration in Indonesian language

NASA Astrophysics Data System (ADS)

Hasanah, N.; Hayashi, Y.; Hirashima, T.

2017-02-01

Arithmetic word problems remain one of the most difficult area of teaching mathematics. Learning by problem posing has been suggested as an effective way to improve students’ understanding. However, the practice in usual classroom is difficult due to extra time needed for assessment and giving feedback to students’ posed problems. To address this issue, we have developed a tablet PC software named Monsakun for learning by posing arithmetic word problems based on Triplet Structure Model. It uses the mechanism of sentence-integration, an efficient implementation of problem-posing that enables agent-assessment of posed problems. The learning environment has been used in actual Japanese elementary school classrooms and the effectiveness has been confirmed in previous researches. In this study, ten Indonesian elementary school students living in Japan participated in a learning session of problem posing using Monsakun in Indonesian language. We analyzed their learning activities and show that students were able to interact with the structure of simple word problem using this learning environment. The results of data analysis and questionnaire suggested that the use of Monsakun provides a way of creating an interactive and fun environment for learning by problem posing for Indonesian elementary school students.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

PubMed

Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2017-01-25

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

From Saying to Doing Interdisciplinary Learning: Is Problem-Based Learning the Answer?

ERIC Educational Resources Information Center

Stentoft, Diana

2017-01-01

Problem-based learning is often characterised as an approach encompassing interdisciplinary learning; however, little attention has been explicitly paid to what a claim of interdisciplinary problem-based learning means in practice. Even less attention has been given to address the consequences of interdisciplinary problem-based learning for…

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.

Modeling social learning of language and skills.

PubMed

Vogt, Paul; Haasdijk, Evert

2010-01-01

We present a model of social learning of both language and skills, while assuming—insofar as possible—strict autonomy, virtual embodiment, and situatedness. This model is built by integrating various previous models of language development and social learning, and it is this integration that, under the mentioned assumptions, provides novel challenges. The aim of the article is to investigate what sociocognitive mechanisms agents should have in order to be able to transmit language from one generation to the next so that it can be used as a medium to transmit internalized rules that represent skill knowledge. We have performed experiments where this knowledge solves the familiar poisonous-food problem. Simulations reveal under what conditions, regarding population structure, agents can successfully solve this problem. In addition to issues relating to perspective taking and mutual exclusivity, we show that agents need to coordinate interactions so that they can establish joint attention in order to form a scaffold for language learning, which in turn forms a scaffold for the learning of rule-based skills. Based on these findings, we conclude by hypothesizing that social learning at one level forms a scaffold for the social learning at another, higher level, thus contributing to the accumulation of cultural knowledge.

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.

Failing to Learn: Towards a Unified Design Approach for Failure-Based Learning

ERIC Educational Resources Information Center

Tawfik, Andrew A.; Rong, Hui; Choi, Ikseon

2015-01-01

To date, many instructional systems are designed to support learners as they progress through a problem-solving task. Often these systems are designed in accordance with instructional design models that progress the learner efficiently through the problem-solving process. However, theories from various fields have discussed failure as a strategic…

Implications of Bandura's Observational Learning Theory for a Competency Based Teacher Education Model.

ERIC Educational Resources Information Center

Hartjen, Raymond H.

Albert Bandura of Stanford University has proposed four component processes to his theory of observational learning: a) attention, b) retention, c) motor reproduction, and d) reinforcement and motivation. This study represents one phase of an effort to relate modeling and observational learning theory to teacher training. The problem of this study…

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

PubMed

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

Effect of Face-to-face Education, Problem-based Learning, and Goldstein Systematic Training Model on Quality of Life and Fatigue among Caregivers of Patients with Diabetes.

PubMed

Masoudi, Reza; Soleimani, Mohammad Ali; Yaghoobzadeh, Ameneh; Baraz, Shahram; Hakim, Ashrafalsadat; Chan, Yiong H

2017-01-01

Education is a fundamental component for patients with diabetes to achieve good glycemic control. In addition, selecting the appropriate method of education is one of the most effective factors in the quality of life. The present study aimed to evaluate the effect of face-to-face education, problem-based learning, and Goldstein systematic training model on the quality of life (QOL) and fatigue among caregivers of patients with diabetes. This randomized clinical trial was conducted in Hajar Hospital (Shahrekord, Iran) in 2012. The study subjects consisted of 105 family caregivers of patients with diabetes. The participants were randomly assigned to three intervention groups (35 caregivers in each group). For each group, 5-h training sessions were held separately. QOL and fatigue were evaluated immediately before and after the intervention, and after 1, 2, 3, and 4 months of intervention. There was a significant increase in QOL for all the three groups. Both the problem-based learning and the Goldstein method showed desirable QOL improvement over time. The desired educational intervention for fatigue reduction during the 4-month post-intervention period was the Goldstein method. A significant reduction was observed in fatigue in all three groups after the intervention ( P < 0.001). The results of the present study illustrated that the problem-based learning and Goldstein systematic training model improve the QOL of caregivers of patients with diabetes. In addition, the Goldstein systematic training model had the greatest effect on the reduction of fatigue within 4 months of the intervention.

Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

Testing the effectiveness of problem-based learning with learning-disabled students in biology

NASA Astrophysics Data System (ADS)

Guerrera, Claudia Patrizia

The purpose of the present study was to investigate the effects of problem-based learning (PBL) with learning-disabled (LD) students. Twenty-four students (12 dyads) classified as LD and attending a school for the learning-disabled participated in the study. Students engaged in either a computer-based environment involving BioWorld, a hospital simulation designed to teach biology students problem-solving skills, or a paper-and-pencil version based on the computer program. A hybrid model of learning was adopted whereby students were provided with direct instruction on the digestive system prior to participating in a problem-solving activity. Students worked in dyads and solved three problems involving the digestive system in either a computerized or a paper-and-pencil condition. The experimenter acted as a coach to assist students throughout the problem-solving process. A follow-up study was conducted, one month later, to measure the long-term learning gains. Quantitative and qualitative methods were used to analyze three types of data: process data, outcome data, and follow-up data. Results from the process data showed that all students engaged in effective collaboration and became more systematic in their problem solving over time. Findings from the outcome and follow-up data showed that students in both treatment conditions, made both learning and motivational gains and that these benefits were still evident one month later. Overall, results demonstrated that the computer facilitated students' problem solving and scientific reasoning skills. Some differences were noted in students' collaboration and the amount of assistance required from the coach in both conditions. Thus, PBL is an effective learning approach with LD students in science, regardless of the type of learning environment. These results have implications for teaching science to LD students, as well as for future designs of educational software for this population.

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

Problem Based Learning in Design and Technology Education Supported by Hypermedia-Based Environments

ERIC Educational Resources Information Center

Page, Tom; Lehtonen, Miika

2006-01-01

Audio-visual advances in virtual reality (VR) technology have given rise to innovative new ways to teach and learn. However, so far teaching and learning processes have been technologically driven as opposed to pedagogically led. This paper identifies the development of a pedagogical model and its application for teaching, studying and learning…

Development of Learning Resources to Promote Knowledge Sharing in Problem Based Learning

ERIC Educational Resources Information Center

Uden, Lorna; Page, Tom

2008-01-01

Problem Based Learning offers many benefits to students' learning, however, the design and implementation of effective problem based learning (PBL) is not trivial. Central to effective implementation of PBL are the problem design and group working of the students. Design of good problems requires that the learning outcomes of the subject are…

Problems as Possibilities: Problem-Based Learning for K-12 Education.

ERIC Educational Resources Information Center

Torp, Linda; Sage, Sara

Problem-based learning (PBL) is an experiential form of learning centered around the collaborative investigation and resolution of "messy, real-world" problems. This book offers opportunities to learn about problem-based learning from the perspectives of teachers, students, parents, administrators, and curriculum developers. Chapter 1 tells…

Integrating Problem-Based Learning and Simulation: Effects on Student Motivation and Life Skills.

PubMed

Roh, Young Sook; Kim, Sang Suk

2015-07-01

Previous research has suggested that a teaching strategy integrating problem-based learning and simulation may be superior to traditional lecture. The purpose of this study was to assess learner motivation and life skills before and after taking a course involving problem-based learning and simulation. The design used repeated measures with a convenience sample of 83 second-year nursing students who completed the integrated course. Data from a self-administered questionnaire measuring learner motivation and life skills were collected at pretest, post-problem-based learning, and post-simulation time points. Repeated-measures analysis of variance determined that the mean scores for total learner motivation (F=6.62, P=.003), communication (F=8.27, P<.001), problem solving (F=6.91, P=.001), and self-directed learning (F=4.45, P=.016) differed significantly between time points. Post hoc tests using the Bonferroni correction revealed that total learner motivation and total life skills significantly increased both from pretest to postsimulation and from post-problem-based learning test to postsimulation test. Subscales of learner motivation and life skills, intrinsic goal orientation, self-efficacy for learning and performance, problem-solving skills, and self-directed learning skills significantly increased both from pretest to postsimulation test and from post-problem-based learning test to post-simulation test. The results demonstrate that an integrating problem-based learning and simulation course elicits significant improvement in learner motivation and life skills. Simulation plus problem-based learning is more effective than problem-based learning alone at increasing intrinsic goal orientation, task value, self-efficacy for learning and performance, problem solving, and self-directed learning.

Simulation-Based Evaluation of Learning Sequences for Instructional Technologies

ERIC Educational Resources Information Center

McEneaney, John E.

2016-01-01

Instructional technologies critically depend on systematic design, and learning hierarchies are a commonly advocated tool for designing instructional sequences. But hierarchies routinely allow numerous sequences and choosing an optimal sequence remains an unsolved problem. This study explores a simulation-based approach to modeling learning…

New Learning: Re-Apprenticing the Learner.

ERIC Educational Resources Information Center

Stockhausen, Lynette; Zimitat, Craig

2002-01-01

Discusses higher education's goal of teaching students to become independent critical thinkers and explores the theoretical background to the development of The Interdisciplinary Critical Thinking Tool (TICTT), an Internet tool based on a model that incorporates concepts from cognitive apprenticeship, problem-based learning, and critical thinking.…

Case study method and problem-based learning: utilizing the pedagogical model of progressive complexity in nursing education.

PubMed

McMahon, Michelle A; Christopher, Kimberly A

2011-08-19

As the complexity of health care delivery continues to increase, educators are challenged to determine educational best practices to prepare BSN students for the ambiguous clinical practice setting. Integrative, active, and student-centered curricular methods are encouraged to foster student ability to use clinical judgment for problem solving and informed clinical decision making. The proposed pedagogical model of progressive complexity in nursing education suggests gradually introducing students to complex and multi-contextual clinical scenarios through the utilization of case studies and problem-based learning activities, with the intention to transition nursing students into autonomous learners and well-prepared practitioners at the culmination of a nursing program. Exemplar curricular activities are suggested to potentiate student development of a transferable problem solving skill set and a flexible knowledge base to better prepare students for practice in future novel clinical experiences, which is a mutual goal for both educators and students.

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…

Beyond Problem-Based Learning: Using Dynamic PBL in Chemistry

ERIC Educational Resources Information Center

Overton, Tina L.; Randles, Christopher A.

2015-01-01

This paper describes the development and implementation of a novel pedagogy, dynamic problem-based learning. The pedagogy utilises real-world problems that evolve throughout the problem-based learning activity and provide students with choice and different data sets. This new dynamic problem-based learning approach was utilised to teach…

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Closing the Gap between Formalism and Application--PBL and Mathematical Skills in Engineering

ERIC Educational Resources Information Center

Christensen, Ole Ravn

2008-01-01

A common problem in learning mathematics concerns the gap between, on the one hand, doing the formalisms and calculations of abstract mathematics and, on the other hand, applying these in a specific contextualized setting for example the engineering world. The skills acquired through problem-based learning (PBL), in the special model used at…

Nonlinear functional approximation with networks using adaptive neurons

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1992-01-01

A novel mathematical framework for the rapid learning of nonlinear mappings and topological transformations is presented. It is based on allowing the neuron's parameters to adapt as a function of learning. This fully recurrent adaptive neuron model (ANM) has been successfully applied to complex nonlinear function approximation problems such as the highly degenerate inverse kinematics problem in robotics.

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…

Authentic assessment based showcase portfolio on learning of mathematical problem solving in senior high school

NASA Astrophysics Data System (ADS)

Sukmawati, Zuhairoh, Faihatuz

2017-05-01

The purpose of this research was to develop authentic assessment model based on showcase portfolio on learning of mathematical problem solving. This research used research and development Method (R & D) which consists of four stages of development that: Phase I, conducting a preliminary study. Phase II, determining the purpose of developing and preparing the initial model. Phase III, trial test of instrument for the initial draft model and the initial product. The respondents of this research are the students of SMAN 8 and SMAN 20 Makassar. The collection of data was through observation, interviews, documentation, student questionnaire, and instrument tests mathematical solving abilities. The data were analyzed with descriptive and inferential statistics. The results of this research are authentic assessment model design based on showcase portfolio which involves: 1) Steps in implementing the authentic assessment based Showcase, assessment rubric of cognitive aspects, assessment rubric of affective aspects, and assessment rubric of skill aspect. 2) The average ability of the students' problem solving which is scored by using authentic assessment based on showcase portfolio was in high category and the students' response in good category.

Improving Pedagogy through Action Learning and Scholarship of Teaching and Learning

ERIC Educational Resources Information Center

Albers, Cheryl

2008-01-01

This ASA Teaching Workshop explored the potential of Action Learning to use teachers' tacit knowledge to collaboratively confront pedagogical issues. The Action Learning model grows out of industrial management and is based on the notion that peers are a valuable resource for learning about how to solve the problems encountered in the workplace.…

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

PubMed Central

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-01-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-04-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

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.

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

PubMed Central

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data. PMID:25706988

Learning Styles of Medical Students Change in Relation to Time

ERIC Educational Resources Information Center

Gurpinar, Erol; Bati, Hilal; Tetik, Cihat

2011-01-01

The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…

Impact of the Second Semester University Modeling Instruction Course on Students' Representation Choices

ERIC Educational Resources Information Center

McPadden, Daryl; Brewe, Eric

2017-01-01

Representation use is a critical skill for learning, problem solving, and communicating in science, especially in physics where multiple representations often scaffold the understanding of a phenomenon. University Modeling Instruction, which is an active-learning, research-based introductory physics curriculum centered on students' use of…

Problem-Based Learning in Higher Education: Untold Stories.

ERIC Educational Resources Information Center

Savin-Baden, Maggi

The central argument of this book is that the potential of problem-based learning is yet to be realized in higher education. Problem-based learning is an important approach to learning, based in the experiential learning tradition, that needs to be more centrally located in higher education curricula. Part 1 of this book explores problem-based…

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Student Cognitive Difficulties and Mental Model Development of Complex Earth and Environmental Systems

NASA Astrophysics Data System (ADS)

Sell, K.; Herbert, B.; Schielack, J.

2004-05-01

Students organize scientific knowledge and reason about environmental issues through manipulation of mental models. The nature of the environmental sciences, which are focused on the study of complex, dynamic systems, may present cognitive difficulties to students in their development of authentic, accurate mental models of environmental systems. The inquiry project seeks to develop and assess the coupling of information technology (IT)-based learning with physical models in order to foster rich mental model development of environmental systems in geoscience undergraduate students. The manipulation of multiple representations, the development and testing of conceptual models based on available evidence, and exposure to authentic, complex and ill-constrained problems were the components of investigation utilized to reach the learning goals. Upper-level undergraduate students enrolled in an environmental geology course at Texas A&M University participated in this research which served as a pilot study. Data based on rubric evaluations interpreted by principal component analyses suggest students' understanding of the nature of scientific inquiry is limited and the ability to cross scales and link systems proved problematic. Results categorized into content knowledge and cognition processes where reasoning, critical thinking and cognitive load were driving factors behind difficulties in student learning. Student mental model development revealed multiple misconceptions and lacked complexity and completeness to represent the studied systems. Further, the positive learning impacts of the implemented modules favored the physical model over the IT-based learning projects, likely due to cognitive load issues. This study illustrates the need to better understand student difficulties in solving complex problems when using IT, where the appropriate scaffolding can then be implemented to enhance student learning of the earth system sciences.

Managing the Complexity of Design Problems through Studio-Based Learning

ERIC Educational Resources Information Center

Cennamo, Katherine; Brandt, Carol; Scott, Brigitte; Douglas, Sarah; McGrath, Margarita; Reimer, Yolanda; Vernon, Mitzi

2011-01-01

The ill-structured nature of design problems makes them particularly challenging for problem-based learning. Studio-based learning (SBL), however, has much in common with problem-based learning and indeed has a long history of use in teaching students to solve design problems. The purpose of this ethnographic study of an industrial design class,…

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…

Problem-Solving Models for Computer Literacy: Getting Smarter at Solving Problems. Student Lessons.

ERIC Educational Resources Information Center

Moursund, David

This book is intended for use as a student guide. It is about human problem solving and provides information on how the mind works, placing a major emphasis on the role of computers as an aid in problem solving. The book is written with the underlying philosophy of discovery-based learning based on two premises: first, through the appropriate…

The implications and outcomes of using problem-based learning to teach middle school science

NASA Astrophysics Data System (ADS)

Nowak, Jeffrey Andrew

Problem-based learning (PBL) is an educational approach where a purposefully ill-structured problem initiates learning and the teacher serves as a coach instead of an information repository (Gallagher & Stepien, 1996). This approach is becoming a very popular curricular innovation, especially at the middle and secondary levels. PBL is necessarily interdisciplinary: By modeling real-world problems, which are seldom unidisciplinary, students are required to cross the traditional disciplinary boundaries in their quest to solve the problem. PBL is also based upon the theories of situated cognition, which posit that transfer occurs infrequently and that learning requires situation-specific competence (Brown, Collins, & Duguid, 1989; Plucker & Nowak, 2000; Resnick, 1987). Rather than present students with information that they may or may not be able to use to solve problems, situated cognition stresses that knowledge should be presented in context, preferably in a problem-solving scenario (Plucker & Nowak, 2000). In addition, PBL is consistent with the principles of constructivism (Savery & Duffy, 1995). Several questions have been raised about the appropriateness of using PBL in the K--12 classroom setting. The purpose of this study is to specifically address whether or not students learn as much via PBL techniques as they do in traditional classroom settings. This was accomplished by comparing two eighth grade gifted and talented science classes in a Midwest public middle school. Focused observations, interviews, test score analyses, and document analyses were incorporated into this study. Test score analyses of pretest and posttests indicate that students in a teacher-directed classroom learn factual content at a higher rate than students learning via a PBL instructional approach. Students engaged in PBL, however, have better retention than those who learn under a teacher-directed instructional approach. Interview analyses indicate that students favor learning via PBL, but many students suggest that embedding teacher-directed lessons within a PBL unit would benefit the students more than an exclusively PBL-based curriculum.

A Comparison of Two Mathematics Problem-Solving Strategies: Facilitate Algebra-Readiness

ERIC Educational Resources Information Center

Xin, Yan Ping; Zhang, Dake; Park, Joo Young; Tom, Kinsey; Whipple, Amanda; Si, Luo

2011-01-01

The authors compared a conceptual model-based problem-solving (COMPS) approach with a general heuristic instructional approach for teaching multiplication-division word-problem solving to elementary students with learning problems (LP). The results indicate that only the COMPS group significantly improved, from pretests to posttests, their…

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

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.

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

Learning planar Ising models

DOE PAGES

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael; ...

2016-12-01

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Learning planar Ising models

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

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Alterations in choice behavior by manipulations of world model.

PubMed

Green, C S; Benson, C; Kersten, D; Schrater, P

2010-09-14

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

Alterations in choice behavior by manipulations of world model

PubMed Central

Green, C. S.; Benson, C.; Kersten, D.; Schrater, P.

2010-01-01

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) “probability matching”—a consistent example of suboptimal choice behavior seen in humans—occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning. PMID:20805507

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

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

Teaching High School Students with Learning Disabilities to Use Model Drawing Strategy to Solve Fraction and Percentage Word Problems

ERIC Educational Resources Information Center

Dennis, Minyi Shih; Knight, Jacqueline; Jerman, Olga

2016-01-01

This article describes how to teach fraction and percentage word problems using a model-drawing strategy. This cognitive strategy places emphasis on explicitly teaching students how to draw a schematic diagram to represent the qualitative relations described in the problem, and how to formulate the solution based on the schematic diagram. The…

Features and Characteristics of Problem Based Learning

ERIC Educational Resources Information Center

Ceker, Eser; Ozdamli, Fezile

2016-01-01

Throughout the years, there appears to be an increase in Problem Based Learning applications in education; and Problem Based Learning related research areas. The main aim of this research is to underline the fundamentals (basic elements) of Problem Based Learning, investigate the dimensions of research approached to PBL oriented areas (with a look…

Students’ errors in solving combinatorics problems observed from the characteristics of RME modeling

NASA Astrophysics Data System (ADS)

Meika, I.; Suryadi, D.; Darhim

2018-01-01

This article was written based on the learning evaluation results of students’ errors in solving combinatorics problems observed from the characteristics of Realistic Mathematics Education (RME); that is modeling. Descriptive method was employed by involving 55 students from two international-based pilot state senior high schools in Banten. The findings of the study suggested that the students still committed errors in simplifying the problem as much 46%; errors in making mathematical model (horizontal mathematization) as much 60%; errors in finishing mathematical model (vertical mathematization) as much 65%; and errors in interpretation as well as validation as much 66%.

A comparison of problem-based and traditional education on nursing students' critical thinking dispositions.

PubMed

Ozturk, Candan; Muslu, Gonca Karayagiz; Dicle, Aklime

2008-07-01

Determining the critical thinking (CT) levels of students in undergraduate nursing schools is important in terms of establishing the methods of education that should be used. Although there is some evidence that active learning approaches like problem-based learning are effective in developing CT, the findings are inconclusive. This descriptive analytic study compared levels of critical thinking among senior nursing students (N=147) in two educational programs, one of which used a problem-based learning (PBL) model while the other used a traditional model. The California critical thinking disposition inventory (CCTDI) was used as a data collection tool. Comparisons between the groups were made using t-test analysis. There was a significant difference (p<0.05) between the critical thinking disposition scores of the seniors in the PBL school and those in the school implementing the traditional model. Analysis of sub-scale scores showed significant differences in truth-seeking and open-mindedness. These findings add to the evidence that the active and self-directed nature of PBL encourages students' ability to think critically, be tolerant of the ideas of others and evaluate conflicting information before reaching a conclusion.

Pedagogical Catalysts of Civic Competence: The Development of a Critical Epistemological Model for Community-Based Learning

ERIC Educational Resources Information Center

Stokamer, Stephanie

2013-01-01

Democratic problem-solving necessitates an active and informed citizenry, but existing research on service-learning has shed little light on the relationship between pedagogical practices and civic competence outcomes. This study developed and tested a model to represent that relationship and identified pedagogical catalysts of civic competence…

The Flipped Classroom: An active teaching and learning strategy for making the sessions more interactive and challenging.

PubMed

Sultan, Amber Shamim

2018-04-01

Flipping the classroom is a pedagogical model that employs easy to use, readily accessible technology based resources such as video lectures, reading handouts, and practice problems outside the classroom, whereas interactive group-based, problem-solving activities conducted in the classroom. This strategy permits for an extended range of learning activities during the session. Using class time for active learning provides greater opportunity for mentoring and peer to peer collaboration. Instead of spending too much time on delivering lectures, class time can best be utilized by interacting with students, discussing their concerns related to the particular topic to be taught, providing real life examples relevant to the course content, challenging students to think in a broader aspect about complex process and encouraging different team based learning activities.

Learning Petri net models of non-linear gene interactions.

PubMed

Mayo, Michael

2005-10-01

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.

Blending problem-based learning with Web technology positively impacts student learning outcomes in acid-base physiology.

PubMed

Taradi, Suncana Kukolja; Taradi, Milan; Radic, Kresimir; Pokrajac, Niksa

2005-03-01

World Wide Web (Web)-based learning (WBL), problem-based learning (PBL), and collaborative learning are at present the most powerful educational options in higher education. A blended (hybrid) course combines traditional face-to-face and WBL approaches in an educational environment that is nonspecific as to time and place. To provide educational services for an undergraduate second-year elective course in acid-base physiology, a rich, student-centered educational Web-environment designed to support PBL was created by using Web Course Tools courseware. The course is designed to require students to work in small collaborative groups using problem solving activities to develop topic understanding. The aim of the study was to identify the impact of the blended WBL-PBL-collaborative learning environment on student learning outcomes. Student test scores and satisfaction survey results from a blended WBL-PBL-based test group (n = 37) were compared with a control group whose instructional opportunities were from a traditional in-class PBL model (n = 84). WBL students scored significantly (t = 3.3952; P = 0.0009) better on the final acid-base physiology examination and expressed a positive attitude to the new learning environment in the satisfaction survey. Expressed in terms of a difference effect, the mean of the treated group (WBL) is at the 76th percentile of the untreated (face-to-face) group, which stands for a "medium" effect size. Thus student progress in the blended WBL-PBL collaborative environment was positively affected by the use of technology.

Feedback control by online learning an inverse model.

PubMed

Waegeman, Tim; Wyffels, Francis; Schrauwen, Francis

2012-10-01

A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made.

The effect of brain based learning with contextual approach viewed from adversity quotient

NASA Astrophysics Data System (ADS)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi, R.

2018-05-01

The aim of this research was to find out the effect of Brain Based Learning (BBL) with contextual approach viewed from adversity quotient (AQ) on mathematics achievement. BBL-contextual is the model to optimize the brain in the new concept learning and real life problem solving by making the good environment. Adversity Quotient is the ability to response and faces the problems. In addition, it is also about how to turn the difficulties into chances. This AQ classified into quitters, campers, and climbers. The research method used in this research was quasi experiment by using 2x3 factorial designs. The sample was chosen by using stratified cluster random sampling. The instruments were test and questionnaire for the data of AQ. The results showed that (1) BBL-contextual is better than direct learning on mathematics achievement, (2) there is no significant difference between each types of AQ on mathematics achievement, and (3) there is no interaction between learning model and AQ on mathematics achievement.

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

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.

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.

Engaging Future Teachers in Problem-Based Learning with the Park City Mathematics Institute Problems

ERIC Educational Resources Information Center

Pilgrim, Mary E.

2014-01-01

Problem-based learning (PBL) is a pedagogical technique recommended for K-12 mathematics classrooms. However, the mathematics courses in future teachers' degree programs are often lecture based. Students typically learn about problem-based learning in theory, but rarely get to experience it first-hand in their mathematics courses. The premise…

Learning control system design based on 2-D theory - An application to parallel link manipulator

NASA Technical Reports Server (NTRS)

Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

1990-01-01

An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

Faculty Adaptation to an Experimental Curriculum.

ERIC Educational Resources Information Center

Moore-West, Maggi; And Others

The adjustment of medical school faculty members to a new curriculum, called problem-based learning, was studied. Nineteen faculty members who taught in both a lecture-based and tutorial program over 2 academic years were surveyed. Besides the teacher-centered approach, the other model of learning was student-centered and could be conducted in…

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

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…

Tracing for the problem-solving ability in advanced calculus class based on modification of SAVI model at Universitas Negeri Semarang

NASA Astrophysics Data System (ADS)

Pujiastuti, E.; Waluya, B.; Mulyono

2018-03-01

There were many ways of solving the problem offered by the experts. The author combines various ways of solving the problem as a form of novelty. Among the learning model that was expected to support the growth of problem-solving skills was SAVI. The purpose, to obtain trace results from the analysis of the problem-solving ability of students in the Dual Integral material. The research method was a qualitative approach. Its activities include tests was filled with mathematical connections, observation, interviews, FGD, and triangulation. The results were: (1) some students were still experiencing difficulties in solving the problems. (2) The application of modification of SAVI learning model effective in supporting the growth of problem-solving abilities. (3) The strength of the students related to solving the problem, there were two students in the excellent category, there were three students in right classes and one student in the medium group.

ROENTGEN: case-based reasoning and radiation therapy planning.

PubMed Central

Berger, J.

1992-01-01

ROENTGEN is a design assistant for radiation therapy planning which uses case-based reasoning, an artificial intelligence technique. It learns both from specific problem-solving experiences and from direct instruction from the user. The first sort of learning is the normal case-based method of storing problem solutions so that they can be reused. The second sort is necessary because ROENTGEN does not, initially, have an internal model of the physics of its problem domain. This dependence on explicit user instruction brings to the forefront representational questions regarding indexing, failure definition, failure explanation and repair. This paper presents the techniques used by ROENTGEN in its knowledge acquisition and design activities. PMID:1482869

The effects of a problem-based learning digital game on continuing motivation to learn science

NASA Astrophysics Data System (ADS)

Toprac, Paul K.

The purpose of this study was to determine whether playing a problem-based learning (PBL) computer game, Alien Rescue III, would promote continuing motivation (CM) to learn science, and to explore the possible sources of CM. Another goal was to determine whether CM and interest to learn science in the classroom were identical constructs. CM was defined as the pursuit of academic learning goals in noninstructional contexts that were initially encountered in the classroom. Alien Rescue was played for a total of 9 hours in the seventh grade of a private middle school with 44 students, total, participating. The study used a design-based research approach that attempted to triangulate quantitative and qualitative methods. A science knowledge test, and two self-report questionnaires---one measuring motivation and one measuring CM---were administered preintervention, postintervention, and follow-up. Qualitative data was also collected, including student interviews, classroom observations, written responses, and a science teacher interview. Repeated measures ANOVAs were used to determine any significant changes in scores. A multiple regression analysis was used to explore whether a model of CM could be determined using the Eccles' expectancy-value achievement motivation model. The constant comparative method was used to obtain relevant information from the qualitative data. Based on contradictory quantitative and qualitative findings, results were mixed as to whether students exhibited an increase in CM to learn space science. Students continued to freely engage Alien Rescue during the mid-class break, but this does not strictly adhere to the definition of CM. However, many students did find space science more interesting than anticipated and developed increased desire to learn more in class, if not outside of class. Results also suggest that CM and interest in learning more in class are separate but related constructs. Finally, no satisfactory model emerged from the multiple regression analysis but based on students' interviews, continuing interest to learn is influenced by all the components of Eccles' expectancy-value model. Response effects may have confounded quantitative results. Discussion includes challenges of researching in classrooms, CM, and Eccles' motivational model, and the tension between PBL and game based approaches. Future design recommendations and research directions are provided.

Vertical and horizontal integration of knowledge and skills - a working model.

PubMed

Snyman, W D; Kroon, J

2005-02-01

The new integrated outcomes-based curriculum for dentistry was introduced at the University of Pretoria in 1997. The first participants graduated at the end of 2001. Educational principles that underpin the new innovative dental curriculum include vertical and horizontal integration, problem-oriented learning, student-centred learning, a holistic attitude to patient care and the promotion of oral health. The aim of this research project was to develop and assay a model to facilitate vertical integration of knowledge and skills thereby justifying the above mentioned action. The learning methodology proposed for the specific outcome of the Odontology module, namely the diagnosis of dental caries and the design of a primary preventive programme, included problem-solving as the driving force for the facilitation of vertical and horizontal integration, and an instructional design for the integration of the basic knowledge and clinical skills into a single learning programme. The paper describes the methodology of problem-oriented learning as applied in this study together with the detail of the programme. The consensus of those teachers who represent the basic and clinical sciences and who participate in this learning programme is that this model is practical and can assist vertical as well as horizontal integration of knowledge.

Probabilities and predictions: modeling the development of scientific problem-solving skills.

PubMed

Stevens, Ron; Johnson, David F; Soller, Amy

2005-01-01

The IMMEX (Interactive Multi-Media Exercises) Web-based problem set platform enables the online delivery of complex, multimedia simulations, the rapid collection of student performance data, and has already been used in several genetic simulations. The next step is the use of these data to understand and improve student learning in a formative manner. This article describes the development of probabilistic models of undergraduate student problem solving in molecular genetics that detailed the spectrum of strategies students used when problem solving, and how the strategic approaches evolved with experience. The actions of 776 university sophomore biology majors from three molecular biology lecture courses were recorded and analyzed. Each of six simulations were first grouped by artificial neural network clustering to provide individual performance measures, and then sequences of these performances were probabilistically modeled by hidden Markov modeling to provide measures of progress. The models showed that students with different initial problem-solving abilities choose different strategies. Initial and final strategies varied across different sections of the same course and were not strongly correlated with other achievement measures. In contrast to previous studies, we observed no significant gender differences. We suggest that instructor interventions based on early student performances with these simulations may assist students to recognize effective and efficient problem-solving strategies and enhance learning.

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

Developing material for promoting problem-solving ability through bar modeling technique

NASA Astrophysics Data System (ADS)

Widyasari, N.; Rosiyanti, H.

2018-01-01

This study aimed at developing material for enhancing problem-solving ability through bar modeling technique with thematic learning. Polya’s steps of problem-solving were chosen as the basis of the study. The methods of the study were research and development. The subject of this study were five teen students of the fifth grade of Lab-school FIP UMJ elementary school. Expert review and student’ response analysis were used to collect the data. Furthermore, the data were analyzed using qualitative descriptive and quantitative. The findings showed that material in theme “Selalu Berhemat Energi” was categorized as valid and practical. The validity was measured by using the aspect of language, contents, and graphics. Based on the expert comments, the materials were easy to implement in the teaching-learning process. In addition, the result of students’ response showed that material was both interesting and easy to understand. Thus, students gained more understanding in learning problem-solving.

Convex Formulations of Learning from Crowds

NASA Astrophysics Data System (ADS)

Kajino, Hiroshi; Kashima, Hisashi

It has attracted considerable attention to use crowdsourcing services to collect a large amount of labeled data for machine learning, since crowdsourcing services allow one to ask the general public to label data at very low cost through the Internet. The use of crowdsourcing has introduced a new challenge in machine learning, that is, coping with low quality of crowd-generated data. There have been many recent attempts to address the quality problem of multiple labelers, however, there are two serious drawbacks in the existing approaches, that are, (i) non-convexity and (ii) task homogeneity. Most of the existing methods consider true labels as latent variables, which results in non-convex optimization problems. Also, the existing models assume only single homogeneous tasks, while in realistic situations, clients can offer multiple tasks to crowds and crowd workers can work on different tasks in parallel. In this paper, we propose a convex optimization formulation of learning from crowds by introducing personal models of individual crowds without estimating true labels. We further extend the proposed model to multi-task learning based on the resemblance between the proposed formulation and that for an existing multi-task learning model. We also devise efficient iterative methods for solving the convex optimization problems by exploiting conditional independence structures in multiple classifiers.

The Relationships between Problem Design and Learning Process in Problem-Based Learning Environments: Two Cases

ERIC Educational Resources Information Center

Hung, Woei; Mehl, Katherine; Holen, Jodi Bergland

2013-01-01

Some researchers have argued that the design of problems used in a Problem-based Learning (PBL) course or curriculum could have an impact on student learning cognitively or psychologically, such as students' self-directed learning process or engagement. To investigate the relationship between PBL problem design and students' self-directed learning…

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Towards High-Quality Reflective Learning amongst Law Undergraduate Students: Analysing Students' Reflective Journals during a Problem-Based Learning Course

ERIC Educational Resources Information Center

Rué, Joan; Font, Antoni; Cebrián, Gisela

2013-01-01

There is wide agreement that problem-based learning is a key strategy to promote individual abilities for "learning how to learn". This paper presents the main contributions that reflective journals and the problem-based learning approach can make to foster professional knowledge and quality learning in higher education. Thirty-six…

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…

Problem-based learning: an approach to enhancing learning and understanding of optics for first-year students

NASA Astrophysics Data System (ADS)

Bowe, Brian W.; Daly, Siobhan; Flynn, Cathal; Howard, Robert

2003-03-01

In this paper a model for the implementation of a problem-based learning (PBL) course for a typical year physics one programme is described. Reference is made to how PBL has been implemented in relation to geometrical and physical optics. PBL derives from the theory that learning is an active process in which the learner constructs new knowledge on the basis of current knowledge, unlike traditional teaching practices in higher education, where the emphasis is on the transmission of factual knowledge. The course consists of a set of optics related real life problems that are carefully constructed to meet specified learning outcomes. The students, working in groups, encounter these problem-solving situations and are facilitated to produce a solution. The PBL course promotes student engagement in order to achieve higher levels of cognitive learning. Evaluation of the course indicates that the students adopt a deep learning approach and that they attain a thorough understanding of the subject instead of the superficial understanding associated with surface learning. The methodology also helps students to develop metacognitive skills. Another outcome of this teaching methodology is the development of key skills such as the ability to work in a group and to communicate, and present, information effectively.

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

NASA Astrophysics Data System (ADS)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

Problem Based Learning in Science

ERIC Educational Resources Information Center

Pepper, Coral

2009-01-01

Problem based learning (PBL) is a recognised teaching and learning strategy used to engage students in deep rather than surface learning. It is also viewed as a successful strategy to align university courses with the real life professional work students are expected to undertake on graduation (Biggs, 2003). Problem based learning is practised…

The Effect of a Case-Based Reasoning Instructional Model on Korean High School Students' Awareness in Climate Change Unit

ERIC Educational Resources Information Center

Jeong, Jinwoo; Kim, Hyoungbum; Chae, Dong-hyun; Kim, Eunjeong

2014-01-01

The purpose of this study is to investigate the effects of the case-based reasoning instructional model on learning about climate change unit. Results suggest that students showed interest because it allowed them to find the solution to the problem and solve the problem for themselves by analogy from other cases such as crossword puzzles in an…

Weather, Climate, Web 2.0: 21st Century Students Speak Climate Science Well

ERIC Educational Resources Information Center

Sundberg, Cheryl White; Kennedy, Teresa; Odell, Michael R. L.

2013-01-01

Problem-based learning (PBL) and inquiry learning (IL) employ extensive scaffolding that results in cognitive load reduction and allows students to learn in complex domains. Hybrid teacher professional development models (PDM) using 21st century social collaboration tools embedding PBL and IL shows promise as a systemic approach for increasing…

The Workplace Learning Cycle: A Problem-Based Curriculum Model for the Preparation of Workplace Learning Professionals

ERIC Educational Resources Information Center

O'Connor, Bridget N.

2004-01-01

Building on the conceptual foundations suggested in the previous two papers in this issue, this article describes the application of a workplace learning cycle theory to the construction of a curriculum for a graduate-level course of study in workplace education. As a way to prepare chief learning officers and heads of corporate universities, the…

[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.

Effect of Worked Examples on Mental Model Progression in a Computer-Based Simulation Learning Environment

ERIC Educational Resources Information Center

Darabi, Aubteen; Nelson, David W.; Meeker, Richard; Liang, Xinya; Boulware, Wilma

2010-01-01

In a diagnostic problem solving operation of a computer-simulated chemical plant, chemical engineering students were randomly assigned to two groups: one studying product-oriented worked examples, the other practicing conventional problem solving. Effects of these instructional strategies on the progression of learners' mental models were examined…

ME science as mobile learning based on virtual reality

NASA Astrophysics Data System (ADS)

Fradika, H. D.; Surjono, H. D.

2018-04-01

The purpose of this article described about ME Science (Mobile Education Science) as mobile learning application learning of Fisika Inti. ME Science is a product of research and development (R&D) that was using Alessi and Trollip model. Alessi and Trollip model consists three stages that are: (a) planning include analysis of problems, goals, need, and idea of development product, (b) designing includes collecting of materials, designing of material content, creating of story board, evaluating and review product, (c) developing includes development of product, alpha testing, revision of product, validation of product, beta testing, and evaluation of product. The article describes ME Science only to development of product which include development stages. The result of development product has been generates mobile learning application based on virtual reality that can be run on android-based smartphone. These application consist a brief description of learning material, quizzes, video of material summery, and learning material based on virtual reality.

Problem-based learning in comparison with lecture-based learning among medical students.

PubMed

Faisal, Rizwan; Bahadur, Sher; Shinwari, Laiyla

2016-06-01

To compare performance of medical students exposed to problem-based learning and lecture-based learning. The descriptive study was conducted at Rehman Medical College, Peshawar, Pakistan from May 20 to September 20, 2014, and comprised 146 students of 3rd year MBBS who were randomised into two equal groups. One group was taught by the traditional lecture based learning, while problem-based learning was conducted for the other group on the same topic. At the end of sessions, the performance of the two groups was evaluated by one-best type of 50 multiple choice questions. Total marks were 100, with each question carrying 2 marks. SPSS 15 was used for statistical analysis. There were 146 students who were divided into two equal groups of 73(50%) each. The mean score in the group exposed to problem-based learning was 3.2 ± 0.8 while those attending lecture-based learning was 2.7±0.8 (p= 0.0001). Problem-based learning was more effective than lecture based learning in the academic performance of medical students.

Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

Interactive Inverse Groundwater Modeling - Addressing User Fatigue

NASA Astrophysics Data System (ADS)

Singh, A.; Minsker, B. S.

2006-12-01

This paper builds on ongoing research on developing an interactive and multi-objective framework to solve the groundwater inverse problem. In this work we solve the classic groundwater inverse problem of estimating a spatially continuous conductivity field, given field measurements of hydraulic heads. The proposed framework is based on an interactive multi-objective genetic algorithm (IMOGA) that not only considers quantitative measures such as calibration error and degree of regularization, but also takes into account expert knowledge about the structure of the underlying conductivity field expressed as subjective rankings of potential conductivity fields by the expert. The IMOGA converges to the optimal Pareto front representing the best trade- off among the qualitative as well as quantitative objectives. However, since the IMOGA is a population-based iterative search it requires the user to evaluate hundreds of solutions. This leads to the problem of 'user fatigue'. We propose a two step methodology to combat user fatigue in such interactive systems. The first step is choosing only a few highly representative solutions to be shown to the expert for ranking. Spatial clustering is used to group the search space based on the similarity of the conductivity fields. Sampling is then carried out from different clusters to improve the diversity of solutions shown to the user. Once the expert has ranked representative solutions from each cluster a machine learning model is used to 'learn user preference' and extrapolate these for the solutions not ranked by the expert. We investigate different machine learning models such as Decision Trees, Bayesian learning model, and instance based weighting to model user preference. In addition, we also investigate ways to improve the performance of these models by providing information about the spatial structure of the conductivity fields (which is what the expert bases his or her rank on). Results are shown for each of these machine learning models and the advantages and disadvantages for each approach are discussed. These results indicate that using the proposed two-step methodology leads to significant reduction in user-fatigue without deteriorating the solution quality of the IMOGA.

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.

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.

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

PubMed

Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J; Manolakos, Elias S

2013-09-01

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

Discriminatively learning for representing local image features with quadruplet model

NASA Astrophysics Data System (ADS)

Zhang, Da-long; Zhao, Lei; Xu, Duan-qing; Lu, Dong-ming

2017-11-01

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network (CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

Students' Usability Evaluation of a Web-Based Tutorial Program for College Biology Problem Solving

ERIC Educational Resources Information Center

Kim, H. S.; Prevost, L.; Lemons, P. P.

2015-01-01

The understanding of core concepts and processes of science in solving problems is important to successful learning in biology. We have designed and developed a Web-based, self-directed tutorial program, "SOLVEIT," that provides various scaffolds (e.g., prompts, expert models, visual guidance) to help college students enhance their…

Developing Environmentally Responsible Behaviours Through the Implementation of Argumentation- and Problem-Based Learning Models

NASA Astrophysics Data System (ADS)

Fettahlıoğlu, Pınar; Aydoğdu, Mustafa

2018-04-01

The purpose of this research is to investigate the effect of using argumentation and problem-based learning approaches on the development of environmentally responsible behaviours among pre-service science teachers. Experimental activities were implemented for 14 weeks for 52 class hours in an environmental education class within a science teaching department. A mixed method was used as a research design; particularly, a special type of Concurrent Nested Strategy was applied. The quantitative portion was based on the one-group pre-test and post-test models, and the qualitative portion was based on the holistic multiple-case study method. The quantitative portion of the research was conducted with 34 third-year pre-service science teachers studying at a state university. The qualitative portion of the study was conducted with six pre-service science teachers selected among the 34 pre-service science teachers based on the pre-test results obtained from an environmentally responsible behaviour scale. t tests for dependent groups were used to analyse quantitative data. Both descriptive and content analyses of the qualitative data were performed. The results of the study showed that the use of the argumentation and problem-based learning approaches significantly contributed to the development of environmentally responsible behaviours among pre-service science teachers.

Teaching renewable energy using online PBL in investigating its effect on behaviour towards energy conservation among Malaysian students: ANOVA repeated measures approach

NASA Astrophysics Data System (ADS)

Nordin, Norfarah; Samsudin, Mohd Ali; Hadi Harun, Abdul

2017-01-01

This research aimed to investigate whether online problem based learning (PBL) approach to teach renewable energy topic improves students’ behaviour towards energy conservation. A renewable energy online problem based learning (REePBaL) instruction package was developed based on the theory of constructivism and adaptation of the online learning model. This study employed a single group quasi-experimental design to ascertain the changed in students’ behaviour towards energy conservation after underwent the intervention. The study involved 48 secondary school students in a Malaysian public school. ANOVA Repeated Measure technique was employed in order to compare scores of students’ behaviour towards energy conservation before and after the intervention. Based on the finding, students’ behaviour towards energy conservation improved after the intervention.

Adaptive Greedy Dictionary Selection for Web Media Summarization.

PubMed

Cong, Yang; Liu, Ji; Sun, Gan; You, Quanzeng; Li, Yuncheng; Luo, Jiebo

2017-01-01

Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l 2,0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.

"The Child's World": a creative and visual trigger to stimulate student enquiry in a problem based learning module.

PubMed

Barron, Carol; Lambert, Veronica; Conlon, Joy; Harrington, Tracey

2008-11-01

Despite the abundance of literature on problem based learning (PBL) [Murray, I., Savin-Baden, M., 2000. Staff development in problem-based learning. Teaching in Higher Education 5 (1), 107-126; Johnson, A.K., Tinning, R.S., 2001. Meeting the challenge of problem-based learning: developing the facilitators. Nurse Education Today 21 (3), 161-169; McCourt, C., Thomas, G., 2001. Evaluation of a problem based curriculum in midwifery. Midwifery 17 (4), 323-331; Cooke, M., Moyle, K., 2002. Students' evaluation of problem-based learning. Nurse Education Today 22, 330-339; Haith-Cooper, M., 2003a. An exploration of tutors' experiences of facilitating problem-based learning. Part 1--an educational research methodology combining innovation and philosophical tradition. Nurse Education Today 23, 58-64; Haith-Cooper, M., 2003b. An exploration of tutor' experiences of facilitating problem-based learning. Part 2--implications for the facilitation of problem based learning. Nurse Education Today 23, 65-75; Rowan, C.J., Mc Court, C., Beake, S., 2007. Problem based learning in midwifery--The teacher's perspective. Nurse Education Today 27, 131-138; Rowan, C.J., Mc Court, C., Beake, S., 2008. Problem based learning in midwifery--The students' perspective. Nurse Education Today 28, 93-99] few studies focus on describing "triggers", the process involved in their development and their evaluation from students' perspective. It is clearly documented that well designed, open ended, real life and challenging "triggers" are key to the success of PBL implementation [Roberts, D., Ousey, K., 2004. Problem based learning: developing the triggers. Experiences from a first wave site. Nurse Education in Practice 4, 154-158, Gibson, I., 2005. Designing projects for learning. In: Barrett, T., Mac Labhrainn, I., Fallon, H., (Eds.), Handbook of Enquiry and Problem-based Learning: Irish Case Studies and International Perspectives. AISHE & CELT: NUI Galway. , Barrett, T., 2005. Understanding problem-based learning. In: Barrett, T. Mac Labhrainn, I., Fallon, H., (Eds.), Handbook of Enquiry and Problem-based Learning: Irish Case Studies and International Perspectives. AISHE & CELT, NUI Galway. ]. This paper outlines the planning, implementation and evaluation of a "trigger" developed for a first year undergraduate nursing module. To meet specific module learning outcomes and to stimulate student inquiry through the learning strategy of PBL, a bright and colourful collage, was constructed. This tool was then evaluated using focus group interviews. Students' perspectives centered round a core theme, 'finding a focus and taking control'. Four categories were identified illustrating students progress from 'initial confusion' to engaging with the 'trigger diversity' before confidently 'exploring their own line of inquiry', thus leading to the 'stimulation of their learning'. Consistent with previous research, we also suggest it is customary for students to experience an initial period of ambiguity as they switch from teacher led to student centered learning [Biley, F., 1999. Creating tension: under graduate students nurses' response to a problem-based learning curriculum. Nurse Education Today 19 (7), 586-589]. One challenge in developing "triggers" is that the process is primarily controlled by lecturers. We suggest that a possible way forward would be to also engage students in the development of "triggers".

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.

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.

The Promise of Technology to Confront Dilemmas in Teacher Education: The Use of WebQuests in Problem-Based Methods Courses

ERIC Educational Resources Information Center

Smith, Leigh K.; Draper, Roni Jo; Sabey, Brenda L.

2005-01-01

This qualitative study examined the use of WebQuests as a teaching tool in problem-based elementary methods courses. We explored the potential of WebQuests to address three dilemmas faced in teacher education: (a) modeling instruction that is based on current learning theory and research-based practices, (b) providing preservice teachers with…

The Effect on Pupils' Science Performance and Problem-Solving Ability through Lego: An Engineering Design-Based Modeling Approach

ERIC Educational Resources Information Center

Li, Yanyan; Huang, Zhinan; Jiang, Menglu; Chang, Ting-Wen

2016-01-01

Incorporating scientific fundamentals via engineering through a design-based methodology has proven to be highly effective for STEM education. Engineering design can be instantiated for learning as they involve mental and physical stimulation and develop practical skills especially in solving problems. Lego bricks, as a set of toys based on design…

Development of Problem-Based Learning Oriented Teaching Learning Materials to Facilitate Students’ Mastery of Concept and Critical Thinking Skill

NASA Astrophysics Data System (ADS)

Reza, M.; Ibrahim, M.; Rahayu, Y. S.

2018-01-01

This research aims to develop problem-based learning oriented teaching materials to improve students’ mastery of concept and critical thinking skill. Its procedure was divided into two phases; developmental phase and experimental phase. This developmental research used Four-D Model. However, within this research, the process of development would not involve the last stages, which is disseminate. The teaching learning materials which were developed consist of lesson plan, student handbook, student worksheet, achievement test and critical thinking skill test. The experimental phase employs a research design called one group pretest-posttest design. Results show that the validity of the teaching materials which were developed was good and revealed the enhancement of students’ activities with positive response to the teaching learning process. Furthermore, the learning materials improve the students’ mastery of concept and critical thinking skill.

Effect of structure in problem based learning on science teaching efficacy beliefs and science content knowledge of elementary preservice teachers

NASA Astrophysics Data System (ADS)

Sasser, Selena Kay

This study examined the effects of differing amounts of structure within the problem based learning instructional model on elementary preservice teachers' science teaching efficacy beliefs, including personal science teaching efficacy and science teaching outcome expectancy, and content knowledge acquisition. This study involved sixty (60) undergraduate elementary preservice teachers enrolled in three sections of elementary science methods classes at a large Midwestern research university. This study used a quasi-experimental nonequivalent design to collect and analyze both quantitative and qualitative data. Participants completed instruments designed to assess science teaching efficacy beliefs, science background, and demographic data. Quantitative data from pre and posttests was obtained using the science teaching efficacy belief instrument-preservice (STEBI-B) developed by Enochs and Riggs (1990) and modified by Bleicher (2004). Data collection instruments also included a demographic questionnaire, an analytic rubric, and a structured interview; both created by the researcher. Quantitative data was analyzed by conducting ANCOVA, paired samples t-test, and independent samples t-test. Qualitative data was analyzed using coding and themes. Each of the treatment groups received the same problem scenario, one group experienced a more structured PBL setting, and one group experienced a limited structure PBL setting. Research personnel administered pre and posttests to determine the elementary preservice teachers' science teaching efficacy beliefs. The results show elementary preservice teachers'science teaching efficacy beliefs can be influence by the problem based learning instructional model. This study did not find that the amount of structure in the form of core ideas to consider and resources for further research increased science teaching efficacy beliefs in this sample. Results from the science content knowledge rubric indicated that structure can increase science content knowledge in this sample. Qualitative data from the tutor, fidelity raters, and interviews indicated the participants were excited about the problem and were interested in the science content knowledge related to the problem. They also indicated they were motivated to continue informal study in the problem area. Participants indicated, during the interview, their initial frustration with the lack of knowledge gained from the tutor; however, indicated this led to more learning on their part. This study will contribute to the overall knowledge of problem based learning and its structures, science teaching efficacy beliefs of elementary preservice teachers, and to current teaching and learning practices.

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.

Developing teaching process for enhancing students' mathematical problem solving in the 21st century through STEM education

NASA Astrophysics Data System (ADS)

Prawvichien, Sutthaporn; Siripun, Kulpatsorn; Yuenyong, Chokchai

2018-01-01

The STEM education could provide the context for students' learning in the 21st century. The Mathematical problem solving requires a context which simulates real life in order to give students experience of the power of mathematics in the world around them. This study aimed to develop the teaching process for enhancing students' mathematical problem solving in the 21st century through STEM education. The paper will clarify the STEM learning activities about graph theories regarding on the 6 steps of engineering design process. These include identify a challenge, exploring ideas, designing and planning, doing and developing, test and evaluate, and present the solution. The learning activities will start from the Identify a challenge stage which provides the northern part of Thailand flooding situation in order to set the students' tasks of develop the solutions of providing the routes of fastest moving people away from the flooding areas. The explore ideas stage will provide activities for enhance students to learn some knowledge based for designing the possible solutions. This knowledge based could focus on measuring, geometry, graph theory, and mathematical process. The design and plan stage will ask students to model the city based on the map and then provide the possible routes. The doing and development stage will ask students to develop the routes based on their possible model. The test and evaluating will ask students to clarify how to test and evaluate the possible routes, and then test it. The present solution stage will ask students to present the whole process of designing routes. Then, the paper will discuss how these learning activities could enhance students' mathematical problem solving. The paper may have implication for STEM education in school setting.

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.

Working towards a scalable model of problem-based learning instruction in undergraduate engineering education

NASA Astrophysics Data System (ADS)

Mantri, Archana

2014-05-01

The intent of the study presented in this paper is to show that the model of problem-based learning (PBL) can be made scalable by designing curriculum around a set of open-ended problems (OEPs). The detailed statistical analysis of the data collected to measure the effects of traditional and PBL instructions for three courses in Electronics and Communication Engineering, namely Analog Electronics, Digital Electronics and Pulse, Digital & Switching Circuits is presented here. It measures the effects of pedagogy, gender and cognitive styles on the knowledge, skill and attitude of the students. The study was conducted two times with content designed around same set of OEPs but with two different trained facilitators for all the three courses. The repeatability of results for effects of the independent parameters on dependent parameters is studied and inferences are drawn.

Integration of problem-based learning and innovative technology into a self-care course.

PubMed

McFalls, Marsha

2013-08-12

To assess the integration of problem-based learning and technology into a self-care course. Problem-based learning (PBL) activities were developed and implemented in place of lectures in a self-care course. Students used technology, such as computer-generated virtual patients and iPads, during the PBL sessions. Students' scores on post-case quizzes were higher than on pre-case quizzes used to assess baseline knowledge. Student satisfaction with problem-based learning and the use of technology in the course remained consistent throughout the semester. Integrating problem-based learning and technology into a self-care course enabled students to become active learners.

A proposal for a problem-oriented pharmacobiochemistry course in dental education.

PubMed

Guven, Y; Bal, F; Issever, H; Can Trosala, S

2014-02-01

Problem-oriented learning is an effective method of learning that increases students' learning motivation, improves the relationship amongst students and results in open-minded discussions. In this study, a new problem-oriented pharmacobiochemistry course related to 'oxidative metabolism of drugs by cytochrome P450 (CYP450) systems' was designed. Students were divided into seven groups. Three keywords related to drug interaction through CYP450 were provided to each group in order for them to conduct research on the information given. After 1 month, the groups attended a session under the supervision of a tutor to solve a simulated problem case that was designed using the keywords. At the end of the integrated course, a multiple-choice examination was given. The success rate of 76 students who attended the course was found to be significantly higher than the success rate of the students who received the lecture-based course only (P < 0.0001). A questionnaire containing 20 items (Cronbach's alpha: 0.92) was administered to the students to learn about their perception regarding this educational model. The questionnaire was evaluated using the Likert scale. Student feedback was very positive, with fourteen answers rated as 'agree' and the remaining six rated as 'strongly agree'. Students thought that the problem-oriented model was very enjoyable and useful in regard to dental education. Based on these results, we conclude that this course model may help achieve an integrated curriculum for dental school programmes. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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…

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

The Role of Multiple Neuromodulators in Reinforcement Learning That Is Based on Competition between Eligibility Traces.

PubMed

Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z

2016-01-01

The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule). To address the first problem synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although, these were mere theoretical constructs, recent experiments have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward nucleus cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP) and one for long-term depression (LTD), each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although, the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different neuromodulators for expressing the LTP and LTD traces? Here we expand on our previous model to include several neuromodulators, and illustrate through various examples how different these contribute to learning reward-timing within a wide set of training paradigms and propose further roles that multiple neuromodulators can play in encoding additional information of the rewarding signal.

The effects of a shared, Intranet science learning environment on the academic behaviors of problem-solving and metacognitive reflection

NASA Astrophysics Data System (ADS)

Parker, Mary Jo

This study investigated the effects of a shared, Intranet science environment on the academic behaviors of problem-solving and metacognitive reflection. Seventy-eight subjects included 9th and 10th grade male and female biology students. A quasi-experimental design with pre- and post-test data collection and randomization occurring through assignment of biology classes to traditional or shared, Intranet learning groups was employed. Pilot, web-based distance education software (CourseInfo) created the Intranet learning environment. A modified ecology curriculum provided contextualization and content for traditional and shared learning environments. The effect of this environment on problem-solving, was measured using the standardized Watson-Glaser Critical Thinking Appraisal test. Metacognitive reflection, was measured in three ways: (a) number of concepts used, (b) number of concept links noted, and (c) number of concept nodes noted. Visual learning software, Inspiration, generated concept maps. Secondary research questions evaluated the pilot CourseInfo software for (a) tracked user movement, (b) discussion forum findings, and (c) difficulties experienced using CourseInfo software. Analysis of problem-solving group means reached no levels of significance resulting from the shared, Intranet environment. Paired t-Test of individual differences in problem-solving reached levels of significance. Analysis of metacognitive reflection by number of concepts reached levels of significance. Metacognitive reflection by number of concept links noted also reach significance. No significance was found for metacognitive reflection by number of concept nodes. No gender differences in problem-solving ability and metacognitive reflection emerged. Lack of gender differences in the shared, Intranet environment strongly suggests an equalizing effect due to the cooperative, collaborative nature of Intranet environments. Such environments appeal to, and rank high with, the female gender. Tracking learner movements in web-based, science environments has metacognitive and problem-solving learner implications. CourseInfo software offers one method of informing instruction within web-based learning environments focusing on academic behaviors. A shared, technology-supported learning environment may pose one model which science classrooms can use to create equitable scientific study across gender. The lack of significant differences resulting from this environment presents one model for improvement of individual problem-solving ability and metacognitive reflection across gender.

Exploring creativity and critical thinking in traditional and innovative problem-based learning groups.

PubMed

Chan, Zenobia C Y

2013-08-01

To explore students' attitude towards problem-based learning, creativity and critical thinking, and the relevance to nursing education and clinical practice. Critical thinking and creativity are crucial in nursing education. The teaching approach of problem-based learning can help to reduce the difficulties of nurturing problem-solving skills. However, there is little in the literature on how to improve the effectiveness of a problem-based learning lesson by designing appropriate and innovative activities such as composing songs, writing poems and using role plays. Exploratory qualitative study. A sample of 100 students participated in seven semi-structured focus groups, of which two were innovative groups and five were standard groups, adopting three activities in problem-based learning, namely composing songs, writing poems and performing role plays. The data were analysed using thematic analysis. There are three themes extracted from the conversations: 'students' perceptions of problem-based learning', 'students' perceptions of creative thinking' and 'students' perceptions of critical thinking'. Participants generally agreed that critical thinking is more important than creativity in problem-based learning and clinical practice. Participants in the innovative groups perceived a significantly closer relationship between critical thinking and nursing care, and between creativity and nursing care than the standard groups. Both standard and innovative groups agreed that problem-based learning could significantly increase their critical thinking and problem-solving skills. Further, by composing songs, writing poems and using role plays, the innovative groups had significantly increased their awareness of the relationship among critical thinking, creativity and nursing care. Nursing educators should include more types of creative activities than it often does in conventional problem-based learning classes. The results could help nurse educators design an appropriate curriculum for preparing professional and ethical nurses for future clinical practice. © 2013 Blackwell Publishing Ltd.

Analysis of a Physics Teacher's Pedagogical "Micro-Actions" That Support 17-Year-Olds' Learning of Free Body Diagrams via a Modelling Approach

ERIC Educational Resources Information Center

Tay, Su Lynn; Yeo, Jennifer

2018-01-01

Great teaching is characterised by the specific actions a teacher takes in the classroom to bring about learning. In the context of model-based teaching (MBT), teachers' difficulty in working with students' models that are not scientifically consistent is troubling. To address this problem, the aim of this study is to identify the pedagogical…

Problem-Based Learning in Foods and Nutrition Classes

ERIC Educational Resources Information Center

Smith, Bettye P.; Katz, Shana H.

2006-01-01

This article focuses on the use of problem-based learning in high school foods and nutrition classes. Problem-based learning, an instructional approach that promotes active learning, is the elaboration of knowledge that occurs through discussion, answering questions, peer teaching, and critiquing. Students are confronted with a simulated or real…

Investigative Primary Science: A Problem-Based Learning Approach

ERIC Educational Resources Information Center

Etherington, Matthew B.

2011-01-01

This study reports on the success of using a problem-based learning approach (PBL) as a pedagogical mode of learning open inquiry science within a traditional four-year undergraduate elementary teacher education program. In 2010, a problem-based learning approach to teaching primary science replaced the traditional content driven syllabus. During…

Problem-based learning in the NICU.

PubMed

Pilcher, Jobeth

2014-01-01

Problem-based learning (PBL) is an educational strategy that provides learners with the opportunity to investigate and solve realistic problem situations. It is also referred to as project-based learning or work-based learning. PBL combines several learning strategies including the use of case studies coupled with collaborative, facilitated, and self-directed learning. Research has demonstrated that use of PBL can result in learners having improved problem-solving skills, increased breadth and analysis of complex data, higher-level thinking skills, and improved collaboration. This article will include background information and a description of PBL, followed by examples of how this strategy can be used for learning in neonatal settings.

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

PubMed

Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

Problem based learning in midwifery - the teachers perspective.

PubMed

Rowan, Catherine J; McCourt, Christine; Bick, Debra; Beake, Sarah

2007-02-01

Problem- or evidence-based learning (PBL or EBL) has become more widely used in the education of health professionals. Although there has been research exploring its effectiveness and the student's perspective, there has been little research exploring the perceptions of the teacher. The objective of this study was to investigate the experiences of teachers facilitating a problem based learning curriculum in midwifery. The study took place at Thames Valley University, which has implemented this approach across the entire curriculum. Semi-structured interviews were undertaken following random selection from two groups of teachers; those more experienced as teachers and those who had entered teaching more recently. Aspects of the teacher's role identified included questioning students to draw out their knowledge and understanding and to help students challenge each other, discuss and evaluate their learning. Strategies used varied depending on the stage of the programme. Difficulties encountered were mostly in relation to facilitating groups of differing backgrounds and ability and seeking to enable the students to work well together. Key challenges for teachers were in relation to developing facilitation skills, balancing input or guidance with facilitating independent learning. Problem based learning was perceived to be beneficial in helping students relate theory to practice and in encouraging an active and enquiring approach to evidence, but teachers raised important questions about its practice. Tensions were identified between the constructivist theories on which the model of PBL rests and the formal requirements of an externally regulated professional curriculum.

Reinforcement Learning in a Nonstationary Environment: The El Farol Problem

NASA Technical Reports Server (NTRS)

Bell, Ann Maria

1999-01-01

This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.

Mobile Voting Systems for Creating Collaboration Environments and Getting Immediate Feedback: A New Curriculum Model of a University Lecture

ERIC Educational Resources Information Center

Titova, Svetlana; Talmo, Tord

2014-01-01

Mobile devices can enhance learning and teaching by providing instant feedback and better diagnosis of learning problems, helping design new assessment models, enhancing learner autonomy and creating new formats of enquiry-based activities. The objective of this paper is to investigate the pedagogical impact of mobile voting tools. The authors'…

Green Map Exercises as an Avenue for Problem-Based Learning in a Data-Rich Environment

ERIC Educational Resources Information Center

Tulloch, David; Graff, Elizabeth

2007-01-01

This article describes a series of data-based Green Map learning exercises positioned within a problem-based framework and examines the appropriateness of projects like these as a form of geography education. Problem-based learning (PBL) is an educational technique that engages students in learning through activities that require creative problem…

An introductory pharmacy practice experience based on a medication therapy management service model.

PubMed

Agness, Chanel F; Huynh, Donna; Brandt, Nicole

2011-06-10

To implement and evaluate an introductory pharmacy practice experience (IPPE) based on the medication therapy management (MTM) service model. Patient Care 2 is an IPPE that introduces third-year pharmacy students to the MTM service model. Students interacted with older adults to identify medication-related problems and develop recommendations using core MTM elements. Course outcome evaluations were based on number of documented medication-related problems, recommendations, and student reviews. Fifty-seven older adults participated in the course. Students identified 52 medication-related problems and 66 medical problems, and documented 233 recommendations relating to health maintenance and wellness, pharmacotherapy, referrals, and education. Students reported having adequate experience performing core MTM elements. Patient Care 2 may serve as an experiential learning model for pharmacy schools to teach the core elements of MTM and provide patient care services to the community.

Use of Problem-Based Learning in the Teaching and Learning of Horticultural Production

ERIC Educational Resources Information Center

Abbey, Lord; Dowsett, Eric; Sullivan, Jan

2017-01-01

Purpose: Problem-based learning (PBL), a relatively novel teaching and learning process in horticulture, was investigated. Proper application of PBL can potentially create a learning context that enhances student learning. Design/Methodology/Approach: Students worked on two complex ill-structured problems: (1) to produce fresh baby greens for a…

Example-based learning: comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge.

PubMed

Dyer, Joseph-Omer; Hudon, Anne; Montpetit-Tourangeau, Katherine; Charlin, Bernard; Mamede, Sílvia; van Gog, Tamara

2015-03-07

Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of problem-solving skills more than studying worked examples alone. Completion examples are worked examples in which some of the solution steps remain unsolved for learners to complete. Providing learners engaged in example-based learning with self-explanation prompts has been shown to foster increased meaningful learning compared to providing no self-explanation prompts. Concept mapping and concept map study are other instructional activities known to promote meaningful learning. This study compares the effects of self-explaining, completing a concept map and studying a concept map on conceptual knowledge and problem-solving skills among novice learners engaged in example-based learning. Ninety-one physiotherapy students were randomized into three conditions. They performed a pre-test and a post-test to evaluate their gains in conceptual knowledge and problem-solving skills (transfer performance) in intervention selection. They studied three pairs of worked/completion examples in a digital learning environment. Worked examples consisted of a written reasoning process for selecting an optimal physiotherapy intervention for a patient. The completion examples were partially worked out, with the last few problem-solving steps left blank for students to complete. The students then had to engage in additional self-explanation, concept map completion or model concept map study in order to synthesize and deepen their knowledge of the key concepts and problem-solving steps. Pre-test performance did not differ among conditions. Post-test conceptual knowledge was higher (P < .001) in the concept map study condition (68.8 ± 21.8%) compared to the concept map completion (52.8 ± 17.0%) and self-explanation (52.2 ± 21.7%) conditions. Post-test problem-solving performance was higher (P < .05) in the self-explanation (63.2 ± 16.0%) condition compared to the concept map study (53.3 ± 16.4%) and concept map completion (51.0 ± 13.6%) conditions. Students in the self-explanation condition also invested less mental effort in the post-test. Studying model concept maps led to greater conceptual knowledge, whereas self-explanation led to higher transfer performance. Self-explanation and concept map study can be combined with worked example and completion example strategies to foster intervention selection.

A Case-Based Learning Model in Orthodontics.

ERIC Educational Resources Information Center

Engel, Francoise E.; Hendricson, William D.

1994-01-01

A case-based, student-centered instructional model designed to mimic orthodontic problem solving and decision making in dental general practice is described. Small groups of students analyze case data, then record and discuss their diagnoses and treatments. Students and instructors rated the seminars positively, and students reported improved…

Predicting explorative motor learning using decision-making and motor noise.

PubMed

Chen, Xiuli; Mohr, Kieran; Galea, Joseph M

2017-04-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

Predicting explorative motor learning using decision-making and motor noise

PubMed Central

Galea, Joseph M.

2017-01-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning. PMID:28437451

Assisting the integration of social media in problem-based learning sessions in the Faculty of Medicine at King Abdulaziz University.

PubMed

Awan, Zuhier A; Awan, Almuatazbellah A; Alshawwa, Lana; Tekian, Ara; Park, Yoon Soo

2018-05-07

Issues related to traditional Problem-Based Learning (PBL) at King Abdulaziz University Faculty of Medicine (KAU-FOM), including lack of student interaction between sessions and outdated instructional materials have led to the examining the use of social media. This study examines factors affecting the implementation of social media into PBL sessions Methods: Mentored social media activities were incorporated between PBL sessions to third year medical students. Ground rules were set, and students were kept on track with learning objectives and authentic references. An online survey consisting of 18 questions were administered to measure the impact of the social media model embedded between PBL sessions. Feedback showed major improvements in students' learning process as well as identifying areas for improvement. The highest ratings were in participation and communication, knowledge and information gathering, and cooperation and team-building. This paper indicates that incorporating social media could facilitate learning between PBL sessions. Furthermore, guidelines are proposed to help educators implement a social media model into their PBL sessions.

The effect of learning models and emotional intelligence toward students learning outcomes on reaction rate

NASA Astrophysics Data System (ADS)

Sutiani, Ani; Silitonga, Mei Y.

2017-08-01

This research focused on the effect of learning models and emotional intelligence in students' chemistry learning outcomes on reaction rate teaching topic. In order to achieve the objectives of the research, with 2x2 factorial research design was used. There were two factors tested, namely: the learning models (factor A), and emotional intelligence (factor B) factors. Then, two learning models were used; problem-based learning/PBL (A1), and project-based learning/PjBL (A2). While, the emotional intelligence was divided into higher and lower types. The number of population was six classes containing 243 grade X students of SMAN 10 Medan, Indonesia. There were 15 students of each class were chosen as the sample of the research by applying purposive sampling technique. The data were analyzed by applying two-ways analysis of variance (2X2) at the level of significant α = 0.05. Based on hypothesis testing, there was the interaction between learning models and emotional intelligence in students' chemistry learning outcomes. Then, the finding of the research showed that students' learning outcomes in reaction rate taught by using PBL with higher emotional intelligence is higher than those who were taught by using PjBL. There was no significant effect between students with lower emotional intelligence taught by using both PBL and PjBL in reaction rate topic. Based on the finding, the students with lower emotional intelligence were quite hard to get in touch with other students in group discussion.

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Enhancement of problem solving ability of high school students through learning with real engagement in active problem solving (REAPS) model on the concept of heat transfer

NASA Astrophysics Data System (ADS)

Yulindar, A.; Setiawan, A.; Liliawati, W.

2018-05-01

This study aims to influence the enhancement of problem solving ability before and after learning using Real Engagement in Active Problem Solving (REAPS) model on the concept of heat transfer. The research method used is quantitative method with 35 high school students in Pontianak as sample. The result of problem solving ability of students is obtained through the test in the form of 3 description questions. The instrument has tested the validity by the expert judgment and field testing that obtained the validity value of 0.84. Based on data analysis, the value of N-Gain is 0.43 and the enhancement of students’ problem solving ability is in medium category. This was caused of students who are less accurate in calculating the results of answers and they also have limited time in doing the questions given.

Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.

PubMed

Riccardi, Annalisa; Fernández-Navarro, Francisco; Carloni, Sante

2014-10-01

In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the additional regularization parameter). The closed form of the derived weighted least squares problem is provided, and it is employed to estimate analytically the parameters connecting the hidden layer to the output layer at each iteration of the boosting algorithm. Compared to the state-of-the-art boosting algorithms, in particular those using ELM as base classifier, the suggested technique does not require the generation of a new training dataset at each iteration. The adoption of the weighted least squares formulation of the problem has been presented as an unbiased and alternative approach to the already existing ELM boosting techniques. Moreover, the addition of a cost model for weighting the patterns, according to the order of the targets, enables the classifier to tackle ordinal regression problems further. The proposed method has been validated by an experimental study by comparing it with already existing ensemble methods and ELM techniques for ordinal regression, showing competitive results.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Integration of Problem-based Learning and Innovative Technology Into a Self-Care Course

PubMed Central

2013-01-01

Objective. To assess the integration of problem-based learning and technology into a self-care course. Design. Problem-based learning (PBL) activities were developed and implemented in place of lectures in a self-care course. Students used technology, such as computer-generated virtual patients and iPads, during the PBL sessions. Assessments. Students’ scores on post-case quizzes were higher than on pre-case quizzes used to assess baseline knowledge. Student satisfaction with problem-based learning and the use of technology in the course remained consistent throughout the semester. Conclusion. Integrating problem-based learning and technology into a self-care course enabled students to become active learners. PMID:23966730

Mobile robots exploration through cnn-based reinforcement learning.

PubMed

Tai, Lei; Liu, Ming

2016-01-01

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information.

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

PubMed

Singh, Anita

2017-07-01

Demand of biomedical engineers continues to rise to meet the needs of healthcare industry. Current training of bioengineers follows the traditional and dominant model of theory-focused curricula. However, the unmet needs of the healthcare industry warrant newer skill sets in these engineers. Translational training strategies such as solving real world problems through active, adaptive, and experiential learning hold promise. In this paper, we report our findings of adding a real-world 4-week problem-based learning unit into a biomechanics capstone course for engineering students. Surveys assessed student perceptions of the activity and learning experience. While students, across three cohorts, felt challenged to solve a real-world problem identified during the simulation lab visit, they felt more confident in utilizing knowledge learned in the biomechanics course and self-directed research. Instructor evaluations indicated that the active and experiential learning approach fostered their technical knowledge and life-long learning skills while exposing them to the components of adaptive learning and innovation.

Assessing the Quality of Problems in Problem-Based Learning

ERIC Educational Resources Information Center

Sockalingam, Nachamma; Rotgans, Jerome; Schmidt, Henk

2012-01-01

This study evaluated the construct validity and reliability of a newly devised 32-item problem quality rating scale intended to measure the quality of problems in problem-based learning. The rating scale measured the following five characteristics of problems: the extent to which the problem (1) leads to learning objectives, (2) is familiar, (3)…

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…

A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)

DTIC Science & Technology

Information Theory (IIT) of Consciousness , which proposes that the fundamental structural elements of consciousness are qualia. By modeling the...This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories

Towards Open-World Person Re-Identification by One-Shot Group-Based Verification.

PubMed

Zheng, Wei-Shi; Gong, Shaogang; Xiang, Tao

2016-03-01

Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods.

Integrating Problem-Based Learning with Community-Engaged Learning in Teaching Program Development and Implementation

ERIC Educational Resources Information Center

Hou, Su-I

2014-01-01

Purpose: Problem-based learning (PBL) challenges students to learn and work in groups to seek solutions to real world problems. Connecting academic study with community-engaged learning (CEL) experience can deeper learning and thinking. This paper highlights the integration of PBL with CEL in the Implementation Course to engage graduate students…

Problem Posing with Realistic Mathematics Education Approach in Geometry Learning

NASA Astrophysics Data System (ADS)

Mahendra, R.; Slamet, I.; Budiyono

2017-09-01

One of the difficulties of students in the learning of geometry is on the subject of plane that requires students to understand the abstract matter. The aim of this research is to determine the effect of Problem Posing learning model with Realistic Mathematics Education Approach in geometry learning. This quasi experimental research was conducted in one of the junior high schools in Karanganyar, Indonesia. The sample was taken using stratified cluster random sampling technique. The results of this research indicate that the model of Problem Posing learning with Realistic Mathematics Education Approach can improve students’ conceptual understanding significantly in geometry learning especially on plane topics. It is because students on the application of Problem Posing with Realistic Mathematics Education Approach are become to be active in constructing their knowledge, proposing, and problem solving in realistic, so it easier for students to understand concepts and solve the problems. Therefore, the model of Problem Posing learning with Realistic Mathematics Education Approach is appropriately applied in mathematics learning especially on geometry material. Furthermore, the impact can improve student achievement.

Instructional Designers' Media Selection Practices for Distributed Problem-Based Learning Environments

ERIC Educational Resources Information Center

Fells, Stephanie

2012-01-01

The design of online or distributed problem-based learning (dPBL) is a nascent, complex design problem. Instructional designers are challenged to effectively unite the constructivist principles of problem-based learning (PBL) with appropriate media in order to create quality dPBL environments. While computer-mediated communication (CMC) tools and…

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…

An Instructional Systems Technology Model for Institutional Change.

ERIC Educational Resources Information Center

Dudgeon, Paul J.

A program based on instructional systems technology was developed at Canadore College as a means of devising the optimal learning experience for each individual student. The systems approach is used to solve educational problems through a process of analysis, synthesis, modeling, and simulation, based on the LOGOS (Language for Optimizing…

The relationship between mathematical problem-solving skills and self-regulated learning through homework behaviours, motivation, and metacognition

NASA Astrophysics Data System (ADS)

Çiğdem Özcan, Zeynep

2016-04-01

Studies highlight that using appropriate strategies during problem solving is important to improve problem-solving skills and draw attention to the fact that using these skills is an important part of students' self-regulated learning ability. Studies on this matter view the self-regulated learning ability as key to improving problem-solving skills. The aim of this study is to investigate the relationship between mathematical problem-solving skills and the three dimensions of self-regulated learning (motivation, metacognition, and behaviour), and whether this relationship is of a predictive nature. The sample of this study consists of 323 students from two public secondary schools in Istanbul. In this study, the mathematics homework behaviour scale was administered to measure students' homework behaviours. For metacognition measurements, the mathematics metacognition skills test for students was administered to measure offline mathematical metacognitive skills, and the metacognitive experience scale was used to measure the online mathematical metacognitive experience. The internal and external motivational scales used in the Programme for International Student Assessment (PISA) test were administered to measure motivation. A hierarchic regression analysis was conducted to determine the relationship between the dependent and independent variables in the study. Based on the findings, a model was formed in which 24% of the total variance in students' mathematical problem-solving skills is explained by the three sub-dimensions of the self-regulated learning model: internal motivation (13%), willingness to do homework (7%), and post-problem retrospective metacognitive experience (4%).

Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?

NASA Astrophysics Data System (ADS)

Markauskaite, Lina; Kelly, Nick; Jacobson, Michael J.

2017-12-01

This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students' reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.

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

ERIC Educational Resources Information Center

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

2016-01-01

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

The Effect of Problem Based Learning (PBL) Instruction on Students' Motivation and Problem Solving Skills of Physics

ERIC Educational Resources Information Center

Argaw, Aweke Shishigu; Haile, Beyene Bashu; Ayalew, Beyene Tesfaw; Kuma, Shiferaw Gadisa

2017-01-01

Through the learning of physics, students will acquire problem solving skills which are relevant to their daily life. Determining the best way in which students learn physics takes a priority in physics education. The goal of the present study was to determine the effect of problem based learning strategy on students' problem solving skills and…

Concept Model on Topological Learning

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Kioi, Kazumasa

2010-11-01

We discuss a new model for concept based on topological learning, where the learning process on the neural network is represented by mathematical topology. The topological learning of neural networks is summarized by a quotient of input space and the hierarchical step induces a tree where each node corresponds to a quotient. In general, the concept acquisition is a difficult problem, but the emotion for a subject is represented by providing the questions to a person. Therefore, a kind of concept is captured by such data and the answer sheet can be mapped into a topology consisting of trees. In this paper, we will discuss a way of mapping the emotional concept to a topological learning model.

Does Solving Insight-Based Problems Differ from Solving Learning-Based Problems? Some Evidence from an ERP Study

ERIC Educational Resources Information Center

Leikin, Roza; Waisman, Ilana; Leikin, Mark

2016-01-01

We asked: "What are the similarities and differences in mathematical processing associated with solving learning-based and insight-based problems?" To answer this question, the ERP research procedure was employed with 69 male adolescent subjects who solved specially designed insight-based and learning-based tests. Solutions of…

Mathematical modeling in realistic mathematics education

NASA Astrophysics Data System (ADS)

Riyanto, B.; Zulkardi; Putri, R. I. I.; Darmawijoyo

2017-12-01

The purpose of this paper is to produce Mathematical modelling in Realistics Mathematics Education of Junior High School. This study used development research consisting of 3 stages, namely analysis, design and evaluation. The success criteria of this study were obtained in the form of local instruction theory for school mathematical modelling learning which was valid and practical for students. The data were analyzed using descriptive analysis method as follows: (1) walk through, analysis based on the expert comments in the expert review to get Hypothetical Learning Trajectory for valid mathematical modelling learning; (2) analyzing the results of the review in one to one and small group to gain practicality. Based on the expert validation and students’ opinion and answers, the obtained mathematical modeling problem in Realistics Mathematics Education was valid and practical.

[Problem based learning from the perspective of tutors].

PubMed

Navarro Hernández, Nancy; Illesca P, Mónica; Cabezas G, Mirtha

2009-02-01

Problem based learning is a student centered learning technique that develops deductive, constructive and reasoning capacities among the students. Teachers must adapt to this paradigm of constructing rather than transmitting knowledge. To interpret the importance of tutors in problem based learning during a module of Health research and management given to medical, nursing, physical therapy, midwifery, technology and nutrition students. Eight teachers that participated in a module using problem based learning accepted to participate in an in depth interview. The qualitative analysis of the textual information recorded, was performed using the ATLAS software. We identified 662 meaning units, grouped in 29 descriptive categories, with eight emerging meta categories. The sequential and cross-generated qualitative analysis generated four domains: competence among students, competence of teachers, student-centered learning and evaluation process. Multiprofessional problem based learning contributes to the development of generic competences among future health professionals, such as multidisciplinary work, critical capacity and social skills. Teachers must shelter the students in the context of their problems and social situation.

Predicting Correctness of Problem Solving from Low-Level Log Data in Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Hord, Casey

2009-01-01

This paper proposes a learning based method that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system. Only log files that record students' actions with the system are used to train the model, therefore the modeling process doesn't require expert knowledge for identifying…

Deep and Surface Learning in Problem-Based Learning: A Review of the Literature

ERIC Educational Resources Information Center

Dolmans, Diana H. J. M.; Loyens, Sofie M. M.; Marcq, Hélène; Gijbels, David

2016-01-01

In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review…

Ask Not Only "What Can Problem-Based Learning Do for Psychology?" but "What Can Psychology Do for Problem-Based Learning?" A Review of the Relevance of Problem-Based Learning for Psychology Teaching and Research

ERIC Educational Resources Information Center

Wiggins, Sally; Chiriac, Eva Hammar; Abbad, Gunvor Larsson; Pauli, Regina; Worrell, Marcia

2016-01-01

Problem-based learning (PBL) is an internationally recognised pedagogical approach that is implemented within a number of disciplines. The relevance and uptake of PBL in psychology has to date, however, received very limited attention. The aim of this paper is therefore to review published accounts of how PBL is being used to deliver psychology…

Voice based gender classification using machine learning

NASA Astrophysics Data System (ADS)

Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

2017-11-01

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

Design and Evaluation of a Problem-Based Learning Environment for Teacher Training

ERIC Educational Resources Information Center

Hemker, Laura; Prescher, Claudia; Narciss, Susanne

2017-01-01

Problem-based learning can have a great impact on the acquisition of practical knowledge, which is a central learning aim in the field of teacher education. Therefore, we implemented a problem-based learning approach in four seminars on educational assessment. In this paper, we outline our didactic design and discuss the results of the first…

Students using visual thinking to learn science in a Web-based environment

NASA Astrophysics Data System (ADS)

Plough, Jean Margaret

United States students' science test scores are low, especially in problem solving, and traditional science instruction could be improved. Consequently, visual thinking, constructing science structures, and problem solving in a web-based environment may be valuable strategies for improving science learning. This ethnographic study examined the science learning of fifteen fourth grade students in an after school computer club involving diverse students at an inner city school. The investigation was done from the perspective of the students, and it described the processes of visual thinking, web page construction, and problem solving in a web-based environment. The study utilized informal group interviews, field notes, Visual Learning Logs, and student web pages, and incorporated a Standards-Based Rubric which evaluated students' performance on eight science and technology standards. The Visual Learning Logs were drawings done on the computer to represent science concepts related to the Food Chain. Students used the internet to search for information on a plant or animal of their choice. Next, students used this internet information, with the information from their Visual Learning Logs, to make web pages on their plant or animal. Later, students linked their web pages to form Science Structures. Finally, students linked their Science Structures with the structures of other students, and used these linked structures as models for solving problems. Further, during informal group interviews, students answered questions about visual thinking, problem solving, and science concepts. The results of this study showed clearly that (1) making visual representations helped students understand science knowledge, (2) making links between web pages helped students construct Science Knowledge Structures, and (3) students themselves said that visual thinking helped them learn science. In addition, this study found that when using Visual Learning Logs, the main overall ideas of the science concepts were usually represented accurately. Further, looking for information on the internet may cause new problems in learning. Likewise, being absent, starting late, and/or dropping out all may negatively influence students' proficiency on the standards. Finally, the way Science Structures are constructed and linked may provide insights into the way individual students think and process information.

Improving Mathematical Problem-Solving Ability and Self-Confidence of High School Students through Contextual Learning Model

ERIC Educational Resources Information Center

Surya, Edy; Putri, Feria Andriana; Mukhtar

2017-01-01

The purposes of this study are: (1) to know if students' mathematical problem-solving ability taught by contextual learning model is higher than students taught by expository learning, (2) to know if students' self-confidence taught by contextual learning model is higher than students taught by expository learning, (3) to know if there is…

Facilitating Facilitators: Enhancing PBL through a Structured Facilitator Development Program

ERIC Educational Resources Information Center

Salinitri, Francine D.; Wilhelm, Sheila M.; Crabtree, Brian L.

2015-01-01

With increasing adoption of the problem-based learning (PBL) model, creative approaches to enhancing facilitator training and optimizing resources to maintain effective learning in small groups is essential. We describe a theoretical framework for the development of a PBL facilitator training program that uses the constructivist approach as the…

Constructivist Learning of Anatomy: Gaining Knowledge by Creating Anatomical Casts

ERIC Educational Resources Information Center

Hermiz, David J.; O'Sullivan, Daniel J.; Lujan, Heidi L.; DiCarlo, Stephen E.

2011-01-01

Educators are encouraged to provide inquiry-based, collaborative, and problem solving activities that enhance learning and promote curiosity, skepticism, objectivity, and the use of scientific reasoning. Making anatomical casts or models by injecting solidifying substances into organs is an example of a constructivist activity for achieving these…

The Activity Summary Board

ERIC Educational Resources Information Center

Touitou, Israel; Barry, Stephen; Bielik, Tom; Schneider, Barbara; Krajcik, Joseph

2018-01-01

Project-based learning (PBL) is an instructional approach to science teaching that supports the "Next Generation Science Standards" (Krajcik 2015; NGSS Lead States 2013). In a PBL lesson, students design and solve real-world problems or explain scientific phenomena. Students using a PBL model learn and retain more than those not using…

Anomaly detection for medical images based on a one-class classification

NASA Astrophysics Data System (ADS)

Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

2018-02-01

Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.

Student Experiences of Problem-Based Learning in Pharmacy: Conceptions of Learning, Approaches to Learning and the Integration of Face-to-Face and On-Line Activities

ERIC Educational Resources Information Center

Ellis, Robert A.; Goodyear, Peter; Brillant, Martha; Prosser, Michael

2008-01-01

This study investigates fourth-year pharmacy students' experiences of problem-based learning (PBL). It adopts a phenomenographic approach to the evaluation of problem-based learning, to shed light on the ways in which different groups of students conceive of, and approach, PBL. The study focuses on the way students approach solving problem…

Learning to use working memory: a reinforcement learning gating model of rule acquisition in rats

PubMed Central

Lloyd, Kevin; Becker, Nadine; Jones, Matthew W.; Bogacz, Rafal

2012-01-01

Learning to form appropriate, task-relevant working memory representations is a complex process central to cognition. Gating models frame working memory as a collection of past observations and use reinforcement learning (RL) to solve the problem of when to update these observations. Investigation of how gating models relate to brain and behavior remains, however, at an early stage. The current study sought to explore the ability of simple RL gating models to replicate rule learning behavior in rats. Rats were trained in a maze-based spatial learning task that required animals to make trial-by-trial choices contingent upon their previous experience. Using an abstract version of this task, we tested the ability of two gating algorithms, one based on the Actor-Critic and the other on the State-Action-Reward-State-Action (SARSA) algorithm, to generate behavior consistent with the rats'. Both models produced rule-acquisition behavior consistent with the experimental data, though only the SARSA gating model mirrored faster learning following rule reversal. We also found that both gating models learned multiple strategies in solving the initial task, a property which highlights the multi-agent nature of such models and which is of importance in considering the neural basis of individual differences in behavior. PMID:23115551

The effect of mathematical model development on the instruction of acceleration to introductory physics students

NASA Astrophysics Data System (ADS)

Sauer, Tim Allen

The purpose of this study was to evaluate the effectiveness of utilizing student constructed theoretical math models when teaching acceleration to high school introductory physics students. The goal of the study was for the students to be able to utilize mathematical modeling strategies to improve their problem solving skills, as well as their standardized scientific and conceptual understanding. This study was based on mathematical modeling research, conceptual change research and constructivist theory of learning, all of which suggest that mathematical modeling is an effective way to influence students' conceptual connectiveness and sense making of formulaic equations and problem solving. A total of 48 students in two sections of high school introductory physics classes received constructivist, inquiry-based, cooperative learning, and conceptual change-oriented instruction. The difference in the instruction for the 24 students in the mathematical modeling treatment group was that they constructed every formula they needed to solve problems from data they collected. In contrast, the instructional design for the control group of 24 students allowed the same instruction with assigned problems solved with formulas given to them without explanation. The results indicated that the mathematical modeling students were able to solve less familiar and more complicated problems with greater confidence and mental flexibility than the control group students. The mathematical modeling group maintained fewer alternative conceptions consistently in the interviews than did the control group. The implications for acceleration instruction from these results were discussed.

A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives.

PubMed

Li, Cai; Lowe, Robert; Ziemke, Tom

2014-01-01

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "reshaping" function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal "reshaping" functions). In this article, we use this architecture with the actor-critic algorithms for finding a good "reshaping" function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.

A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives

PubMed Central

Li, Cai; Lowe, Robert; Ziemke, Tom

2014-01-01

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a “reshaping” function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal “reshaping” functions). In this article, we use this architecture with the actor-critic algorithms for finding a good “reshaping” function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion. PMID:25324773

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Modeling the Problem-Based Learning Preferences of McMaster University Undergraduate Medical Students Using a Discrete Choice Conjoint Experiment

ERIC Educational Resources Information Center

Cunningham, Charles E.; Deal, Ken; Neville, Alan; Rimas, Heather; Lohfeld, Lynne

2006-01-01

Objectives: To use methods from the field of marketing research to involve students in the redesign of McMaster University's small group, problem-based undergraduate medical education program. Methods: We used themes from a focus group conducted in an electronic decision support lab to compose 14 four-level educational attributes. Undergraduate…

Problem Solving-based Learning Materials on Fraction for Training Creativity of Elementary School Students

NASA Astrophysics Data System (ADS)

Widhitama, Y. N.; Lukito, A.; Khabibah, S.

2018-01-01

The aim of this research is to develop problem solving based learning materials on fraction for training creativity of elementary school students. Curriculum 2006 states that mathematics should be studied by all learners starting from elementary level in order for them mastering thinking skills, one of them is creative thinking. To our current knowledge, there is no such a research topic being done. To promote this direction, we initiate by developing learning materials with problem solving approach. The developed materials include Lesson Plan, Student Activity Sheet, Mathematical Creativity Test, and Achievement Test. We implemented a slightly modified 4-D model by Thiagajan et al. (1974) consisting of Define, Design, Development, and Disseminate. Techniques of gathering data include observation, test, and questionnaire. We applied three good qualities for the resulted materials; that is, validity, practicality, and effectiveness. The results show that the four mentioned materials meet the corresponding criteria of good quality product.

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

An exploration of tutors' experiences of facilitating problem-based learning. Part 2--implications for the facilitation of problem based learning.

PubMed

Haith-Cooper, Melanie

2003-01-01

This paper is the second of two parts exploring a study that was undertaken to investigate the role of the tutor in facilitating problem-based learning (PBL). The first part focussed on the methodological underpinnings of the study. This paper aims to focus on the findings of the study and their implications for the facilitation of PBL. Six essential themes emerged from the findings that described the facilitation role. The tutors believed that their facilitation role was essentially structured around the decision of when to intervene and how to intervene in the PBL process. Modelling and non-verbal communication were seen as essential strategies for the facilitator. Underpinning these decisions was the need to trust in the philosophy of PBL. However, within many of the themes, there was a divergence of opinion as to how the role should actually be undertaken. Despite this, these findings have implications for the future role of PBL facilitators in Health Professional Education.

Problem Based Learning (PBL) - An Effective Approach to Improve Learning Outcomes in Medical Teaching.

PubMed

Preeti, Bajaj; Ashish, Ahuja; Shriram, Gosavi

2013-12-01

As the "Science of Medicine" is getting advanced day-by-day, need for better pedagogies & learning techniques are imperative. Problem Based Learning (PBL) is an effective way of delivering medical education in a coherent, integrated & focused manner. It has several advantages over conventional and age-old teaching methods of routine. It is based on principles of adult learning theory, including student's motivation, encouragement to set goals, think critically about decision making in day-to-day operations. Above all these, it stimulates challenge acceptance and learning curiosity among students and creates pragmatic educational program. To measure the effectiveness of the "Problem Based Learning" as compared to conventional theory/didactic lectures based learning. The study was conducted on 72 medical students from Dayanand Medical College & Hospital, Ludhiana. Two modules of problem based sessions designed and delivered. Pre & Post-test score's scientific statistical analysis was done. Student feed-back received based on questionnaire in the five-point Likert scale format. Significant improvement in overall performance observed. Feedback revealed majority agreement that "Problem-based learning" helped them create interest (88.8 %), better understanding (86%) & promotes self-directed subject learning (91.6 %). Substantial improvement in the post-test scores clearly reveals acceptance of PBL over conventional learning. PBL ensures better practical learning, ability to create interest, subject understanding. It is a modern-day educational strategy, an effective tool to objectively improve the knowledge acquisition in Medical Teaching.

The Effects of a Problem Based Learning Approach on Students' Attitude Levels: A Meta-Analysis

ERIC Educational Resources Information Center

Batdi, Veli

2014-01-01

This research aimed to examine the effect of a problem-based learning approach in comparison to traditional learning approaches. In this context, the question "What is the effect size of problem-based learning on students' attitudes?" was tried to be answered. Among 190 studies made in national and international field between the…

Mathematical Problem Solving Ability of Junior High School Students through Ang’s Framework for Mathematical Modelling Instruction

NASA Astrophysics Data System (ADS)

Fasni, N.; Turmudi, T.; Kusnandi, K.

2017-09-01

This research background of this research is the importance of student problem solving abilities. The purpose of this study is to find out whether there are differences in the ability to solve mathematical problems between students who have learned mathematics using Ang’s Framework for Mathematical Modelling Instruction (AFFMMI) and students who have learned using scientific approach (SA). The method used in this research is a quasi-experimental method with pretest-postest control group design. Data analysis of mathematical problem solving ability using Indepent Sample Test. The results showed that there was a difference in the ability to solve mathematical problems between students who received learning with Ang’s Framework for Mathematical Modelling Instruction and students who received learning with a scientific approach. AFFMMI focuses on mathematical modeling. This modeling allows students to solve problems. The use of AFFMMI is able to improve the solving ability.

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…

Facilitating Problem Framing in Project-Based Learning

ERIC Educational Resources Information Center

Svihla, Vanessa; Reeve, Richard

2016-01-01

While problem solving is a relatively well understood process, problem framing is less well understood, particularly with regard to supporting students to learn as they frame problems. Project-based learning classrooms are an ideal setting to investigate how teachers facilitate this process. Using participant observation, this study investigated…

Implementation and Refinement of a Problem-based Learning Model: A Ten-Year Experience

PubMed Central

Crabtree, Brian L.; Theilman, Gary D.; Ross, Brendan S.; Cleary, John D.; Byrd, H. Joseph

2007-01-01

Objectives To evaluate the effectiveness of a problem-based learning (PBL) model implemented in 1995 at the University of Mississippi School of Pharmacy. Design The third-professional (P3) year curriculum was reoriented from a faculty-centered model of teaching to a student-centered model of learning. Didactic lectures and structured classroom time were diminished. Small student groups were organized and a faculty facilitator monitored each group's discussions and provided individual student assessments. At the end of each 8-week block, students were assessed on group participation, disease and drug content knowledge, and problem-solving abilities. Faculty and student input was solicited at the end of each year to aid programmatic improvement. In 2000, a formal 5-year review of the PBL program was conducted. Assessment Recommendations for improvement included clarifying course objectives, adopting a peer-review process for examination materials, refining the group assessment instruments, and providing an opportunity for student remediation after a course was failed. A weekly case conference presided over by a faculty content expert was also recommended. Ongoing critical evaluation during the following 5-year period was provided by graduates of the program, faculty participants, and accreditation reviews. Conclusion Over our 10-year experience with a PBL model of P3 education, we found that although the initial challenges of increased demands on personnel and teaching space were easily overcome, student acceptance of the program depended on their acknowledgment of the practical benefits of active learning and on the value afforded their input on curricular development. PMID:17429517

Learning, Attention/Hyperactivity, and Conduct Problems as Sequelae of Excessive Daytime Sleepiness in a General Population Study of Young Children

PubMed Central

Calhoun, Susan L.; Fernandez-Mendoza, Julio; Vgontzas, Alexandros N.; Mayes, Susan D.; Tsaoussoglou, Marina; Rodriguez-Muñoz, Alfredo; Bixler, Edward O.

2012-01-01

Study Objectives: Although excessive daytime sleepiness (EDS) is a common problem in children, with estimates of 15%; few studies have investigated the sequelae of EDS in young children. We investigated the association of EDS with objective neurocognitive measures and parent reported learning, attention/hyperactivity, and conduct problems in a large general population sample of children. Design: Cross-sectional. Setting: Population based. Participants: 508 children from The Penn State Child Cohort. Interventions: N/A. Measurements and Results: Children underwent a 9-h polysomnogram, comprehensive neurocognitive testing, and parent rating scales. Children were divided into 2 groups: those with and without parent-reported EDS. Structural equation modeling was used to examine whether processing speed and working memory performance would mediate the relationship between EDS and learning, attention/hyperactivity, and conduct problems. Logistic regression models suggest that parent-reported learning, attention/hyperactivity, and conduct problems, as well as objective measurement of processing speed and working memory are significant sequelae of EDS, even when controlling for AHI and objective markers of sleep. Path analysis demonstrates that processing speed and working memory performance are strong mediators of the association of EDS with learning and attention/hyperactivity problems, while to a slightly lesser degree are mediators from EDS to conduct problems. Conclusions: This study suggests that in a large general population sample of young children, parent-reported EDS is associated with neurobehavioral (learning, attention/hyperactivity, conduct) problems and poorer performance in processing speed and working memory. Impairment due to EDS in daytime cognitive and behavioral functioning can have a significant impact on children's development. Citation: Calhoun SL; Fernandez-Mendoza J; Vgontzas AN; Mayes SD; Tsaoussoglou M; Rodriguez-Muñoz A; Bixler EO. Learning, attention/hyperactivity, and conduct problems as sequelae of excessive daytime sleepiness in a general population study of young children. SLEEP 2012;35(5):627-632. PMID:22547888

Development and Design of Problem Based Learning Game-Based Courseware

ERIC Educational Resources Information Center

Chang, Chiung-Sui; Chen, Jui-Fa; Chen, Fei-Ling

2015-01-01

In an educational environment, instructors would always think of ways to provide students with motivational learning materials and efficient learning strategies. Hence, many researchers have proposed that students' problem-solving ability enhances their learning. Problem-solving ability plays an important role for users in dealing with problems…

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

An evolutionary morphological approach for software development cost estimation.

PubMed

Araújo, Ricardo de A; Oliveira, Adriano L I; Soares, Sergio; Meira, Silvio

2012-08-01

In this work we present an evolutionary morphological approach to solve the software development cost estimation (SDCE) problem. The proposed approach consists of a hybrid artificial neuron based on framework of mathematical morphology (MM) with algebraic foundations in the complete lattice theory (CLT), referred to as dilation-erosion perceptron (DEP). Also, we present an evolutionary learning process, called DEP(MGA), using a modified genetic algorithm (MGA) to design the DEP model, because a drawback arises from the gradient estimation of morphological operators in the classical learning process of the DEP, since they are not differentiable in the usual way. Furthermore, an experimental analysis is conducted with the proposed model using five complex SDCE problems and three well-known performance metrics, demonstrating good performance of the DEP model to solve SDCE problems. Copyright © 2012 Elsevier Ltd. All rights reserved.

Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale

NASA Astrophysics Data System (ADS)

Marçais, J.; Gupta, H. V.; De Dreuzy, J. R.; Troch, P. A. A.

2016-12-01

Geomorphological structure and geological heterogeneity of hillslopes are major controls on runoff responses. The diversity of hillslopes (morphological shapes and geological structures) on one hand, and the highly non linear runoff mechanism response on the other hand, make it difficult to transpose what has been learnt at one specific hillslope to another. Therefore, making reliable predictions on runoff appearance or river flow for a given hillslope is a challenge. Applying a classic model calibration (based on inverse problems technique) requires doing it for each specific hillslope and having some data available for calibration. When applied to thousands of cases it cannot always be promoted. Here we propose a novel modeling framework based on coupling process based models with data based approach. First we develop a mechanistic model, based on hillslope storage Boussinesq equations (Troch et al. 2003), able to model non linear runoff responses to rainfall at the hillslope scale. Second we set up a model database, representing thousands of non calibrated simulations. These simulations investigate different hillslope shapes (real ones obtained by analyzing 5m digital elevation model of Brittany and synthetic ones), different hillslope geological structures (i.e. different parametrizations) and different hydrologic forcing terms (i.e. different infiltration chronicles). Then, we use this model library to train a machine learning model on this physically based database. Machine learning model performance is then assessed by a classic validating phase (testing it on new hillslopes and comparing machine learning with mechanistic outputs). Finally we use this machine learning model to learn what are the hillslope properties controlling runoffs. This methodology will be further tested combining synthetic datasets with real ones.

Comparing Problem-Based Learning Students to Students in a Lecture-Based Curriculum: Learning Strategies and the Relation with Self-Study Time

ERIC Educational Resources Information Center

Wijnen, Marit; Loyens, Sofie M. M.; Smeets, Guus; Kroeze, Maarten; van der Molen, Henk

2017-01-01

In educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one's own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational…

A rational model of function learning.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Williams, Joseph J; Kalish, Michael L

2015-10-01

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.

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.

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention are Coordinated Using Surface-Based Attentional Shrouds

ERIC Educational Resources Information Center

Fazl, Arash; Grossberg, Stephen; Mingolla, Ennio

2009-01-01

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified…

Problem-Based Learning Associated by Action-Process-Object-Schema (APOS) Theory to Enhance Students' High Order Mathematical Thinking Ability

ERIC Educational Resources Information Center

Mudrikah, Achmad

2016-01-01

The research has shown a model of learning activities that can be used to stimulate reflective abstraction in students. Reflective abstraction as a method of constructing knowledge in the Action-Process-Object-Schema theory, and is expected to occur when students are in learning activities, will be able to encourage students to make the process of…

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.

Teaching Lean Manufacturing with Simulations and Games: A Survey and Future Directions

ERIC Educational Resources Information Center

Badurdeen, Fazleena; Marksberry, Philip; Hall, Arlie; Gregory, Bob

2010-01-01

Problem-based learning focuses on small groups using authentic problems as a means to help participants obtain knowledge and problem-solving skills. This approach makes problem-based learning ideal for teaching lean manufacturing, which is driven by a culture of problem solving that values learning as one key output of manufacturing production.…

Evidence in the learning organization

PubMed Central

Crites, Gerald E; McNamara, Megan C; Akl, Elie A; Richardson, W Scott; Umscheid, Craig A; Nishikawa, James

2009-01-01

Background Organizational leaders in business and medicine have been experiencing a similar dilemma: how to ensure that their organizational members are adopting work innovations in a timely fashion. Organizational leaders in healthcare have attempted to resolve this dilemma by offering specific solutions, such as evidence-based medicine (EBM), but organizations are still not systematically adopting evidence-based practice innovations as rapidly as expected by policy-makers (the knowing-doing gap problem). Some business leaders have adopted a systems-based perspective, called the learning organization (LO), to address a similar dilemma. Three years ago, the Society of General Internal Medicine's Evidence-based Medicine Task Force began an inquiry to integrate the EBM and LO concepts into one model to address the knowing-doing gap problem. Methods During the model development process, the authors searched several databases for relevant LO frameworks and their related concepts by using a broad search strategy. To identify the key LO frameworks and consolidate them into one model, the authors used consensus-based decision-making and a narrative thematic synthesis guided by several qualitative criteria. The authors subjected the model to external, independent review and improved upon its design with this feedback. Results The authors found seven LO frameworks particularly relevant to evidence-based practice innovations in organizations. The authors describe their interpretations of these frameworks for healthcare organizations, the process they used to integrate the LO frameworks with EBM principles, and the resulting Evidence in the Learning Organization (ELO) model. They also provide a health organization scenario to illustrate ELO concepts in application. Conclusion The authors intend, by sharing the LO frameworks and the ELO model, to help organizations identify their capacities to learn and share knowledge about evidence-based practice innovations. The ELO model will need further validation and improvement through its use in organizational settings and applied health services research. PMID:19323819

Course Design and Student Responses to an Online PBL Course in 3D Modelling for Mining Engineers

ERIC Educational Resources Information Center

McAlpine, Iain; Stothard, Phillip

2005-01-01

To enhance a course in 3D Virtual Reality (3D VR) modelling for mining engineers, and to create the potential for off campus students to fully engage with the course, a problem based learning (PBL) approach was applied to the course design and all materials and learning activities were provided online. This paper outlines some of the theoretical…

Rebecca's in the Dark: A Comparative Study of Problem-Based Learning and Direct Instruction/Experiential Learning in Two 4th-Grade Classrooms

ERIC Educational Resources Information Center

Drake, Kay N.; Long, Deborah

2009-01-01

Seeking improved student performance in elementary schools has led educators to advocate inquiry-based teaching approaches, including problem-based learning (PBL). In PBL, students simultaneously develop problem-solving strategies, disciplinary knowledge bases, collaborative skills, and dispositions. Research into the efficacy of PBL in elementary…

An Electronic Library-Based Learning Environment for Supporting Web-Based Problem-Solving Activities

ERIC Educational Resources Information Center

Tsai, Pei-Shan; Hwang, Gwo-Jen; Tsai, Chin-Chung; Hung, Chun-Ming; Huang, Iwen

2012-01-01

This study aims to develop an electronic library-based learning environment to support teachers in developing web-based problem-solving activities and analyzing the online problem-solving behaviors of students. Two experiments were performed in this study. In study 1, an experiment on 103 elementary and high school teachers (the learning activity…

Problem-posing in education: transformation of the practice of the health professional.

PubMed

Casagrande, L D; Caron-Ruffino, M; Rodrigues, R A; Vendrúsculo, D M; Takayanagui, A M; Zago, M M; Mendes, M D

1998-02-01

This study was developed by a group of professionals from different areas (nurses and educators) concerned with health education. It proposes the use of a problem-posing model for the transformation of professional practice. The concept and functions of the model and their relationships with the educative practice of health professionals are discussed. The model of problem-posing education is presented (compared to traditional, "banking" education), and four innovative experiences of teaching-learning are reported based on this model. These experiences, carried out in areas of environmental and occupational health and patient education have shown the applicability of the problem-posing model to the practice of the health professional, allowing transformation.

A Cognitive Apprenticeship Approach to Facilitating Web-Based Collaborative Problem Solving

ERIC Educational Resources Information Center

Kuo, Fan-Ray; Hwang, Gwo-Jen; Chen, Szu-Chuang; Chen, Sherry Y.

2012-01-01

Enhancing students' problem-solving abilities has been recognized as an important and challenging issue for technology-enhanced learning. Thus, previous research has attempted to address this issue by developing various mechanisms, among which a cognitive apprenticeship model can particularly enhance students' abilities. However, it is not clear…

Examining Mathematics Classroom Interactions: Elevating Student Roles in Teaching and Learning

ERIC Educational Resources Information Center

Kent, Laura

2017-01-01

This article introduces a model entitled, "Responsive Teaching through Problem Posing" or RTPP, that addresses a type of reform oriented mathematics teaching based on posing relevant problems, positioning students as experts of mathematics, and facilitating discourse. RTPP incorporates decades of research on students' thinking in…

An exploratory study into students' conceptual understanding of acid/base principles associated with chemical buffer systems

NASA Astrophysics Data System (ADS)

MacGowan, Catherine Elizabeth

The overall objective of this research project was to provide an insight into students' conceptual understanding of acid/base principles as it relates to the comprehension and correct application of scientific concepts during a problem-solving activity. The difficulties experienced learning science and in developing appropriate problem-solving strategies most likely are predetermined by students' existing conceptual and procedural knowledge constructs; with the assimilation of newly acquired knowledge hindering or aiding the learning process. Learning chemistry requires a restructuring of content knowledge which will allow the individual to assemble and to integrate his/her own perception of science with instructional knowledge. The epistemology of constructivism, the theoretical grounding for this research project, recognizes the student's role as an active participant in the learning process. The study's design was exploratory in nature and descriptive in design. The problem-solving activity, the preparation of a chemical buffer solution at pH of 9, was selected and modified to reflect and meet the study's objective. Qualitative research methods (i.e., think aloud protocols, retrospective interviews, survey questionnaires such as the Scale of Intellectual Development (SID), and archival data sources) were used in the collection and assessment of data. Given its constructivist grounding, simplicity, and interpretative view of knowledge acquisition and learning of collegiate aged individuals, the Perry Intellectual and Ethical Development Model (1970) was chosen as the applied model for evaluation student cognition. The study's participants were twelve traditional college age students from a small, private liberal arts college. All participants volunteered for the project and had completed or were completing a general college chemistry course at the time of the project. Upon analysis of the data the following observations and results were noted: (1) students' overall comprehension level of key acid/base principles was at the misconception/miscued level of understanding; (2) the level of a student's conceptual knowledge effected their problem-solving performance and influenced their use of problem-solving tactics; (3) students casual use of the terms "acid" and/or "base" played a significant role in the misuse and misunderstanding of the principles of acid/base chemistry; (4) as assessed from their think aloud protocols and described by the Perry Scheme positions of intellect the study's participants' overall level of cognition were ranked as dualistic/relativistic thinkers; and (5) the SID questionnaire survey rankings did not seem to assess or reflect the participants' cognitive ability to learn or correctly use acid/base concepts as they preformed the study's problem-solving activity--the preparation of buffer solution having a pH of 9.

Designing Web-Based Science Lesson Plans That Use Problem-Based Learning To Inspire Middle School Kids: KaAMS (Kids as Airborne Mission Scientists).

ERIC Educational Resources Information Center

Koszalka, Tiffany A.; Grabowski, Barbara; Kim, Younghoon

Problem-based learning (PBL) has great potential for inspiring K-12 learning. KaAMS (Kids as Airborne Mission Scientists), an example of PBL, was designed to help teachers inspire middle school students to learning science, math, technology, and geography. The children participate as scientists investigating environmental problems using NASA…

Manifold regularized matrix completion for multi-label learning with ADMM.

PubMed

Liu, Bin; Li, Yingming; Xu, Zenglin

2018-05-01

Multi-label learning is a common machine learning problem arising from numerous real-world applications in diverse fields, e.g, natural language processing, bioinformatics, information retrieval and so on. Among various multi-label learning methods, the matrix completion approach has been regarded as a promising approach to transductive multi-label learning. By constructing a joint matrix comprising the feature matrix and the label matrix, the missing labels of test samples are regarded as missing values of the joint matrix. With the low-rank assumption of the constructed joint matrix, the missing labels can be recovered by minimizing its rank. Despite its success, most matrix completion based approaches ignore the smoothness assumption of unlabeled data, i.e., neighboring instances should also share a similar set of labels. Thus they may under exploit the intrinsic structures of data. In addition, the matrix completion problem can be less efficient. To this end, we propose to efficiently solve the multi-label learning problem as an enhanced matrix completion model with manifold regularization, where the graph Laplacian is used to ensure the label smoothness over it. To speed up the convergence of our model, we develop an efficient iterative algorithm, which solves the resulted nuclear norm minimization problem with the alternating direction method of multipliers (ADMM). Experiments on both synthetic and real-world data have shown the promising results of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

PubMed

Khellal, Atmane; Ma, Hongbin; Fei, Qing

2018-05-09

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

PubMed Central

Abdelsamea, Mohammed M.; Gnecco, Giorgio; Gaber, Mohamed Medhat; Elyan, Eyad

2015-01-01

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses. PMID:25960736

A hybrid learning method for constructing compact rule-based fuzzy models.

PubMed

Zhao, Wanqing; Niu, Qun; Li, Kang; Irwin, George W

2013-12-01

The Takagi–Sugeno–Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature.

Learning from Evidence in a Complex World

PubMed Central

Sterman, John D.

2006-01-01

Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended “side effects.” Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond. PMID:16449579

Developing a Problem-Based Learning Simulation: An Economics Unit on Trade

ERIC Educational Resources Information Center

Maxwell, Nan L.; Mergendoller, John R.; Bellisimo, Yolanda

2004-01-01

This article argues that the merger of simulations and problem-based learning (PBL) can enhance both active-learning strategies. Simulations benefit by using a PBL framework to promote student-directed learning and problem-solving skills to explain a simulated dilemma with multiple solutions. PBL benefits because simulations structure the…

The Challenge of Problem-Based Learning. 2nd Edition.

ERIC Educational Resources Information Center

Boud, David, Ed.; Feletti, Grahame I., Ed.

Problem-based learning is an approach to structuring the curriculum which involves confronting students with problems from practice which provide a stimulus for learning. However, there are many possible forms that a curriculum and process for teaching and learning might take and still be compatible with this definition. This book explores these…

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…

Creating a Complex Measurement Model Using Evidence Centered Design.

ERIC Educational Resources Information Center

Williamson, David M.; Bauer, Malcom; Steinberg, Linda S.; Mislevy, Robert J.; Behrens, John T.

In computer-based simulations meant to support learning, students must bring a wide range of relevant knowledge, skills, and abilities to bear jointly as they solve meaningful problems in a learning domain. To function efficiently as an assessment, a simulation system must also be able to evoke and interpret observable evidence about targeted…

Virtual Cerebral Ventricular System: An MR-Based Three-Dimensional Computer Model

ERIC Educational Resources Information Center

Adams, Christina M.; Wilson, Timothy D.

2011-01-01

The inherent spatial complexity of the human cerebral ventricular system, coupled with its deep position within the brain, poses a problem for conceptualizing its anatomy. Cadaveric dissection, while considered the gold standard of anatomical learning, may be inadequate for learning the anatomy of the cerebral ventricular system; even with…

Searching for Buried Treasure: Uncovering Discovery in Discovery-Based Learning

ERIC Educational Resources Information Center

Chase, Kiera; Abrahamson, Dor

2018-01-01

Forty 4th and 9th grade students participated individually in tutorial interviews centered on a problem-solving activity designed for learning basic algebra mechanics through diagrammatic modeling of an engaging narrative about a buccaneering giant burying and unearthing her treasure on a desert island. Participants were randomly assigned to…

IP Addressing: Problem-Based Learning Approach on Computer Networks

ERIC Educational Resources Information Center

Jevremovic, Aleksandar; Shimic, Goran; Veinovic, Mladen; Ristic, Nenad

2017-01-01

The case study presented in this paper describes the pedagogical aspects and experience gathered while using an e-learning tool named IPA-PBL. Its main purpose is to provide additional motivation for adopting theoretical principles and procedures in a computer networks course. In the proposed model, the sequencing of activities of the learning…

Surveillance in Programming Plagiarism beyond Techniques: An Incentive-Based Fishbone Model

ERIC Educational Resources Information Center

Wang, Yanqing; Chen, Min; Liang, Yaowen; Jiang, Yu

2013-01-01

Lots of researches have showed that plagiarism becomes a severe problem in higher education around the world, especially in programming learning for its essence. Therefore, an effective strategy for plagiarism surveillance in program learning is much essential. Some literature focus on code similarity algorithm and the related tools can help to…

Planning Model of Physics Learning In Senior High School To Develop Problem Solving Creativity Based On National Standard Of Education

NASA Astrophysics Data System (ADS)

Putra, A.; Masril, M.; Yurnetti, Y.

2018-04-01

One of the causes of low achievement of student’s competence in physics learning in high school is the process which they have not been able to develop student’s creativity in problem solving. This is shown that the teacher’s learning plan is not accordance with the National Eduction Standard. This study aims to produce a reconstruction model of physics learning that fullfil the competency standards, content standards, and assessment standards in accordance with applicable curriculum standards. The development process follows: Needs analysis, product design, product development, implementation, and product evaluation. The research process involves 2 peers judgment, 4 experts judgment and two study groups of high school students in Padang. The data obtained, in the form of qualitative and quantitative data that collected through documentation, observation, questionnaires, and tests. The result of this research up to the product development stage that obtained the physics learning plan model that meets the validity of the content and the validity of the construction in terms of the fulfillment of Basic Competence, Content Standards, Process Standards and Assessment Standards.

Andragogical Modeling and the Success of the "EMPACTS" project-based learning model in the STEM disciplines: A decade of growth and learner success in the 2Y College Learning Environment.

NASA Astrophysics Data System (ADS)

Phillips, C. D.; Thomason, R.; Galloway, M.; Sorey, N.; Stidham, L.; Torgerson, M.

2014-12-01

EMPACTS (Educationally Managed Projects Advancing Curriculum, Technology/Teamwork and Service) is a project-based, adult learning modelthat is designed to enhance learning of course content through real-world application and problem solving self directed and collaborative learning use of technology service to the community EMPACTS students are self-directed in their learning, often working in teams to develop, implement, report and present final project results. EMPACTS faculty use community based projects to increase deeper learning of course content through "real-world" service experiences. Learners develop personal and interpersonal work and communication skills as they plan, execute and complete project goals together. Technology is used as a tool to solve problems and to publish the products of their learning experiences. Courses across a broad STEM curriculum integrate the EMPACTS project experience into the overall learning outcomes as part of the learning college mission of preparing 2Y graduates for future academic and/or workforce success. Since the program began in 2005, there have been over 200 completed projects/year. Student driven successes have led to the establishment of an EMPACTS Technology Corp, which is funded through scholarship and allows EMPACTS learners the opportunity to serve and learn from one another as "peer instructors." Engineering and 3D graphic design teams have written technology proposals and received funding for 3D printing replication projects, which have benefited the college as a whole through grant opportunities tied to these small scale successes. EMPACTS students engage in a variety of outreachprojects with area schools as they share the successes and joys of self directed, inquiry, project based learning. The EMPACTS Program has successfully trained faculty and students in the implementation of the model and conduct semester to semester and once a year workshops for college and K-12 faculty, who are interested in enhancing the learning experience and retention of course content through meaningful, engaging, character building projects. Learner Project successes are celebrated and archived within the framework of the EMPACTS Student Project website. http://faculty.nwacc.edu/EAST_original/Spring2014/Spring2014index.htm

Neural networks for learning and prediction with applications to remote sensing and speech perception

NASA Astrophysics Data System (ADS)

Gjaja, Marin N.

1997-11-01

Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An unsupervised neural network model is proposed that embodies two principal hypotheses supported by experimental data--that sensory experience guides language-specific development of an auditory neural map and that a population vector can predict psychological phenomena based on map cell activities. Model simulations show how a nonuniform distribution of map cell firing preferences can develop from language-specific input and give rise to the magnet effect.

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

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

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.

Problem solving strategies used by RN-to-BSN students in an online problem-based learning course.

PubMed

Oldenburg, Nancy L; Hung, Wei-Chen

2010-04-01

It is essential that nursing students develop the problem solving and critical thinking skills required in the current health care environment. Problem-based learning has been promoted as a way to help students acquire those skills; however, gaps exist in the knowledge base of the strategies used by learners. The purpose of this case study was to gain insight into the problem solving experience of a group of six RN-to-BSN students in an online problem-based learning course. Data, including discussion transcripts, reflective papers, and interview transcripts, were analyzed using a qualitative approach. Students expanded their use of resources and resolved the cases, identifying relevant facts and clinical applications. They had difficulty communicating their findings, establishing the credibility of sources, and offering challenging feedback. Increased support and direction are needed to facilitate the development of problem solving abilities of students in the problem-based learning environment.

Modeling Zombie Outbreaks: A Problem-Based Approach to Improving Mathematics One Brain at a Time

ERIC Educational Resources Information Center

Lewis, Matthew; Powell, James A.

2016-01-01

A great deal of educational literature has focused on problem-based learning (PBL) in mathematics at the primary and secondary level, but arguably there is an even greater need for PBL in college math courses. We present a project centered around the Humans versus Zombies moderated tag game played on the Utah State University campus. We discuss…

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

Comparison of Example-Based Learning and Problem-Based Learning in Engineering Domain

ERIC Educational Resources Information Center

Sern, Lai Chee; Salleh, Kahirol Mohd; Sulaiman, Nor lisa; Mohamad, Mimi Mohaffyza; Yunos, Jailani Md

2015-01-01

The research was conducted to compare the impacts of problem-based learning (PBL) and example-based learning (EBL) on the learning performance in an engineering domain. The research was implemented by means of experimental design. Specifically, a two-group experiment with a pre- and post-test design was used in this research. A total of 37…

Team-Based Learning Practices and Principles in Comparison with Cooperative Learning and Problem-Based Learning

ERIC Educational Resources Information Center

Michaelsen, Larry K.; Davidson, Neil; Major, Claire Howell

2014-01-01

The authors address three questions: (1) What are the foundational practices of team-based learning (TBL)? (2) What are the fundamental principles underlying TBL's foundational practices? and (3) In what ways are TBL's foundational practices similar to and/or different from the practices employed by problem-based learning (PBL) and…

Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking.

PubMed

Khoiriyah, Umatul; Roberts, Chris; Jorm, Christine; Van der Vleuten, C P M

2015-08-26

Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL tutorial. Self-assessment is a central component of the self-regulation of student learning behaviours. There are few measures to investigate self-assessment relevant to PBL processes. We developed a Self-assessment Scale on Active Learning and Critical Thinking (SSACT) to address this gap. We wished to demonstrated evidence of its validity in the context of PBL by exploring its internal structure. We used a mixed methods approach to scale development. We developed scale items from a qualitative investigation, literature review, and consideration of previous existing tools used for study of the PBL process. Expert review panels evaluated its content; a process of validation subsequently reduced the pool of items. We used structural equation modelling to undertake a confirmatory factor analysis (CFA) of the SSACT and coefficient alpha. The 14 item SSACT consisted of two domains "active learning" and "critical thinking." The factorial validity of SSACT was evidenced by all items loading significantly on their expected factors, a good model fit for the data, and good stability across two independent samples. Each subscale had good internal reliability (>0.8) and strongly correlated with each other. The SSACT has sufficient evidence of its validity to support its use in the PBL process to encourage students to self-assess. The implementation of the SSACT may assist students to improve the quality of their learning in achieving PBL goals such as critical thinking and self-directed learning.

Problem-Based Learning in the Physical Science Classroom, K-12

ERIC Educational Resources Information Center

McConnell, Tom J.; Parker, Joyce; Eberhardt, Janet

2018-01-01

"Problem-Based Learning in the Physical Science Classroom, K-12" will help your students truly understand concepts such as motion, energy, and magnetism in true-to-life contexts. The book offers a comprehensive description of why, how, and when to implement problem-based learning (PBL) in your curriculum. Its 14 developmentally…

Hybrid Problem-Based Learning in Digital Image Processing: A Case Study

ERIC Educational Resources Information Center

Tan, Songxin; Shen, Zixing

2018-01-01

Contribution: This paper reports a curriculum development in hybrid problem-based learning (h-PBL), addresses the design, implementation, effectiveness, and assessment issues of h-PBL, and explains the mixed results observed regarding the impact of problem-based learning (PBL) on student grades from a hybrid perspective. Background: The effect of…

The Effect of Teaching Model ‘Learning Cycles 5E’ toward Students’ Achievement in Learning Mathematic at X Years Class SMA Negeri 1 Banuhampu 2013/2014 Academic Year

NASA Astrophysics Data System (ADS)

Yeni, N.; Suryabayu, E. P.; Handayani, T.

2017-02-01

Based on the survey showed that mathematics teacher still dominated in teaching and learning process. The process of learning is centered on the teacher while the students only work based on instructions provided by the teacher without any creativity and activities that stimulate students to explore their potential. Realized the problem above the writer interested in finding the solution by applying teaching model ‘Learning Cycles 5E’. The purpose of his research is to know whether teaching model ‘Learning Cycles 5E’ is better than conventional teaching in teaching mathematic. The type of the research is quasi experiment by Randomized Control test Group Only Design. The population in this research were all X years class students. The sample is chosen randomly after doing normality, homogeneity test and average level of students’ achievement. As the sample of this research was X.7’s class as experiment class used teaching model learning cycles 5E and X.8’s class as control class used conventional teaching. The result showed us that the students achievement in the class that used teaching model ‘Learning Cycles 5E’ is better than the class which did not use the model.

Solving problems with group work in problem-based learning: hold on to the philosophy.

PubMed

Dolmans, D H; Wolfhagen, I H; van der Vleuten, C P; Wijnen, W H

2001-09-01

Problem-based learning (PBL) has gained a foothold within many schools in higher education as a response to the problems faced within traditional education. Working with PBL tutorial groups is assumed to have positive effects on student learning. Several studies provide empirical evidence that PBL stimulates cognitive effects and leads to restructuring of knowledge and enhanced intrinsic interest in the subject matter. However, staff members do not always experience the positive effects of group work which they had hoped for. When confronted with problems in group work, such as students who only maintain an appearance of being actively involved and students who let others do the work, teachers all too often implement solutions which can be characterized as teacher- directed rather than student-directed. Teachers tend to choose solutions which are familiar from their own experience during professional training, i.e. using the teacher-directed model. These solutions are not effective in improving group work and the negative experiences persist. It is argued that teachers should hold on to the underlying educational philosophy when solving problems arising from group work in PBL, by choosing actions which are consistent with the student-directed view of education in PBL.

Dynamic Assessment of Algebraic Learning in Predicting Third Graders’ Development of Mathematical Problem Solving

PubMed Central

Fuchs, Lynn S.; Compton, Donald L.; Fuchs, Douglas; Hollenbeck, Kurstin N.; Craddock, Caitlin F.; Hamlett, Carol L.

2008-01-01

Dynamic assessment (DA) involves helping students learn a task and indexing responsiveness to that instruction as a measure of learning potential. The purpose of this study was to explore the utility of a DA of algebraic learning in predicting 3rd graders’ development of mathematics problem solving. In the fall, 122 3rd-grade students were assessed on language, nonverbal reasoning, attentive behavior, calculations, word-problem skill, and DA. On the basis of random assignment, students received 16 weeks of validated instruction on word problems or received 16 weeks of conventional instruction on word problems. Then, students were assessed on word-problem measures proximal and distal to instruction. Structural equation measurement models showed that DA measured a distinct dimension of pretreatment ability and that proximal and distal word-problem measures were needed to account for outcome. Structural equation modeling showed that instruction (conventional vs. validated) was sufficient to account for math word-problem outcome proximal to instruction; by contrast, language, pretreatment math skill, and DA were needed to forecast learning on word-problem outcomes more distal to instruction. Findings are discussed in terms of responsiveness-to-intervention models for preventing and identifying learning disabilities. PMID:19884957

Student Engagement in a Structured Problem-Based Approach to Learning: A First-Year Electronic Engineering Study Module on Heat Transfer

ERIC Educational Resources Information Center

Montero, E.; Gonzalez, M. J.

2009-01-01

Problem-based learning has been at the core of significant developments in engineering education in recent years. This term refers to any learning environment in which the problem drives the learning, because it is posed in such a way that students realize they need to acquire new knowledge before the problem can be solved. This paper presents the…

Factors influencing a problem-based learning implementation: A case study of IT courses

NASA Astrophysics Data System (ADS)

Darus, Norida Muhd; Mohd, Haslina; Baharom, Fauziah; Saip, Mohamed Ali; Puteh, Nurnasran; Marzuki @ Matt, Zaharin; Husain, Mohd Zabidin; Yasin, Azman

2016-08-01

IT students must be trained to work efficiently as teamwork. One of the techniques that can be used to train them is through Problem-Based Learning (PBL) approach. The PBL implementation can be influenced by various factors depending on the ultimate goal of the study. This study is focusing on the IT students' perception of the PBL implementation. The student's perception is important to ensure the successfulness of the PBL implementation. Therefore, it is important to identify the factors that might influence the implementation of PBL of IT courses. This study aims to identify some catalyst factors that may influence the PBL implementation of IT courses. The study involved three (3) main phases: identifying PBL implementation factors, constructing a PBL model, and PBL model validation using statistical analysis. Four main factors are identified: PBL Characteristics, PBL Course Assessment, PBL Practices, and PBL Perception. Based on these four factors, a PBL model is constructed. Then, based on the proposed PBL model, four hypotheses are formulated and analyzed to validate the model. All hypotheses are significantly acceptable. The result shows that the PBL Characteristics and PBL Course Assessment factors are significantly influenced the PBL Practices and indirectly influenced the Students' Perception of the PBL Implementation for IT courses. This PBL model can assist decision makers in enhancing the PBL teaching and learning strategy for IT courses. It is also can be tested to other courses in the future.

Deep learning based syndrome diagnosis of chronic gastritis.

PubMed

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

PubMed Central

Liu, Guo-Ping; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

[Problem-based learning, a strategy to employ it].

PubMed

Guillamet Lloveras, Ana; Celma Vicente, Matilde; González Carrión, Pilar; Cano-Caballero Gálvez, Ma Dolores; Pérez Ramírez, Francisca

2009-02-01

The Virgen de las Nieves University School of Nursing has adopted the methodology of Problem-Based Learning (ABP in Spanish acronym) as a supplementary method to gain specific transversal competencies. In so doing, all basic required/obligatory subjects necessary for a degree have been partially affected. With the objective of identifying and administering all the structural and cultural barriers which could impede the success or effectiveness of its adoption, a strategic analysis at the School was carried out. This technique was based on a) knowing the strong and weak points the School has for adopting the Problem-Based Learning methodology; b) describing the structural problems and necessities to carry out this teaching innovation; c) to discover the needs professors have regarding knowledge and skills related to Problem-Based Learning; d) to prepare students by informing them about the characteristics of Problem-Based Learning; e) to evaluate the results obtained by means of professor and student opinions, f) to adopt the improvements identified. The stages followed were: strategic analysis, preparation, pilot program, adoption and evaluation.

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

PubMed

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game.

PubMed

Zhong, Xiangnan; He, Haibo; Wang, Ding; Ni, Zhen

2018-05-01

In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively. The explicit updating rules for these three neural networks are provided based on the data generated during the online learning along the system trajectories. The stability analysis in terms of the neural network approximation errors is discussed based on the Lyapunov approach. Finally, two simulation examples are provided to show the effectiveness of the proposed method.

Teachers' Temporary Support and Worked-Out Examples as Elements of Scaffolding in Mathematical Modeling

ERIC Educational Resources Information Center

Tropper, Natalie; Leiss, Dominik; Hänze, Martin

2015-01-01

Empirical findings show that students have manifold difficulties when dealing with mathematical modeling problems. Accordingly, approaches for supporting students in modeling-based learning environments have to be investigated. In the research presented here, we adopted a scaffolding perspective on teaching modeling with the aim of both providing…

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

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

The Effectiveness of Problem-Based Learning on Teaching the First Law of Thermodynamics

ERIC Educational Resources Information Center

Tatar, Erdal; Oktay, Munir

2011-01-01

Background: Problem-based learning (PBL) is a teaching approach working in cooperation with self-learning and involving research to solve real problems. The first law of thermodynamics states that energy can neither be created nor destroyed, but that energy is conserved. Students had difficulty learning or misconceptions about this law. This study…

Evaluation of Health Profession Student Attitudes toward an Online Nutrition Education Problem-Based Learning Module

ERIC Educational Resources Information Center

Gould, Kathleen; Sadera, William

2015-01-01

The intent of problem-based learning (PBL) is to increase student motivation to learn, to promote critical thinking and to teach students to learn with complexity. PBL encourages students to understand that there are no straightforward answers and that problem solutions depend on context. This paper discusses the experience of undergraduate health…

Redesigning Problem-Based Learning in the Knowledge Creation Paradigm for School Science Learning

ERIC Educational Resources Information Center

Yeo, Jennifer; Tan, Seng Chee

2014-01-01

The introduction of problem-based learning into K-12 science classrooms faces the challenge of achieving the dual goal of learning science content and developing problem-solving skills. To overcome this content-process tension in science classrooms, we employed the knowledge-creation approach as a boundary object between the two seemingly…

Improving High-Level Thinking Skills by Development of Learning PBL Approach on the Learning Mathematics for Senior High School Students

ERIC Educational Resources Information Center

Surya, Edy; Syahputra, Edi

2017-01-01

This study aims to improve the ability of high-level thinking by developing learning models based on problems in senior high school students. The type study is research development. The subject of dissemination consists in 3 district/city in North Sumatera, namely: SMK Negeri 6 Medan, MAN Deli Serdang Distric and SMA Yapim Taruna Langkat Distric,…

Technology Development, Implementation and Assessment: K-16 Pre-Service, In-Service and Distance Learning Initiatives

NASA Technical Reports Server (NTRS)

Williams, William B., Jr.

1999-01-01

The technologies associated with distance learning are evolving rapidly, giving to educators a potential tool for enhancing the educational experiences of large numbers of students simultaneously. This enhancement, in order to be effective, must take into account the various agendas of teachers, administrators, state systems, and of course students. It must also make use of the latest research on effective pedagogy. This combination, effective pedagogy and robust information technology, is a powerful vehicle for communicating, to a large audience of school children the excitement of mathematics and science--an excitement that for the most part is now well-hidden. This project,"Technology Development, Implementation and Assessment," proposed to bring to bear on the education of learners in grades 3 - 8 in science and mathematics both advances in information technology and in effective pedagogy. Specifically, the project developed components NASA CONNECT video series--problem-based learning modules that focus on the scientific method and that incorporate problem-based learning scenarios tied to national mathematics and science standards. These videos serve two purposes; they engage students in the excitement of hands-on learning and they model for the teachers of these students the problem-based learning practices that are proving to be excellent ways to teach science and mathematics to school students. Another component of NASA CONNECT is the accompanying web-site.

Connecting Authentic Activities with Multimedia to Enhance Teaching and Learning, an Exemplar from Scottish History

ERIC Educational Resources Information Center

Hillis, Peter

2010-01-01

Much of the current focus on maximizing the potential of ICT to enhance teaching and learning is on learning tasks rather than the technology. These learning tasks increasingly employ a constructivist, problem-based methodology especially one based around authentic learning. The problem-based nature of history provides fertile ground for this…

The Triangle Technique: a new evidence-based educational tool for pediatric medication calculations.

PubMed

Sredl, Darlene

2006-01-01

Many nursing student verbalize an aversion to mathematical concepts and experience math anxiety whenever a mathematical problem is confronted. Since nurses confront mathematical problems on a daily basis, they must learn to feel comfortable with their ability to perform these calculations correctly. The Triangle Technique, a new educational tool available to nurse educators, incorporates evidence-based concepts within a graphic model using visual, auditory, and kinesthetic learning styles to demonstrate pediatric medication calculations of normal therapeutic ranges. The theoretical framework for the technique is presented, as is a pilot study examining the efficacy of the educational tool. Statistically significant results obtained by Pearson's product-moment correlation indicate that students are better able to calculate accurate pediatric therapeutic dosage ranges after participation in the educational intervention of learning the Triangle Technique.

Web-Based Virtual Laboratory for Food Analysis Course

NASA Astrophysics Data System (ADS)

Handayani, M. N.; Khoerunnisa, I.; Sugiarti, Y.

2018-02-01

Implementation of learning on food analysis course in Program Study of Agro-industrial Technology Education faced problems. These problems include the availability of space and tools in the laboratory that is not comparable with the number of students also lack of interactive learning tools. On the other hand, the information technology literacy of students is quite high as well the internet network is quite easily accessible on campus. This is a challenge as well as opportunities in the development of learning media that can help optimize learning in the laboratory. This study aims to develop web-based virtual laboratory as one of the alternative learning media in food analysis course. This research is R & D (research and development) which refers to Borg & Gall model. The results showed that assessment’s expert of web-based virtual labs developed, in terms of software engineering aspects; visual communication; material relevance; usefulness and language used, is feasible as learning media. The results of the scaled test and wide-scale test show that students strongly agree with the development of web based virtual laboratory. The response of student to this virtual laboratory was positive. Suggestions from students provided further opportunities for improvement web based virtual laboratory and should be considered for further research.

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.

ICT as a Catalyst in Problem-Based Learning Processes? A Comparison of Online and Campus-Based PBL in Swedish Fire-Fighter Training

ERIC Educational Resources Information Center

Holmgren, Robert

2013-01-01

This article focuses on the impact on learning processes when digital technologies are integrated into PBL (problem-based learning) oriented distance training. Based on socio-cultural perspectives on learning and a comparative distance-campus as well as a time-perspective, instructor and student roles, and learning activities were explored.…

Supporting Collaborative Learning and Problem-Solving in a Constraint-Based CSCL Environment for UML Class Diagrams

ERIC Educational Resources Information Center

Baghaei, Nilufar; Mitrovic, Antonija; Irwin, Warwick

2007-01-01

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on…

"A Cellular Encounter": Constructing the Cell as a Whole System Using Illustrative Models

ERIC Educational Resources Information Center

Cohen, Joel I.

2014-01-01

A standard part of biology curricula is a project-based assessment of cell structure and function. However, these are often individual assignments that promote little problem-solving or group learning and avoid the subject of organelle chemical interactions. I evaluate a model-based cell project designed to foster group and individual guided…

Problem-Based Learning

ERIC Educational Resources Information Center

Allen, Deborah E.; Donham, Richard S.; Bernhardt, Stephen A.

2011-01-01

In problem-based learning (PBL), students working in collaborative groups learn by resolving complex, realistic problems under the guidance of faculty. There is some evidence of PBL effectiveness in medical school settings where it began, and there are numerous accounts of PBL implementation in various undergraduate contexts, replete with…

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

PubMed Central

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

2017-01-01

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

"What I Learn Is What I Like." How Do Students in ICT Supported Problem-Based Learning Rate the Quality of the Learning Experience, and How Does It Relate to the Acquisition of Competences?

ERIC Educational Resources Information Center

Scholkmann, Antonia

2017-01-01

Although advocated in theory, research findings on the benefits of ICT integration into inquiry-based learning arrangements such as problem-based learning (PBL) are still ambiguous. One explanation might be that until now students' subjective views on learning in ICT-integrated, inquiry-based arrangements have not been considered extensively. The…

Increasing participation in the Earth sciences through engagement of K-12 educators in Earth system science analysis, inquiry and problem- based learning and teaching

NASA Astrophysics Data System (ADS)

Burrell, S.

2012-12-01

Given low course enrollment in geoscience courses, retention in undergraduate geoscience courses, and granting of BA and advanced degrees in the Earth sciences an effective strategy to increase participation in this field is necessary. In response, as K-12 education is a conduit to college education and the future workforce, Earth science education at the K-12 level was targeted with the development of teacher professional development around Earth system science, inquiry and problem-based learning. An NSF, NOAA and NASA funded effort through the Institute for Global Environmental Strategies led to the development of the Earth System Science Educational Alliance (ESSEA) and dissemination of interdisciplinary Earth science content modules accessible to the public and educators. These modules formed the basis for two teacher workshops, two graduate level courses for in-service teachers and two university course for undergraduate teacher candidates. Data from all three models will be presented with emphasis on the teacher workshop. Essential components of the workshop model include: teaching and modeling Earth system science analysis; teacher development of interdisciplinary, problem-based academic units for implementation in the classroom; teacher collaboration; daily workshop evaluations; classroom observations; follow-up collaborative meetings/think tanks; and the building of an on-line professional community for continued communication and exchange of best practices. Preliminary data indicate increased understanding of Earth system science, proficiency with Earth system science analysis, and renewed interest in innovative delivery of content amongst teachers. Teacher-participants reported increased student engagement in learning with the implementation of problem-based investigations in Earth science and Earth system science thinking in the classroom, however, increased enthusiasm of the teacher acted as a contributing factor. Teacher feedback on open-ended questionnaires about impact on students identify higher order thinking, critical evaluation of quantitative and qualitative information, cooperative learning, and engagement in STEM content through inquiry as core competencies of this educational method. This presentation will describe the program model and results from internal evaluation.

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

Problem-based learning using patient-simulated videos showing daily life for a comprehensive clinical approach.

PubMed

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

2017-02-27

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. 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. 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 (χ 2 =24.319, p<0.001) and paper (χ 2 =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. 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.

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…

Problem-Based Learning Revisited, Introduction of Active and Self-Directed Learning to Reduce Fatigue among Students

ERIC Educational Resources Information Center

Czabanowska, Katarzyna; Moust, Jos H. C.; Meijer, Andre W. M.; Schroder-Back, Peter; Roebertsen, Herma

2012-01-01

Despite several years of successfully applying problem-based learning at Maastricht University, the Faculty of Medicine observed a slow erosion of problem-based practices and "PBL fatigue" among themselves and students. In response to this fatigue and new research into the development of the young adult brain, Active Self-Directed…

Using Problem-Based Pre-Class Activities to Prepare Students for In-Class Learning

ERIC Educational Resources Information Center

Alayont, Feryal

2014-01-01

This article presents a problem-based approach that prepares students for future learning in the classroom. In this approach, students complete problem-based activities before coming to class to familiarize themselves with the topics to be covered. After the discussion on how the use of these activities relate to the learning and transfer…

Crib Work--An Evaluation of a Problem-Based Learning Experiment: Preliminary Results

ERIC Educational Resources Information Center

Walsh, Vonda K.; Bush, H. Francis

2013-01-01

Problem-based learning has been proven to be successful in both medical colleges and physics classes, but not uniformly across all disciplines. A college course in probability and statistics was used as a setting to test the effectiveness of problem-based learning when applied to homework. This paper compares the performances of the students from…

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…

Problem-Centered Supplemental Instruction in Biology: Influence on Content Recall, Content Understanding, and Problem Solving Ability

ERIC Educational Resources Information Center

Gardner, Joel; Belland, Brian R.

2017-01-01

To address the need for effective, efficient ways to apply active learning in undergraduate biology courses, in this paper, we propose a problem-centered approach that utilizes supplemental web-based instructional materials based on principles of active learning. We compared two supplementary web-based modules using active learning strategies: the…

The Iterative Development and Use of an Online Problem-Based Learning Module for Preservice and Inservice Teachers

ERIC Educational Resources Information Center

Rillero, Peter; Camposeco, Laurie

2018-01-01

Teachers' problem-based learning knowledge, abilities, and attitudes are important factors in successful K--12 PBL implementations. This article describes the development and use of a free, online module entitled "Design a Problem-Based Learning Experience." The module production, aligned with theories of andragogy, was a partnership…

Efficacy of problem based learning in a high school science classroom

NASA Astrophysics Data System (ADS)

Rissi, James Ryan

At the high school level, the maturity of the students, as well as constraints of the traditional high school (both in terms of class time, and number of students), impedes the use of the Problem-based instruction. But with more coaching, guidance, and planning, Problem-based Learning may be an effective teaching technique with secondary students. In recent years, the State of Michigan High School Content Expectations have emphasized the importance of inquiry and problem solving in the high school science classroom. In order to help students gain inquiry and problem solving skills, a move towards a problem-based curriculum and away from the didactic approach may lead to favorable results. In this study, the problem-based-learning framework was implemented in a high school Anatomy and Physiology classroom. Using pre-tests and post-tests over the material presented using the Problem-based technique, student comprehension and long-term retention of the material was monitored. It was found that Problem-based Learning produced comparable test performance when compared to traditional lecture, note-taking, and enrichment activities. In addition, students showed evidence of gaining research and team-working skills.

[E-Learning--an important contribution to general medical training and continuing education?].

PubMed

Ruf, D; Berner, M M; Kriston, L; Härter, M

2008-09-01

There is increasing activity in the development of e-learning modules for general medical training and continuing education. One of the central advantages of e-learning is flexibility regarding time and place of its use. The quality of the available e-learning opportunities varies quite considerably. For users it is often not easy to assess the quality of e-learning modules or to find offers of high quality. This could be a reason for the fact that despite the huge number of e-learning modules still only few students and physicians are using them. This is although e-learning has proven to be as effective as and even more efficient than learning in the classroom or with paper-based materials. This article summarizes the different models of e-learning, how and where to find offers of high quality, advantages of using e-learning, and the effectiveness and efficiency of such offers. In addition problems of e-learning and possibilities to overcome these problems are shown.

[Construction and Application of Innovative Education Technology Strategies in Nursing].

PubMed

Chao, Li-Fen; Huang, Hsiang-Ping; Ni, Lee-Fen; Tsai, Chia-Lan; Huang, Tsuey-Yuan

2017-12-01

The evolution of information and communication technologies has deeply impacted education reform, promoted the development of digital-learning models, and stimulated the development of diverse nursing education strategies in order to better fulfill needs and expand in new directions. The present paper introduces the intelligent-learning resources that are available for basic medical science education, problem-based learning, nursing scenario-based learning, objective structured clinical examinations, and other similar activities in the Department of Nursing at Chang Gung University of Science and Technology. The program is offered in two parts: specialized classroom facilities and cloud computing / mobile-learning. The latter includes high-fidelity simulation classrooms, online e-books, and virtual interactive simulation and augmented reality mobile-learning materials, which are provided through multimedia technology development, learning management systems, web-certificated examinations, and automated teaching and learning feedback mechanisms. It is expected that the teaching experiences that are shared in this article may be used as a reference for applying professional wisdom teaching models into nursing education.

Problem-Based Learning and Problem-Solving Tools: Synthesis and Direction for Distributed Education Environments.

ERIC Educational Resources Information Center

Friedman, Robert S.; Deek, Fadi P.

2002-01-01

Discusses how the design and implementation of problem-solving tools used in programming instruction are complementary with both the theories of problem-based learning (PBL), including constructivism, and the practices of distributed education environments. Examines how combining PBL, Web-based distributed education, and a problem-solving…

History matching through dynamic decision-making

PubMed Central

Maschio, Célio; Santos, Antonio Alberto; Schiozer, Denis; Rocha, Anderson

2017-01-01

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark. PMID:28582413

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

NASA Astrophysics Data System (ADS)

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

2018-05-01

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods-the particle Metropolis-Hastings algorithm-which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis-Hastings algorithm is that it is guaranteed to converge to the "true solution" under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods-including particle Metropolis-Hastings-to a large group of users without requiring them to know all the underlying mathematical details.

Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum.

PubMed

Leatemia, Lukas D; Susilo, Astrid P; van Berkel, Henk

2016-12-03

To identify the student's readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students' life styles, students' perceptions of the topics, and mood, were factors influencing their self-directed learning. A hybrid problem based curriculum may not fully affect the students' self-directed learning. The curriculum system, teacher's experiences, student's background and cultural factors might contribute to the difficulties for the student's in conducting self-directed learning.

Learning to rank using user clicks and visual features for image retrieval.

PubMed

Yu, Jun; Tao, Dacheng; Wang, Meng; Rui, Yong

2015-04-01

The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.

Towards a Web-Based Handbook of Generic, Process-Oriented Learning Designs

ERIC Educational Resources Information Center

Marjanovic, Olivera

2005-01-01

Process-oriented learning designs are innovative learning activities that include a set of inter-related learning tasks and are generic (could be used across disciplines). An example includes a problem-solving process widely used in problem-based learning today. Most of the existing process-oriented learning designs are not documented, let alone…

Physics faculty beliefs and values about the teaching and learning of problem solving. II. Procedures for measurement and analysis

NASA Astrophysics Data System (ADS)

Henderson, Charles; Yerushalmi, Edit; Kuo, Vince H.; Heller, Kenneth; Heller, Patricia

2007-12-01

To identify and describe the basis upon which instructors make curricular and pedagogical decisions, we have developed an artifact-based interview and an analysis technique based on multilayered concept maps. The policy capturing technique used in the interview asks instructors to make judgments about concrete instructional artifacts similar to those they likely encounter in their teaching environment. The analysis procedure alternatively employs both an a priori systems view analysis and an emergent categorization to construct a multilayered concept map, which is a hierarchically arranged set of concept maps where child maps include more details than parent maps. Although our goal was to develop a model of physics faculty beliefs about the teaching and learning of problem solving in the context of an introductory calculus-based physics course, the techniques described here are applicable to a variety of situations in which instructors make decisions that influence teaching and learning.

Synthesizing Huber's Problem Solving and Kolb's Learning Cycle: A Balanced Approach to Technical Problem Solving

ERIC Educational Resources Information Center

Kamis, Arnold; Khan, Beverly K.

2009-01-01

How do we model and improve technical problem solving, such as network subnetting? This paper reports an experimental study that tested several hypotheses derived from Kolb's experiential learning cycle and Huber's problem solving model. As subjects solved a network subnetting problem, they mapped their mental processes according to Huber's…

Improvement of nursing students' critical thinking skills through problem-based learning in the People's Republic of China: a quasi-experimental study.

PubMed

Yuan, Haobin; Kunaviktikul, Wipada; Klunklin, Areewan; Williams, Beverly A

2008-03-01

A quasi-experimental, two-group pretest-post-test design was conducted to examine the effect of problem-based learning on the critical thinking skills of 46 Year 2 undergraduate nursing students in the People's Republic of China. The California Critical Thinking Skills Test Form A, Chinese-Taiwanese version was used as both a pretest and as a post-test for a semester-long nursing course. There was no significant difference in critical thinking skills at pretest, whereas, significant differences in critical thinking skills existed between the problem-based learning and lecture groups at post-test. The problem-based learning students had a significantly greater improvement on the overall California Critical Thinking Skills Test, analysis, and induction subscale scores compared with the lecture students. Problem-based learning fostered nursing students' critical thinking skills.

Student Motivation in Response to Problem-Based Learning

ERIC Educational Resources Information Center

Fukuzawa, Sherry; Boyd, Cleo; Cahn, Joel

2017-01-01

Problem-based learning (PBL) is a self-directed learning strategy where students work collaboratively in small groups to investigate open-ended relatable case scenarios. Students develop transferable skills that can be applied across disciplines, such as collaboration, problem-solving, and critical thinking. Despite extensive research on…

Students' Perception of Interdisciplinary, Problem-Based Learning in a Food Biotechnology Course

ERIC Educational Resources Information Center

Ng, Betsy L. L.; Yap, Kueh C.; Hoh, Yin K.

2011-01-01

Abstract: Students' perception of 8 criteria (rationale of the problem; interdisciplinary learning; facilitator asked essential questions; learner's skills; assessments; facilitation procedures; team's use of resources [team collaboration], and facilitator within a problem-based learning context) were assessed for a food biotechnology course that…

What can we learn from facilitator and student perceptions of facilitation skills and roles in the first year of a problem-based learning curriculum?

PubMed

McLean, Michelle

2003-10-30

The small group tutorial is a cornerstone of problem-based learning. By implication, the role of the facilitator is of pivotal importance. The present investigation canvassed perceptions of facilitators with differing levels of experience regarding their roles and duties in the tutorial. In January 2002, one year after problem-based learning implementation at the Nelson R. Mandela School of Medicine, facilitators with the following experience were canvassed: trained and about to facilitate, facilitated once only and facilitated more than one six-week theme. Student comments regarding facilitator skills were obtained from a 2001 course survey. While facilitators generally agreed that the three-day training workshop provided sufficient insight into the facilitation process, they become more comfortable with increasing experience. Many facilitators experienced difficulty not providing content expertise. Again, this improved with increasing experience. Most facilitators saw students as colleagues. They agreed that they should be role models, but were less enthusiastic about being mentors. Students were critical of facilitators who were not up to date with curriculum implementation or who appeared disinterested. While facilitator responses suggest that there was considerable intrinsic motivation, this might in fact not be the case. Even if they had facilitated on all six themes, facilitators could still be considered as novices. Faculty support is therefore critical for the first few years of problem-based learning, particularly for those who had facilitated once only. Since student and facilitator expectations in the small group tutorial may differ, roles and duties of facilitators must be explicit for both parties from the outset.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

Analysis of problem solving on project based learning with resource based learning approach computer-aided program

NASA Astrophysics Data System (ADS)

Kuncoro, K. S.; Junaedi, I.; Dwijanto

2018-03-01

This study aimed to reveal the effectiveness of Project Based Learning with Resource Based Learning approach computer-aided program and analyzed problem-solving abilities in terms of problem-solving steps based on Polya stages. The research method used was mixed method with sequential explanatory design. The subject of this research was the students of math semester 4. The results showed that the S-TPS (Strong Top Problem Solving) and W-TPS (Weak Top Problem Solving) had good problem-solving abilities in each problem-solving indicator. The problem-solving ability of S-MPS (Strong Middle Problem Solving) and (Weak Middle Problem Solving) in each indicator was good. The subject of S-BPS (Strong Bottom Problem Solving) had a difficulty in solving the problem with computer program, less precise in writing the final conclusion and could not reflect the problem-solving process using Polya’s step. While the Subject of W-BPS (Weak Bottom Problem Solving) had not been able to meet almost all the indicators of problem-solving. The subject of W-BPS could not precisely made the initial table of completion so that the completion phase with Polya’s step was constrained.

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

eLearning techniques supporting problem based learning in clinical simulation.

PubMed

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

2005-08-01

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

An eLearning Standard Approach for Supporting PBL in Computer Engineering

ERIC Educational Resources Information Center

Garcia-Robles, R.; Diaz-del-Rio, F.; Vicente-Diaz, S.; Linares-Barranco, A.

2009-01-01

Problem-based learning (PBL) has proved to be a highly successful pedagogical model in many fields, although it is not that common in computer engineering. PBL goes beyond the typical teaching methodology by promoting student interaction. This paper presents a PBL trial applied to a course in a computer engineering degree at the University of…

Disrupting Traditions: Swimming against the Current of Adolescent Bullying

ERIC Educational Resources Information Center

Khasnabis, Debi; Upton, Kevin

2013-01-01

Advances in technology have aggravated the generations-old problem of bullying in schools. In this article, the authors attend to the impact of social media on bullying and advocate an approach to teaching anti-bullying that incorporates a project-based learning approach for young adolescents. Process drama as a model of learning and the use of…

Consequences of Team Charter Quality: Teamwork Mental Model Similarity and Team Viability in Engineering Design Student Teams

ERIC Educational Resources Information Center

Conway Hughston, Veronica

2014-01-01

Since 1996 ABET has mandated that undergraduate engineering degree granting institutions focus on learning outcomes such as professional skills (i.e. solving unstructured problems and working in teams). As a result, engineering curricula were restructured to include team based learning--including team charters. Team charters were diffused into…

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…

Understanding Social and Legal Justice Issues for Aboriginal Women within the Context of an Indigenous Australian Studies Classroom: A Problem-Based Learning Approach

ERIC Educational Resources Information Center

Mackinlay, Elizabeth; Thatcher, Kristy; Seldon, Camille

2004-01-01

Problem-based learning (PBL) is a pedagogical approach in which students encounter a problem and systematically set about finding ways to understand the problem through dialogue and research. PBL is an active process where students take responsibility for their learning by asking their own questions about the problem and in this paper we explore…

Multiplicative noise removal via a learned dictionary.

PubMed

Huang, Yu-Mei; Moisan, Lionel; Ng, Michael K; Zeng, Tieyong

2012-11-01

Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.

Undergraduate medical student's perceptions on traditional and problem based curricula: pilot study.

PubMed

Meo, Sultan Ayoub

2014-07-01

To evaluate and compare students' perceptions about teaching and learning, knowledge and skills, outcomes of course materials and their satisfaction in traditional Lecture Based learning versus Problem-Based Learning curricula in two different medical schools. The comparative cross-sectional questionnaire-based study was conducted in the Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, from July 2009 to January 2011. Two different undergraduate medical schools were selected; one followed the traditional curriculum, while the other followed the problem-based learning curriculum. Two equal groups of first year medical students were selected. They were taught in respiratory physiology and lung function lab according to their curriculum for a period of two weeks. At the completion of the study period, a five-point Likert scale was used to assess students' perceptions on satisfaction, academic environment, teaching and learning, knowledge and skills and outcomes of course materials about effectiveness of problem-based learning compared to traditional methods. SPSS 19 was used for statistical analysis. Students used to problem-based learning curriculum obtained marginally higher scores in their perceptions (24.10 +/- 3.63) compared to ones following the traditional curriculum (22.67 +/- 3.74). However, the difference in perceptions did not achieve a level of statistical significance. Students following problem-based learning curriculum have more positive perceptions on teaching and learning, knowledge and skills, outcomes of their course materials and satisfaction compared to the students belonging to the traditional style of medical school. However, the difference between the two groups was not statistically significant.

A novel model of motor learning capable of developing an optimal movement control law online from scratch.

PubMed

Shimansky, Yury P; Kang, Tao; He, Jiping

2004-02-01

A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

Development of Contemporary Problem-Based Learning Projects in Particle Technology

ERIC Educational Resources Information Center

Harris, Andrew T.

2009-01-01

The University of Sydney has offered an undergraduate course in particle technology using a contemporary problem based learning (PBL) methodology since 2005. Student learning is developed through the solution of complex, open-ended problems drawn from modern chemical engineering practice. Two examples are presented; i) zero emission electricity…

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…

A Natural Fit: Problem-based Learning and Technology Standards.

ERIC Educational Resources Information Center

Sage, Sara M.

2000-01-01

Discusses the use of problem-based learning to meet technology standards. Highlights include technology as a tool for locating and organizing information; the Wolf Wars problem for elementary and secondary school students that provides resources, including Web sites, for information; Web-based problems; and technology as assessment and as a…

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.

PubMed

Saa, Jaime F Delgado; Çetin, Müjdat

2012-04-01

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.

Cooperative Learning through Team-Based Projects in the Biotechnology Industry †

PubMed Central

Luginbuhl, Sarah C.; Hamilton, Paul T.

2013-01-01

We have developed a cooperative-learning, case studies project model that has teams of students working with biotechnology professionals on company-specific problems. These semester-long, team-based projects can be used effectively to provide students with valuable skills in an industry environment and experience addressing real issues faced by biotechnology companies. Using peer-evaluations, we have seen improvement in students’ professional skills such as time-management, quality of work, and level of contribution over multiple semesters. This model of team-based, industry-sponsored projects could be implemented in other college and university courses/programs to promote professional skills and expose students to an industry setting. PMID:24358386

Cooperative Learning through Team-Based Projects in the Biotechnology Industry.

PubMed

Luginbuhl, Sarah C; Hamilton, Paul T

2013-01-01

We have developed a cooperative-learning, case studies project model that has teams of students working with biotechnology professionals on company-specific problems. These semester-long, team-based projects can be used effectively to provide students with valuable skills in an industry environment and experience addressing real issues faced by biotechnology companies. Using peer-evaluations, we have seen improvement in students' professional skills such as time-management, quality of work, and level of contribution over multiple semesters. This model of team-based, industry-sponsored projects could be implemented in other college and university courses/programs to promote professional skills and expose students to an industry setting.

Investigating Problem-Based Learning Tutorship in Medical and Engineering Programs in Malaysia

ERIC Educational Resources Information Center

Servant, Virginie F. C.; Dewar, Eleanor F. A.

2015-01-01

Although Malaysia was the first country in Asia to adopt problem-based learning (PBL), the impact that this has had on its tutors remains largely unexplored. This paper details a qualitative study of the changing perceptions of teaching roles in two groups of problem-based learning tutors in two institutional contexts--one in medicine located in…

Problem-based learning on quantitative analytical chemistry course

NASA Astrophysics Data System (ADS)

Fitri, Noor

2017-12-01

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

Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Volpi, Michele; Copa, Loris

2010-05-01

The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of MNO problem: 1) hierarchical top-down clustering in an input space in order to remove redundancy when data are clustered, and 2) a general method (independent on classifier) which gives posterior probabilities that can be used to define the classifier confidence and corresponding proposals for new measurement points. The basic ideas and procedures are explained by applying simulated data sets. The real case study deals with the analysis and mapping of soil types, which is a multi-class classification problem. Maps of soil types are important for the analysis and 3D modeling of heavy metals migration in soil and prediction risk mapping. The results obtained demonstrate the high quality of SVM mapping and efficiency of monitoring network optimization by using active learning approaches. The research was partly supported by SNSF projects No. 200021-126505 and 200020-121835.

Sparsity and Nullity: Paradigm for Analysis Dictionary Learning

DTIC Science & Technology

2016-08-09

16. SECURITY CLASSIFICATION OF: Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and...we investigate the relation between the SNS problem and the analysis dictionary learning problem, and show that the SNS problem plays a central role...and may be utilized to solve dictionary learning problems. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

PubMed Central

Zhao, Xin; Cheung, Leo Wang-Kit

2007-01-01

Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. Conclusion Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently. PMID:17328811

Perceptual learning: toward a comprehensive theory.

PubMed

Watanabe, Takeo; Sasaki, Yuka

2015-01-03

Visual perceptual learning (VPL) is long-term performance increase resulting from visual perceptual experience. Task-relevant VPL of a feature results from training of a task on the feature relevant to the task. Task-irrelevant VPL arises as a result of exposure to the feature irrelevant to the trained task. At least two serious problems exist. First, there is the controversy over which stage of information processing is changed in association with task-relevant VPL. Second, no model has ever explained both task-relevant and task-irrelevant VPL. Here we propose a dual plasticity model in which feature-based plasticity is a change in a representation of the learned feature, and task-based plasticity is a change in processing of the trained task. Although the two types of plasticity underlie task-relevant VPL, only feature-based plasticity underlies task-irrelevant VPL. This model provides a new comprehensive framework in which apparently contradictory results could be explained.

Rocket to Creativity: A Field Experience in Problem-Based and Project-Based Learning

ERIC Educational Resources Information Center

Dole, Sharon F.; Bloom, Lisa A.; Doss, Kristy Kowalske

2016-01-01

This article reports the impact of a field experience in problem-based (PBL) and project-based learning (PjBL) on in-service teachers' conceptions of experiential learning. Participants had been enrolled in a hybrid class that included an online component in which they learned about PBL and PjBL, and an experiential component in which they…

The effects of using concept mapping as an artifact to engender metacognitive thinking in first-year medical students' problem-based learning discussions: A mixed-methods investigation

NASA Astrophysics Data System (ADS)

Shoop, Glenda Hostetter

Attention in medical education is turning toward instruction that not only focuses on knowledge acquisition, but on developing the medical students' clinical problem-solving skills, and their ability to critically think through complex diseases. Metacognition is regarded as an important consideration in how we teach medical students these higher-order, critical thinking skills. This study used a mixed-methods research design to investigate if concept mapping as an artifact may engender metacognitive thinking in the medical student population. Specifically the purpose of the study is twofold: (1) to determine if concept mapping, functioning as an artifact during problem-based learning, improves learning as measured by scores on test questions; and (2) to explore if the process of concept mapping alters the problem-based learning intragroup discussion in ways that show medical students are engaged in metacognitive thinking. The results showed that students in the problem-based learning concept-mapping groups used more metacognitive thinking patterns than those in the problem-based learning discussion-only group, particularly in the monitoring component. These groups also engaged in a higher level of cognitive thinking associated with reasoning through mechanisms-of-action and breaking down complex biochemical and physiologic principals. The students disclosed in focus-group interviews that concept mapping was beneficial to help them understand how discrete pieces of information fit together in a bigger structure of knowledge. They also stated that concept mapping gave them some time to think through these concepts in a larger conceptual framework. There was no significant difference in the exam-question scores between the problem-based learning concept-mapping groups and the problem-based learning discussion-only group.

Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Fang; Li, Lingling; Jiao, Licheng; Hao, Hongxia; Shang, Ronghua; Li, Yangyang

2018-01-01

In recent compressed sensing (CS)-based pan-sharpening algorithms, pan-sharpening performance is affected by two key problems. One is that there are always errors between the high-resolution panchromatic (HRP) image and the linear weighted high-resolution multispectral (HRM) image, resulting in spatial and spectral information lost. The other is that the dictionary construction process depends on the nontruth training samples. These problems have limited applications to CS-based pan-sharpening algorithm. To solve these two problems, we propose a pan-sharpening algorithm via compressed superresolution reconstruction and multidictionary learning. Through a two-stage implementation, compressed superresolution reconstruction model reduces the error effectively between the HRP and the linear weighted HRM images. Meanwhile, the multidictionary with ridgelet and curvelet is learned for both the two stages in the superresolution reconstruction process. Since ridgelet and curvelet can better capture the structure and directional characteristics, a better reconstruction result can be obtained. Experiments are done on the QuickBird and IKONOS satellites images. The results indicate that the proposed algorithm is competitive compared with the recent CS-based pan-sharpening methods and other well-known methods.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

PubMed Central

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S.

2017-01-01

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a=(u,v) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. PMID:28771201

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

PubMed

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Learning Spatially-Smooth Mappings in Non-Rigid Structure from Motion

PubMed Central

Hamsici, Onur C.; Gotardo, Paulo F.U.; Martinez, Aleix M.

2013-01-01

Non-rigid structure from motion (NRSFM) is a classical underconstrained problem in computer vision. A common approach to make NRSFM more tractable is to constrain 3D shape deformation to be smooth over time. This constraint has been used to compress the deformation model and reduce the number of unknowns that are estimated. However, temporal smoothness cannot be enforced when the data lacks temporal ordering and its benefits are less evident when objects undergo abrupt deformations. This paper proposes a new NRSFM method that addresses these problems by considering deformations as spatial variations in shape space and then enforcing spatial, rather than temporal, smoothness. This is done by modeling each 3D shape coefficient as a function of its input 2D shape. This mapping is learned in the feature space of a rotation invariant kernel, where spatial smoothness is intrinsically defined by the mapping function. As a result, our model represents shape variations compactly using custom-built coefficient bases learned from the input data, rather than a pre-specified set such as the Discrete Cosine Transform. The resulting kernel-based mapping is a by-product of the NRSFM solution and leads to another fundamental advantage of our approach: for a newly observed 2D shape, its 3D shape is recovered by simply evaluating the learned function. PMID:23946937

Learning Spatially-Smooth Mappings in Non-Rigid Structure from Motion.

PubMed

Hamsici, Onur C; Gotardo, Paulo F U; Martinez, Aleix M

2012-01-01

Non-rigid structure from motion (NRSFM) is a classical underconstrained problem in computer vision. A common approach to make NRSFM more tractable is to constrain 3D shape deformation to be smooth over time. This constraint has been used to compress the deformation model and reduce the number of unknowns that are estimated. However, temporal smoothness cannot be enforced when the data lacks temporal ordering and its benefits are less evident when objects undergo abrupt deformations. This paper proposes a new NRSFM method that addresses these problems by considering deformations as spatial variations in shape space and then enforcing spatial, rather than temporal, smoothness. This is done by modeling each 3D shape coefficient as a function of its input 2D shape. This mapping is learned in the feature space of a rotation invariant kernel, where spatial smoothness is intrinsically defined by the mapping function. As a result, our model represents shape variations compactly using custom-built coefficient bases learned from the input data, rather than a pre-specified set such as the Discrete Cosine Transform. The resulting kernel-based mapping is a by-product of the NRSFM solution and leads to another fundamental advantage of our approach: for a newly observed 2D shape, its 3D shape is recovered by simply evaluating the learned function.

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.

Constructivist, Problem-Based Learning Does Work: A Meta-Analysis of Curricular Comparisons Involving a Single Medical School

ERIC Educational Resources Information Center

Schmidt, Henk G.; van der Molen, Henk T.; te Winkel, Wilco W. R.; Wijnen, Wynand H. F. W.

2009-01-01

Effects of problem-based learning as reported in curricular comparison studies have been shown to be inconsistent over different medical schools. Therefore, we decided to summarize effects of a single well-established problem-based curriculum rather than to add up sometimes-conflicting findings from different problem-based curricula. Effect sizes…

Using Problem-Based Learning to Bring the Workplace into the Classroom

ERIC Educational Resources Information Center

Dadd, Kelsie A.

2009-01-01

A modified form of problem-based learning (PBL) with problems based on real workplace scenarios was trialled in a third year university class on Environmental Geology. Problems were developed in consultation with industry and based on their recent projects. These were then modified to allow for the shorter timeframe available, the less developed…

Effects of congruence between preferred and perceived learning environments in nursing education in Taiwan: a cross-sectional study

PubMed Central

Yeh, Ting-Kuang; Huang, Hsiu-Mei; Chan, Wing P; Chang, Chun-Yen

2016-01-01

Objective To investigate the effects of congruence between preferred and perceived learning environments on learning outcomes of nursing students. Setting A nursing course at a university in central Taiwan. Participants 124 Taiwanese nursing students enrolled in a 13-week problem-based Fundamental Nursing curriculum. Design and methods Students' preferred learning environment, perceptions about the learning environment and learning outcomes (knowledge, self-efficacy and attitudes) were assessed. On the basis of test scores measuring their preferred and perceived learning environments, students were assigned to one of two groups: a ‘preferred environment aligned with perceived learning environment’ group and a ‘preferred environment discordant with perceived learning environment’ group. Learning outcomes were analysed by group. Outcome measures Most participants preferred learning in a classroom environment that combined problem-based and lecture-based instruction. However, a mismatch of problem-based instruction with students' perceptions occurred. Learning outcomes were significantly better when students' perceptions of their instructional activities were congruent with their preferred learning environment. Conclusions As problem-based learning becomes a focus of educational reform in nursing, teachers need to be aware of students' preferences and perceptions of the learning environment. Teachers may also need to improve the match between an individual student's perception and a teacher's intention in the learning environment, and between the student's preferred and actual perceptions of the learning environment. PMID:27207620

The Application of Problem-Based Learning in Mechanical Engineering

NASA Astrophysics Data System (ADS)

Putra, Z. A.; Dewi, M.

2018-02-01

The course of Technology and Material Testing prepare students with the ability to do a variety of material testing in the study of mechanical engineering. Students find it difficult to understand the materials to make them unable to carry out the material testing in accordance with the purpose of study. This happens because they knowledge is not adequately supported by the competence to find and construct learning experience. In this study, quasy experiment research method with pre-post-test with control group design was used. The subjects of the study were students divided in two groups; control and experiment with twenty-two students in each group. Study result: their grades showed no difference in between the pre-test or post-test in control group, but the difference in grade existed between the pre-test and post-test in experiment group. Yet, there is no significant difference in the study result on both groups. The researcher recommend that it is necessary to develop Problem-Based Learning that suits need analysis on D3 Program for Mechanical Engineering Department at the State University of Padang, to ensure the compatibility between Model of Study and problems and need. This study aims to analyze how Problem-Based Learning effects on the course of Technology and Material Testing for the students of D3 Program of Mechanical Engineering of the State University of Padang.

Problem Finding in Professional Learning Communities: A Learning Study Approach

ERIC Educational Resources Information Center

Tan, Yuen Sze Michelle; Caleon, Imelda Santos

2016-01-01

This study marries collaborative problem solving and learning study in understanding the onset of a cycle of teacher professional development process within school-based professional learning communities (PLCs). It aimed to explore how a PLC carried out collaborative problem finding--a key process involved in collaborative problem solving--that…

Improving the learning of clinical reasoning through computer-based cognitive representation.

PubMed

Wu, Bian; Wang, Minhong; Johnson, Janice M; Grotzer, Tina A

2014-01-01

Objective Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results A significant improvement was found in students' learning products from the beginning to the end of the study, consistent with students' report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction.

Improving the learning of clinical reasoning through computer-based cognitive representation

PubMed Central

Wu, Bian; Wang, Minhong; Johnson, Janice M.; Grotzer, Tina A.

2014-01-01

Objective Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results A significant improvement was found in students’ learning products from the beginning to the end of the study, consistent with students’ report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction. PMID:25518871

Improving the learning of clinical reasoning through computer-based cognitive representation.

PubMed

Wu, Bian; Wang, Minhong; Johnson, Janice M; Grotzer, Tina A

2014-01-01

Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. A significant improvement was found in students' learning products from the beginning to the end of the study, consistent with students' report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction.

Effective use of real-life events as tools for teaching-learning clinical pharmacology in a problem-based learning curriculum.

PubMed

James, Henry; Al Khaja, Khalid A; Sequeira, Reginald P

2015-01-01

This paper describes how in a problem-based learning (PBL) medical curriculum, having identified the learning outcomes, problems can be developed from real-life events for teaching-learning clinical pharmacology topics for which PBL cases might be inadequate. Such problems can be very interesting and educational. Using the story of the development and withdrawal of rofecoxib (Vioxx(®)), we developed a problem for undergraduate medical students to address important issues related to clinical pharmacology and therapeutics such as new drug development, preclinical testing, clinical trials, adverse drug reactions, professionalism, and critical appraisal of literature. These topics would otherwise be difficult to address in patient-based problems. The evaluation of the problem based on pooled feedback from 57 tutorial groups, each comprising 8-10 students, collected over 5 years, supported the effectiveness of the problem. A systematic approach described in this paper can be used for the development and validation of educational material for introducing focal topics of pharmacology/clinical pharmacology integrated with other disciplines in innovative medical (and other health profession) curricula.

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…

Understanding `green chemistry' and `sustainability': an example of problem-based learning (PBL)

NASA Astrophysics Data System (ADS)

Günter, Tuğçe; Akkuzu, Nalan; Alpat, Şenol

2017-10-01

Background: This study uses problem-based learning (PBL) to ensure that students comprehend the significance of green chemistry better by experiencing the stages of identifying the problem, developing hypotheses, and providing solutions within the problem-solving process.

The Role of Mental Models in Learning to Program.

ERIC Educational Resources Information Center

Pirolli, Peter L.; Anderson, John R.

This study reports two experiments which indicate that the processes of providing subjects with insightful representations of example programs and guiding subjects through an "ideal" problem solving strategy facilitate learning. A production system model (GRAPES) has been developed that simulates problem-solving and learning in the…

PBL and CDIO: Complementary Models for Engineering Education Development

ERIC Educational Resources Information Center

Edström, Kristina; Kolmos, Anette

2014-01-01

This paper compares two models for reforming engineering education, problem/project-based learning (PBL), and conceive-design-implement-operate (CDIO), identifying and explaining similarities and differences. PBL and CDIO are defined and contrasted in terms of their history, community, definitions, curriculum design, relation to disciplines,…

LEARNING STYLE OF MEDICAL STUDENTS AND ITS CORRELATION WITH PREFERRED TEACHING METHODOLOGIES AND ACADEMIC ACHIEVEMENT.

PubMed

Bhalli, Muhammad Asif; Khan, Ishtiaq Ali; Sattar, Abdul

2015-01-01

Researchers have categorized the learning styles in many ways. Kolb proposed a classification of learner's styles as convergers, divergers, assimilators and accommodators. Honey and Mumford simplified learning styles as activists, reflectors, theorists and pragmatists. Neil Fleming's VARK model (Visual, Auditory, Read/write and Kinesthetic) is also popular. This study was carried out to determine the frequency of learning styles (Honey and Mumford) of medical students and its correlation with preferred teaching methodologies and academic achievements. A total of 77 medical students of 4th year MBBS were selected through non-probability convenient sampling for this study. Honey and Mumford's learning style questionnaire, and a 2nd questionnaire to know their preference for different teaching methodologies were distributed to the students. Learning styles were identified and correlated with preferred teaching methodologies and academic achievements by Chi-square test. Mean age of the medical students was 22.75 ± 1.05 years. Twenty one (27.3%) participants were males and 56 (72.7%) females. By learning styles, 7 (9.1%) medical students were activists, 36 (46.8%) reflectors, 13 (16.9%) theorists and 21 (27.3%) were pragmatists. Out of 77 students, 22 preferred interactive lectures; 16, small group discussion; 20 problem based learning, 10 preferred demonstration on models. Only 01 students preferred one-way lecture as the best teaching methodology. No significant correlation was found between learning styles and preferred teaching methodologies and learning styles and academic scores. Most of the medical students had reflector (46.8%) and pragmatist (27.3%) learning styles. Majority preferred interactive lectures (28.57%) and problem based learning (25.98%) as teaching methodologies. Aligning our instructional strategies with learning styles of the medical students will improve learning and academic performance.

Off-policy reinforcement learning for H∞ control design.

PubMed

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen

2015-01-01

The H∞ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H∞ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

PubMed Central

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-01-01

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

PubMed

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-02-08

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

Query-based learning for aerospace applications.

PubMed

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

2003-01-01

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

Unified Deep Learning Architecture for Modeling Biology Sequence.

PubMed

Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

Effects of Problem-Based Learning on Recognition Learning and Transfer Accounting for GPA and Goal Orientation

ERIC Educational Resources Information Center

Bergstrom, Cassendra M.; Pugh, Kevin J.; Phillips, Michael M.; Machlev, Moshe

2016-01-01

Conflicting research results have stirred controversy over the effectiveness of problem-based learning (PBL) compared to direct instruction at fostering content learning, particularly for novices. We addressed this by investigating effectiveness with respect to recognition learning and transfer and conducting an aptitude-treatment interaction…

Learning Outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities

ERIC Educational Resources Information Center

Wongsri, Piyaluk; Nuangchalerm, Prasart

2010-01-01

Problem statement: Socioscientific issues-based learning activity is essential for scientific reasoning skills and it could be used for analyzing problems be applied to each situation for more successful and suitable. The purposes of this research aimed to compare learning achievement, analytical thinking and moral reasoning of seventh grade…

Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum

PubMed Central

Susilo, Astrid P.; van Berkel, Henk

2016-01-01

Objectives To identify the student’s readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. Methods A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. Results The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students’ life styles, students’ perceptions of the topics, and mood, were factors influencing their self-directed learning. Conclusion A hybrid problem based curriculum may not fully affect the students’ self-directed learning. The curriculum system, teacher’s experiences, student’s background and cultural factors might contribute to the difficulties for the student’s in conducting self-directed learning. PMID:27915308

Using Problem-Based Learning in Accounting

ERIC Educational Resources Information Center

Hansen, James D.

2006-01-01

In this article, the author describes the process of writing a problem-based learning (PBL) problem and shows how a typical end-of-chapter accounting problem can be converted to a PBL problem. PBL uses complex, real-world problems to motivate students to identify and research the concepts and principles they need to know to solve these problems.…

Problem-based learning versus lecture-based learning in postgraduate medical education.

PubMed

Smits, Paul B; de Buisonjé, Cathelijn D; Verbeek, Jos H; van Dijk, Frank J; Metz, Jaap C; ten Cate, Olle J

2003-08-01

The objective of this study was to investigate the effectiveness of problem-based learning in comparison with lecture-based learning in a postgraduate medical training program concerning the management of mental health problems for occupational health physicians. A randomized controlled trial in 1999, with a mean follow-up of 14 months after the educational intervention, was used involving postgraduate medical education and training for occupational health physicians in The Netherlands, with 118 physicians in training as occupational health physicians. The experimental program was based on the principles of problem-based learning; the control program used the traditional lecture-based approach. Both programs were aimed at improving knowledge of and performance in the occupational management of work-related mental health problems. As the main outcome measures, knowledge tests consisting of true-or-false and open-answer questions and performance in practice based on self-reports and performance indicators were used. Satisfaction with the course was rated by the participants. In both groups, knowledge had increased equally directly after the programs and decreased equally after the follow-up. The gain in knowledge remained positive. The performance indicator scores also increased in both groups, but significantly more so in the problem-based group. The problem-based group was less satisfied with the course. Both forms of postgraduate medical training are effective. In spite of less favorable evaluations, the problem-based program appeared to be more effective than the lecture-based program in improving performance. Both programs, however, were equally effective in improving knowledge levels.

Physics students' approaches to learning and cognitive processes in solving physics problems

NASA Astrophysics Data System (ADS)

Bouchard, Josee

This study examined traditional instruction and problem-based learning (PBL) approaches to teaching and the extent to which they foster the development of desirable cognitive processes, including metacognition, critical thinking, physical intuition, and problem solving among undergraduate physics students. The study also examined students' approaches to learning and their perceived role as physics students. The research took place in the context of advanced courses of electromagnetism at a Canadian research university. The cognitive science, expertise, physics and science education, instructional psychology, and discourse processes literature provided the framework and background to conceptualize and structure this study. A within-stage mixed-model design was used and a number of instruments, including a survey, observation grids, and problem sets were developed specifically for this study. A special one-week long problem-based learning (PBL) intervention was also designed. Interviews with the instructors participating in the study provided complementary data. Findings include evidence that students in general engage in metacognitive processes in the organization of their personal study time. However, this potential, including the development of other cognitive processes, might not be stimulated as much as it could in the traditional lecture instructional context. The PBL approach was deemed as more empowering for the students. An unexpected finding came from the realisation that a simple exposure to a structured exercise of problem-solving (pre-test) was sufficient to produce superior planning and solving strategies on a second exposure (post-test) even for the students who had not been exposed to any special treatment. Maturation was ruled out as a potential threat to the validity of this finding. Another promising finding appears to be that the problem-based learning (PBL) intervention tends to foster the development of cognitive competencies, particularly physical intuition, even if it was only implemented for a short period of time. Other findings relate to the nature of the cognitive actions and activities that the students engage in when learning to solve electromagnetism problems in a PBL environment for the first time and the tutoring actions that guide students in this context.