Temporal Patterns of Struggle and Success in Asynchronous Problem-Based Learning

Author

Munyaradzi Maravanyika, Namibia University of Science and Technology, Namibia

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.VNUV7620

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

The early identification of struggling students is a persistent challenge for resource-constrained higher education institutions, especially those relying on asynchronous online learning. This paper presents a temporal feature engineering approach using only Learning Management System (LMS) timestamp data to distinguish struggling students from their successful peers in asynchronous problem-based learning (PBL) environments. Five temporal features are derived and evaluated against the Open University Learning Analytics Dataset (OULAD), covering 1,938 students. Results show that struggling students can be flagged with 69.9% recall by week 4 of a module, yielding actionable intelligence within a practically useful intervention window. The paper discusses the implications of this sensitivity relative to the standard 75% threshold, proposes avenues to close this gap, and critically examines the extent to which findings obtained on a UK dataset are transferable to African higher education institutions.

Keywords

Learning analytics; temporal features; problem-based learning; early warning systems; student success; asynchronous learning; Africa; OULAD

Cite this paper

M. Maravanyika (2026) "Temporal Patterns of Struggle and Success in Asynchronous Problem-Based Learning", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.VNUV7620

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