Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017

By Haoran Xie, Hui-Chun Chu, Gwo-Jen Hwang, Chun-Chieh Wang. Comput. Educ., 2019

558 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/9058171392374ced71b61733420bf4a873f462ed

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Citations in the corpus

Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 (2022, background): “For instance, existing review work has summarized the trends of AIEd (Tang et al., 2021; Xie et al., 2019; Zhai et al., 2021), applications (Alyahyan & Düştegör, 2020; Hooshyar et al., 2016; Liz-Domínguez et al., 2019; Shahiri et al., 2015), theoretical paradigms (Ouyang & Jiao, 2021) and AI…”
Vision, challenges, roles and research issues of Artificial Intelligence in Education (2020, background): “…learning system" gain much attention that emphasized the aim of facilitating individual students’ learning by adapting several possible aspects of learning systems, such as user interfaces, learning content, or learning paths based on each learner’s status (Essa, 2016; Xie et al., 2017, 2019).”
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, methodology): “The loop includes four steps, namely a) AI model development, b) model and algorithm optimization, c) AI model educational application, and d) validation through empirical research (Wu et al., 2022; Xie et al., 2019; Yang et al., 2021).”
A systematic literature review of personalized learning terms (2020, background): “Developing learning approaches that build on students ’ current ability and support efficacy beliefs by allowing autonomy with a proper challenge to promote academic attainment (Foshee, Elliott and Atkinson, 2016; Xie et al., 2019).”