From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Pogorskiy, Eduard; Beckmann, Jens F.

Abstract

Compared to traditional classroom learning, success in online learning tends to depend more on the learner’s skill to self-regulate. Self-regulation is a complex meta-cognitive skill set that can be acquired. This study explores the effectiveness of a virtual learning assistant in terms of (a) developmental, (b) general compensatory, and (c) differential compensatory effects on learners’ self-regulatory skills in a sample of N = 157 online learners using an experimental intervention-control group design. Methods employed include behavioural trace data as well as self-reporting measures. Participants provided demographic information and responded to a 24-item self-regulation questionnaire and a 20-item personality trait questionnaire. Results indicate that the adaptive assistance did not lead to substantial developmental shifts as captured in learners’ perceived levels of self-regulation. However, various patterns of behavioural changes emerged in response to the intervention. This suggests that the virtual learning assistant has the potential to help online learners effectively compensate for deficits (in contrast to developmental shifts) in self-regulatory skills that might not yet have been developed.

Citation

Pogorskiy, E., & Beckmann, J. F. (2022). From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning. Computers and Education: Artificial Intelligence, 4, Article 100111. https://doi.org/10.1016/j.caeai.2022.100111

• Journal Article Type: Article
• Acceptance Date: Nov 25, 2022
• Online Publication Date: Dec 12, 2022
• Publication Date: 2022
• Deposit Date: Dec 13, 2022
• Publicly Available Date: Jan 23, 2023
• Journal: Computers and Education: Artificial Intelligence
• Electronic ISSN: 2666-920X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Article Number: 100111
• DOI: https://doi.org/10.1016/j.caeai.2022.100111
• Public URL: https://durham-repository.worktribe.com/output/1184577