Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions

Abstract

Spaced repetition has been widely implemented and examined in mobile-assisted word learning as an important learning strategy. However, the nature of spaced repetition by commercial word-learning apps and the factors leading to the favoured mobile-assisted spaced repetition have yet to be investigated in authentic contexts. In this study, we coded the spaced repetition patterns and methods of nine apps and interviewed 72 Chinese English learners about their perceptions of spaced repetition for word learning. The results showed three major repetition patterns at three knowledge levels (i.e. the word is unknown, familiar-but-unsure, and known to the learner). The three most common repetition methods were using text-plus-audio for multimedia learning, conducting retrieval practice through flashcards and multiple-choice questions, and integrating game elements such as Goals/Rules and Rewards/Points into learning. Concerning learner preferences, they preferred to have (a) six or seven learning sessions for ‘unknown’ words, three or four sessions for ‘familiar-but-unsure’ words, and two or three sessions for ‘known’ words over ten- to fourteen- day periods, (b) gradually longer intervals between learning sessions, (c) text-plus-audio-plus-image as multimedia, (d) two or three innovative formats of retrieval practice, and (e) integration of Goals/Rules, Rewards/Points, and Time Limits. The results indicate that teachers, researchers, and app designers ought to consider both learning effectiveness and learner perceptions when applying, designing, and developing spaced repetition patterns and methods for mobile-assisted word learning.

Keywords:

• Mobile-assisted language learning
• repetition method
• repetition pattern
• technology-enhanced language learning
• word learning

Additional information

Notes on contributors

Ruofei Zhang

Ruofei Zhang is a research assistant at the Education University of Hong Kong. She received her bachelor’s degree in Tongji University and master’s degree in City University of Hong Kong. Her research interests include technology-enhanced language learning, game-based learning, and self-regulated language learning.

Di Zou

Dr. Di Zou is an Assistant Professor at The Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and flipped classrooms. She is the Associate Editor of the Australasian Journal of Educational Technology and Editorial Board member of the International Journal of Mobile Learning and Organisation. She has approximately 100 publications in international journals, conferences, and books, including Computers & Education, Computer Assisted Language Learning, Language Teaching Research, and Studies in Higher Education.

Haoran Xie

Dr. Haoran Xie is an Associate Professor at the Department of Computing and Decision Sciences, Lingnan University, Hong Kong. His research interest includes artificial intelligence, big data, language learning, and educational technology. He has totally published 247 research publications including 119 journal articles (94 SCI/SSCI indexed and 13 SCOPUS indexed journal articles). He has obtained 14 research awards including the Golden Medal and the special award from International Invention Innovation Competition in Canada, the Silver Award from Geneva’s Invention Expo, 2nd Prize Winner from Multimedia Grand Challenges of ACM Multimedia 2019, President’s Award for Outstanding Performance in Research in EdUHK, and five best/excellent paper awards from international conferences. He is the Editor-in-Chief of Computers & Education: Artificial Intelligence, Associate Editors of Array Journal, Australasian Journal of Educational Technology, Advances in Computational Intelligence, and International Journal of Mobile Learning and Organisation. He has successfully obtained more than 50 research grants; the total amount is more than HK$27 million. He is the Senior Member of IEEE and ACM, and the Life Member of AAAI.