为了清晰梳理并准确把握国际“学习者建模”领域的研究热点与脉络,以Web of Science核心期刊数据库2013-2022年间有关“学习者建模”的载文为研究对象,借助CiteSpace等可视化分析软件,对其进行文献计量分析和知识图谱分析。结果表明:“...为了清晰梳理并准确把握国际“学习者建模”领域的研究热点与脉络,以Web of Science核心期刊数据库2013-2022年间有关“学习者建模”的载文为研究对象,借助CiteSpace等可视化分析软件,对其进行文献计量分析和知识图谱分析。结果表明:“学习者建模”研究在过去10年呈现从平稳发展到急剧上升的趋势;美国、澳大利亚和英国在该研究领域起步较早且持续时间较长,中国则在近3年迈开了研究的步伐;IEEE Access是“学习者建模”领域发文量较多的期刊;研究作者、研究机构之间合作偏少;研究热点主要集中在数据训练、智能导师系统、机器学习、人工智能和学习分析等5个方面;过去10年间“学习者建模”研究分为两个阶段,2013-2019年间热点研究为智能导师系统中学习者模型的构建和应用,2019年至今的前沿热点研究是深度学习在“学习者建模”中的应用。在未来研究中可以重点关注以下方面:“学习者建模”领域要加强技术研究和应用研究合作,形成一个良好的合作循环;研究团队互相间要加强合作,要能够跨领域、交叉学科地进行更深一步的交流;继续聚焦新兴技术,将其应用于学习者建模;在大数据和深度学习技术研究不断深入的过程中,要注意数据安全和隐私问题。展开更多
Knowledge tracing aims to track students’knowledge status over time to predict students’future performance accurately.In a real environment,teachers expect knowledge tracing models to provide the interpretable resul...Knowledge tracing aims to track students’knowledge status over time to predict students’future performance accurately.In a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge status.Markov chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over time.However,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over time.In addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same problem.To address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over time.To solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly.To better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and problems.We conduct experiments with four real-world datasets in three knowledge-driven tasks.The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise sequences.We also conduct several case studies.The case studies show that TRACED exhibits excellent 展开更多
With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent lea...With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.展开更多
Background:The Science,PE,&Me!(SPEM)curriculum is a concept-based physical education curriculum that offers students coherent educational experiences for constructing health-related fitness knowledge through movem...Background:The Science,PE,&Me!(SPEM)curriculum is a concept-based physical education curriculum that offers students coherent educational experiences for constructing health-related fitness knowledge through movement experiences.The purpose of this study was to evaluate students’motivational response to the SPEM curriculum from the situational interest perspective.Methods:The study used a cluster randomized controlled design in which 30 elementary schools in one of the largest metropolitan areas in the eastern United States were randomly assigned to an experimental or comparison condition.Although all students in the 3rd,4th,and 5th grades in the targeted schools were eligible to participate in the study,a random sample of students from the experimental(n=1749;15 schools)and comparison groups(n=1985;15 schools)provided data.Students’motivational response to the SPEM curriculum or comparison curriculum was measured using the previously validated Situational Interest Scale-Elementary.Data were analyzed using structural mean modeling.Results:The results demonstrated that the experimental group(as reference group)showed significantly higher enjoyment(z=-2.01),challenge(z=-6.54),exploration(z=-12.195),novelty(z=-8.80),and attention demand(z=-7.90)than the comparison group.Conclusion:The findings indicate that the SPEM curriculum created a more situationally interesting context for learning than the comparison physical education curriculum.展开更多
文摘为了清晰梳理并准确把握国际“学习者建模”领域的研究热点与脉络,以Web of Science核心期刊数据库2013-2022年间有关“学习者建模”的载文为研究对象,借助CiteSpace等可视化分析软件,对其进行文献计量分析和知识图谱分析。结果表明:“学习者建模”研究在过去10年呈现从平稳发展到急剧上升的趋势;美国、澳大利亚和英国在该研究领域起步较早且持续时间较长,中国则在近3年迈开了研究的步伐;IEEE Access是“学习者建模”领域发文量较多的期刊;研究作者、研究机构之间合作偏少;研究热点主要集中在数据训练、智能导师系统、机器学习、人工智能和学习分析等5个方面;过去10年间“学习者建模”研究分为两个阶段,2013-2019年间热点研究为智能导师系统中学习者模型的构建和应用,2019年至今的前沿热点研究是深度学习在“学习者建模”中的应用。在未来研究中可以重点关注以下方面:“学习者建模”领域要加强技术研究和应用研究合作,形成一个良好的合作循环;研究团队互相间要加强合作,要能够跨领域、交叉学科地进行更深一步的交流;继续聚焦新兴技术,将其应用于学习者建模;在大数据和深度学习技术研究不断深入的过程中,要注意数据安全和隐私问题。
基金supported by the National Natural Science Foundation of China(Grant Nos.62272093,62137001,U1811261,and 61902055).
文摘Knowledge tracing aims to track students’knowledge status over time to predict students’future performance accurately.In a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge status.Markov chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over time.However,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over time.In addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same problem.To address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over time.To solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly.To better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and problems.We conduct experiments with four real-world datasets in three knowledge-driven tasks.The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise sequences.We also conduct several case studies.The case studies show that TRACED exhibits excellent
文摘With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.
基金the Office of the Director,U.S.National Institutes of Health,under Award Number R25RR015674-1.
文摘Background:The Science,PE,&Me!(SPEM)curriculum is a concept-based physical education curriculum that offers students coherent educational experiences for constructing health-related fitness knowledge through movement experiences.The purpose of this study was to evaluate students’motivational response to the SPEM curriculum from the situational interest perspective.Methods:The study used a cluster randomized controlled design in which 30 elementary schools in one of the largest metropolitan areas in the eastern United States were randomly assigned to an experimental or comparison condition.Although all students in the 3rd,4th,and 5th grades in the targeted schools were eligible to participate in the study,a random sample of students from the experimental(n=1749;15 schools)and comparison groups(n=1985;15 schools)provided data.Students’motivational response to the SPEM curriculum or comparison curriculum was measured using the previously validated Situational Interest Scale-Elementary.Data were analyzed using structural mean modeling.Results:The results demonstrated that the experimental group(as reference group)showed significantly higher enjoyment(z=-2.01),challenge(z=-6.54),exploration(z=-12.195),novelty(z=-8.80),and attention demand(z=-7.90)than the comparison group.Conclusion:The findings indicate that the SPEM curriculum created a more situationally interesting context for learning than the comparison physical education curriculum.
