Title: Improved personalised learning pedagogy through mobile computing model for South African higher education learners

Authors: Refilwe Constance Mogase; Billy Mathias Kalema; Tope Samuel Adeyelure

Addresses: Department of Informatics, Faculty of Information and Communication Technology, Tshwane University of Technology, South Africa; Faculty of Agriculture and Natural Sciences, School of Computing and Mathematical Sciences, University of Mpumalanga, South Africa ' Department of Informatics, Faculty of Information and Communication Technology, Tshwane University of Technology, South Africa; Faculty of Agriculture and Natural Sciences, School of Computing and Mathematical Sciences, University of Mpumalanga, South Africa ' Department of Informatics, Faculty of Information and Communication Technology, Tshwane University of Technology, South Africa; Faculty of Agriculture and Natural Sciences, School of Computing and Mathematical Sciences, University of Mpumalanga, South Africa

Abstract: The rise of the Fourth Industrial Revolution and COVID-19 necessitate personalised learning (PL) to adapt and utilise virtual environments for education. However, many current PL approaches lack adaptability, flexibility, and consideration for the South African educational system. This study aimed to create a mobile computing PL model (MPCL) that enhances learner motivation. Through factor analysis and principal component analysis (PCA), influential PL factors were identified and a conceptual model was developed. A South African university provided data via an online questionnaire, which was quantitatively analysed. The results revealed that factors such as social support and outcome expectations significantly impacted MPCL, while content currency did not contribute significantly. This research expands the limited knowledge on MPCL and establishes a basis for future investigations. The developed model can serve as a platform for subsequent researchers to build upon in related research areas.

Keywords: mobile computing; personalised learning; personalised learning environments; mobile computing personalised learning; MCPL; principal component analysis; PCA.

DOI: 10.1504/IJIL.2024.137645

International Journal of Innovation and Learning, 2024 Vol.35 No.3, pp.338 - 365

Received: 10 Nov 2022
Accepted: 05 Feb 2023
Published online: 02 Apr 2024 *

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