{"title":"自适应电子学习系统中学习者模型的开发与技术:系统综述","authors":"Xiyu Wang, Yukiko Maeda, Hua-Hua Chang","doi":"10.1016/j.compedu.2024.105184","DOIUrl":null,"url":null,"abstract":"<div><div>Adaptive e-learning systems (AeLS), which emerged in the late 1990s, offer an alternative to the 'one-size-fits-all' approach by addressing the demand for individualized learning experiences. These systems typically consist of five elements, including a domain model, a media space, an adaptation model, a user interface, and a learner model. Despite the increasing academic interest in this topic and the rapid development of techniques for adaptation over the past decade, there remains a significant gap in reviews that investigate learner characteristics and the techniques used for characteristic identification. To bridge this gap, we conducted a systematic review with a total of 57 studies reported from 2013 to 2023 to provide a comprehensive overview of the current trends in adaptive e-learning system research. While this review may serve as a reference for setting up a learner model as it provides the landscape of techniques utilized in recent studies, our review revealed a scarcity of research on the development of the learner model, particularly the studies that share clear theoretical or empirical justification of the techniques used for adaptation. We recommend incorporating multiple relevant learner characteristics in learner model and providing clear rationales for selecting these characteristics. We also suggest that future research should consider incorporating adaptive assessment more extensively in AeLSs.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"225 ","pages":"Article 105184"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and techniques in learner model in adaptive e-learning system: A systematic review\",\"authors\":\"Xiyu Wang, Yukiko Maeda, Hua-Hua Chang\",\"doi\":\"10.1016/j.compedu.2024.105184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Adaptive e-learning systems (AeLS), which emerged in the late 1990s, offer an alternative to the 'one-size-fits-all' approach by addressing the demand for individualized learning experiences. These systems typically consist of five elements, including a domain model, a media space, an adaptation model, a user interface, and a learner model. Despite the increasing academic interest in this topic and the rapid development of techniques for adaptation over the past decade, there remains a significant gap in reviews that investigate learner characteristics and the techniques used for characteristic identification. To bridge this gap, we conducted a systematic review with a total of 57 studies reported from 2013 to 2023 to provide a comprehensive overview of the current trends in adaptive e-learning system research. While this review may serve as a reference for setting up a learner model as it provides the landscape of techniques utilized in recent studies, our review revealed a scarcity of research on the development of the learner model, particularly the studies that share clear theoretical or empirical justification of the techniques used for adaptation. We recommend incorporating multiple relevant learner characteristics in learner model and providing clear rationales for selecting these characteristics. We also suggest that future research should consider incorporating adaptive assessment more extensively in AeLSs.</div></div>\",\"PeriodicalId\":10568,\"journal\":{\"name\":\"Computers & Education\",\"volume\":\"225 \",\"pages\":\"Article 105184\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Education\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360131524001982\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Education","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360131524001982","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Development and techniques in learner model in adaptive e-learning system: A systematic review
Adaptive e-learning systems (AeLS), which emerged in the late 1990s, offer an alternative to the 'one-size-fits-all' approach by addressing the demand for individualized learning experiences. These systems typically consist of five elements, including a domain model, a media space, an adaptation model, a user interface, and a learner model. Despite the increasing academic interest in this topic and the rapid development of techniques for adaptation over the past decade, there remains a significant gap in reviews that investigate learner characteristics and the techniques used for characteristic identification. To bridge this gap, we conducted a systematic review with a total of 57 studies reported from 2013 to 2023 to provide a comprehensive overview of the current trends in adaptive e-learning system research. While this review may serve as a reference for setting up a learner model as it provides the landscape of techniques utilized in recent studies, our review revealed a scarcity of research on the development of the learner model, particularly the studies that share clear theoretical or empirical justification of the techniques used for adaptation. We recommend incorporating multiple relevant learner characteristics in learner model and providing clear rationales for selecting these characteristics. We also suggest that future research should consider incorporating adaptive assessment more extensively in AeLSs.
期刊介绍:
Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.