Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Intelligent Modelling of Learning Patterns in Tertiary Education Setting

Version 1 : Received: 21 September 2021 / Approved: 22 September 2021 / Online: 22 September 2021 (22:31:53 CEST)

How to cite: Tuyishimire, E.; Mabuto, W.; Gatabazi, P.; Bayisingize, S. Intelligent Modelling of Learning Patterns in Tertiary Education Setting. Preprints 2021, 2021090392 (doi: 10.20944/preprints202109.0392.v1). Tuyishimire, E.; Mabuto, W.; Gatabazi, P.; Bayisingize, S. Intelligent Modelling of Learning Patterns in Tertiary Education Setting. Preprints 2021, 2021090392 (doi: 10.20944/preprints202109.0392.v1).

Abstract

We are in the era where various processes need to be online. However, data from digital learning platforms are still underutilised in higher education, yet, they contain student learning patterns, whose awareness would contribute to educational development. This limits development of adaptive teaching and learning mechanisms. In this paper, a model for data exploitation to dynamically study students progress is proposed. Variables to determine current students progress are defined and are used to group students into different clusters. K-means clustering is performed on real data consisting of students from a South African tertiary institution. Cluster migration is analysed and the corresponding learning patterns are revealed.

Keywords

K-means; performance; pattern

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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