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IJIET 2021 Vol.11(12): 646-650 ISSN: 2010-3689
doi: 10.18178/ijiet.2021.11.12.1576

Why do Generative Learning Strategy Improve Memory in VR? — Based on ICALM

Wenya Yang and Xue Wang

Abstract—Generative learning strategies interconnect with cognition and emotion. Based on one-factorial experimental design, 75 participants were randomly assigned to study a chemical Virtual Reality (VR) lesson in one of three conditions: VR, VR+ summarizing, and VR+ self-testing. An emWave system was used to record the learners’ emotional state during learning. The learners’ learning outcomes were measured with retention tests, learning experiences were measured with instruments. The results showed that compared to the students were given a VR lesson without generative learning strategy, 1) the students who engaged in generative self-testing strategy during learning displayed more positive emotions in the cognition process, more positive ratings after learning, and higher memory test scores; 2) the students who engaged in generative summary strategy during learning showed more positive emotions in the cognition process, but lower immediate memory scores. These findings give new evidence to explaining how generative summarizing and self-testing learning strategies affect learning based VR.

Index Terms—Emotion, generative learning strategy, VR.

The authors are with the Faculty of Education, Tianjin Normal University, Tianjin, China (e-mail: YYYang_A@163.com, wangxuetjnu@qq.com).


Cite: Wenya Yang and Xue Wang, "Why do Generative Learning Strategy Improve Memory in VR? — Based on ICALM," International Journal of Information and Education Technology vol. 11, no. 12, pp. 646-650, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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