Understanding the impact of self-regulation on perceived learning outcomes based on social cognitive theory

Shih-Wei Chou, Ming-Chia Hsieh and Hui-Chun Pan

Behaviour and Information Technology, 2024, vol. 43, issue 6, 1129-1148

Abstract: This study aims to understand how self-regulation can improve perceived e-learning outcomes. We build on social cognitive theory to explore the antecedents and consequences of self-regulation. Self-regulation is captured as IT mindfulness and self-regulated learning. The proposed hypotheses are largely supported, showing that self-regulated learning affects perceived e-learning outcomes and is influenced by IT mindfulness and its antecedents, in terms of individual and contextual factors. This study collected questionnaire data from an e-learning system of university (i.e. Zuvio). This system provides both learning and social interaction features. This study provides a systematic analysis of self-regulation, which integrates social cognitive theory and the monitoring and controlling aspect to analyse how e-learners improve outcomes through self-regulation.

Date: 2024
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DOI: 10.1080/0144929X.2023.2198048

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