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Autor/inn/enHwang, Gwo-Jen; Tu, Yun-Fang; Tang, Kai-Yu
TitelAI in Online-Learning Research: Visualizing and Interpreting the Journal Publications from 1997 to 2019
QuelleIn: International Review of Research in Open and Distributed Learning, 23 (2022) 1, S.104-130 (27 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1492-3831
SchlagwörterArtificial Intelligence; Electronic Learning; Educational Research; Data Analysis; Visual Aids; Data Interpretation; Intelligent Tutoring Systems; Distance Education; Trend Analysis; Foreign Countries
AbstractThis study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010-2014, AIoL research emphasized learner-oriented learning. In 2015-2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. "Intelligent tutoring systems" have played the most important role in AIoL, followed by "profiling and prediction," and "adaptive systems with personalization." Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers. (As Provided).
AnmerkungenAthabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2022/4/11
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