Evaluating student engagement and deep learning in interactive online psychology learning activities

Keywords: online learning, engagement, deep learning, interactive activities, psychology teaching, mixed methods

Abstract

There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and deep learning with the activities. A mixed-methods design was used to explore students’ interactions with the online activities. This method integrated learning analytics data with responses from 63 survey, nine interview, and 16 focus group participants. We found that students used a combination of mobile devices to access the online learning activities across a variety of locations during opportunistic study sessions in order to fit study into their daily routines. The online activities were perceived positively, facilitating affective, cognitive, and behavioural engagement as well as stimulating deep learning. Activities that were authentic, promoted problem-solving, applied theory to real-life scenarios, and increased students’ feelings of being supported were perceived as most beneficial to learning. These findings have implications for the future design of online activities, where activities need to accommodate students’ need for flexibility as students’ study habits become more mobile.

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Author Biographies

Dr Nicole Sugden, Charles Sturt University

Lecturer, School of Psychology

Dr Robyn Brunton, Charles Sturt University

Lecturer, School of Psychology.

Published
2021-05-10
How to Cite
Sugden, N., Brunton, R., MacDonald, J., Yeo, M., & Hicks, B. (2021). Evaluating student engagement and deep learning in interactive online psychology learning activities. Australasian Journal of Educational Technology, 37(2), 45-65. https://doi.org/10.14742/ajet.6632
Section
Special Issue 2021 – Smart Learning Environments