The effects of mobile technology usage on cognitive, affective, and behavioural learning outcomes in primary and secondary education: A systematic review with meta-analysis
Journal article, Peer reviewed
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Original versionJournal of Computer Assisted Learning. 2022, first online . 10.1111/jcal.12759
Background: The impact of mobile technology usage on student learning in various educational stages has been the subject of ongoing empirical and review research. The most recent meta‐analyses on various types of mobile technology use for poten- tial benefits of learning covered the empirical studies up to about nine years ago. Since then, the use of mobile technology in primary and secondary education has increased tremendously, and numerous empirical studies have been conducted on this topic, but their conclusions were inconsistent. Objectives: The purpose of this systematic review is to re‐examine this issue by meta‐analyzing the empirical research studies from the last nine years, with a focus on cognitive, affective, and behavioral learning outcomes in primary and secondary education, and to examine the potential moderators that may have contributed to the heterogeneity across findings. Methods: Based on our inclusion and exclusion criteria, we found 85 studies of 78 peer‐reviewed papers (N = 9157) from electronic databases and major journals in educational technology and mobile learning between 2014 and 2022. We then exam- ined 15 moderators that were expected to affect student learning outcomes. Results and Conclusions: Compared with traditional technology and non‐technology groups, using mobile technology produced medium positive and statistically signifi- cant effects on primary and secondary students' learning, in terms of cognitive (g = 0.498, 95% CI [0.382, 0.614]), affective (g = 0.449, 95% CI [0.301, 0.598]) and beha- vioural (g = 0.339, 95% CI [0.051, 0.627]) learning outcomes. Further moderator ana- lyses revealed that student factors (i.e., community type, students’ socioeconomic status), learning process (i.e., hardware used, student‐to‐hardware ratio, teaching method) and study quality (i.e., learning topic/content equivalence, degree of tech- nology use in the control group) were among the variables that moderated the sum- mary effect sizes for at least one learning outcome dimension significantly. The findings and their implications for researchers, policymakers and practitioners are discussed.