Learners’ web navigation behaviour beyond learning management systems: A way into addressing procrastination in online learning?

Pogorskiy, Eduard; Beckmann, Jens F.

Abstract

The attractiveness of online games, social media, and mobile apps is frequently considered a challenge for online learners. Procrastinatory behaviour is often associated with a relative lack of self-regulatory skills that would otherwise help learners to resist distractions and to progress in learning. This paper reports a pilot study, conducted with 49 online learners, in which we describe the use of a virtual learning assistant as a tool for collecting online learners' web navigation behaviour. As this virtual learning assistant operates as an extension to the Chrome web browser, it is possible that data collection is achieved independently of, and beyond specific learning management systems. Furthermore, the study opens up the possibility of leveraging the collected dataset for visual learning analytics and pattern mining. To demonstrate the potential utility of the virtual learning assistant, we present an example for a detailed examination of a learner's web navigation behaviour. The results of the detailed examination of a single learner's web navigation behaviour over 333 days, presented as a case study, revealed the presence of seasonality in accessing certain web resources and stable sequential patterns in the learner's web navigation that can be associated with procrastinatory behaviour.

Citation

Pogorskiy, E., & Beckmann, J. F. (2022). Learners’ web navigation behaviour beyond learning management systems: A way into addressing procrastination in online learning?. Computers and Education: Artificial Intelligence, 3, https://doi.org/10.1016/j.caeai.2022.100094

• Journal Article Type: Article
• Acceptance Date: Aug 21, 2022
• Online Publication Date: Sep 9, 2022
• Publication Date: 2022
• Deposit Date: Sep 12, 2022
• Publicly Available Date: Nov 10, 2022
• Journal: Computers and Education: Artificial Intelligence
• Electronic ISSN: 2666-920X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 3
• DOI: https://doi.org/10.1016/j.caeai.2022.100094
• Public URL: https://durham-repository.worktribe.com/output/1192102
• Related Public URLs: https://www.sciencedirect.com/science/article/pii/S2666920X22000492?utm_campaign=STMJ_AUTH_SERV_PUBLISHED&utm_medium=email&utm_acid=77841987&SIS_ID=&dgcid=STMJ_AUTH_SERV_PUBLISHED&CMX_ID=&utm_in=DM294062&utm_source=AC_