Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017
In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to 2017). To be specific, we investigated many research issues such as the parameters of adaptive/persona...
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| Published in | Computers and education Vol. 140; p. 103599 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to 2017). To be specific, we investigated many research issues such as the parameters of adaptive/personalized learning, learning supports, learning outcomes, subjects, participants, hardware, and so on. Furthermore, this study reveals that personalized/adaptive learning has always been an attractive topic in this field, and personalized data sources, for example, students’ preferences, learning achievements, profiles, and learning logs have become the main parameters for supporting personalized/adaptive learning. In addition, we found that the majority of the studies on personalized/adaptive learning still only supported traditional computers or devices, while only a few studies have been conducted on wearable devices, smartphones and tablet computers. In other words, personalized/adaptive learning has a significant number of potential applications on the above smart devices with the rapid development of artificial intelligence, virtual reality, cloud computing and wearable computing. Through the in-depth analysis of the trends and developments in the various dimensions of personalized/adaptive learning, the future research directions, issues and challenges are discussed in our paper.
•A systematic review of adaptive/personalized learning from 2007 to 2017 is conducted.•A comprehensive coding scheme is developed based on the constructivism.•Research issues like learning supports, learning outcomes and so on are addressed.•The results in all categories of the coding scheme are discussed.•Future trends and applications of adaptive/personalized learning are analyzed. |
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| AbstractList | In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to 2017). To be specific, we investigated many research issues such as the parameters of adaptive/personalized learning, learning supports, learning outcomes, subjects, participants, hardware, and so on. Furthermore, this study reveals that personalized/adaptive learning has always been an attractive topic in this field, and personalized data sources, for example, students’ preferences, learning achievements, profiles, and learning logs have become the main parameters for supporting personalized/adaptive learning. In addition, we found that the majority of the studies on personalized/adaptive learning still only supported traditional computers or devices, while only a few studies have been conducted on wearable devices, smartphones and tablet computers. In other words, personalized/adaptive learning has a significant number of potential applications on the above smart devices with the rapid development of artificial intelligence, virtual reality, cloud computing and wearable computing. Through the in-depth analysis of the trends and developments in the various dimensions of personalized/adaptive learning, the future research directions, issues and challenges are discussed in our paper.
•A systematic review of adaptive/personalized learning from 2007 to 2017 is conducted.•A comprehensive coding scheme is developed based on the constructivism.•Research issues like learning supports, learning outcomes and so on are addressed.•The results in all categories of the coding scheme are discussed.•Future trends and applications of adaptive/personalized learning are analyzed. |
| ArticleNumber | 103599 |
| Author | Hwang, Gwo-Jen Xie, Haoran Chu, Hui-Chun Wang, Chun-Chieh |
| Author_xml | – sequence: 1 givenname: Haoran orcidid: 0000-0003-0965-3617 surname: Xie fullname: Xie, Haoran email: hrxie2@gmail.com organization: Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong – sequence: 2 givenname: Hui-Chun orcidid: 0000-0002-3131-4673 surname: Chu fullname: Chu, Hui-Chun email: carolhcchu@gmail.com organization: Department of Computer Science and Information Management, Soochow University, Taiwan – sequence: 3 givenname: Gwo-Jen orcidid: 0000-0001-5155-276X surname: Hwang fullname: Hwang, Gwo-Jen email: gjhwang.academic@gmail.com organization: Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan – sequence: 4 givenname: Chun-Chieh surname: Wang fullname: Wang, Chun-Chieh email: jie809@gmail.com organization: Department of Computer Science and Information Management, Soochow University, Taiwan |
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