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Adaptive learning path recommendation model for examination-oriented education

The Journal of China Universities of Posts and Telecommunications 2022年第4期29卷 77-88页

作者： Wang Jian Qiao Kuoyuan Yuan Yanlei Liu Xiaole Yang Jian School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Research and Development Department Tongfang Knowledge Network Technology Company LimitedBeijing 100192China The Teaching Affaires Office Zhengzhou Hi-Tech Zone Langyuehui Foreign Language Middle SchoolZhengzhou 450001China Yunnan Key Laboratory of Smart City in Cyberspace Security Yuxi Normal UniversityYuxi 653100China

Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.

关键词： adaptive learning learning path recommendation Dempster-Shaferevidence theory knowledge model learner model

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Using DEMATEL for Contextual learner modeling in Personalized and Ubiquitous Learning

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Computers, Materials & Continua 2021年第12期69卷 3981-4001页

作者： Saurabh Pal Pijush Kanti Dutta Pramanik Musleh Alsulami Anand Nayyar Mohammad Zarour Prasenjit Choudhury Department of Computer Science and Engineering National Institute of TechnologyDurgapurIndia Department of Information Systems Umm Al-Qura UniversityMakkahKSA Graduate School Duy Tan UniversityDa NangVietnam Prince Sultan University RiyadhSaudi Arabia

With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.

关键词： Personalized e-learning DEMATEL learner model ontology learner context personalized recommendation adaptive decisions

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Research of Context-oriented Adaptive Content Framework in Seamless Learning

Research of Context-oriented Adaptive Content Framework in S...

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2013年教育技术与管理科学国际会议

作者： ZHONG Qiuyan LIU Xiaodong JI Shaobo Faculty of Management and Economics Dalian University of Technology Sprott School of Business Carleton University Canada

The direct driving force of the research and development of mobile learning comes from the advances in mobile communication technology. However, it’s far from enough that we improve learning experience depending on new technology alone. The biggest difference between m-learning and traditional e-learning is from the change of context which is brought by mobility. Therefore context adaptation seems especially important to m-learning. This paper presents a framework to describe the factors that play an important role in delivering learning content to mobile learners, and their relationship to each other. The system constructs learner models by processing personalized information, reorganizes learning materials with adaptation labels or learning units, and generates learning content according to individual learner model so as to meet the specific personal needs.

关键词： seamless learning context adaptation learner model learning content

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