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Leveraging self-regulation theory in mobile learning: learning behaviours, outcomes, and their interconnections among university students of varied prior knowledge levels
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DOI：10.1080/02188791.2026.2638798.png)
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Abstract
En 中文
Although technology-enhanced approaches have been widely used to support students' self-regulated learning (SRL), limited research has explored how prior knowledge influences SRL performance and behavioural patterns. This study investigated differences in learning outcomes and SRL behaviours between high-performing (HG) and low-performing (LG) university students in Mainland China using Vocab+, a mobile app embedded with a self-regulation support scheme. Twenty students participated, with data collected through pre- and post-tests and in-app log data. Quantitative analysis and lag sequential analysis were conducted. Results revealed that LG students demonstrated greater improvement in English vocabulary learning over 2 weeks, narrowing the performance gap with HG students. While both groups displayed similar SRL strategies, LG students showed a higher frequency of goal adjustment during the forethought phase. In the performance phase, LG students focused more on time monitoring than task completion, whereas HG students balanced both. In the reflection phase, LG students primarily evaluated their own progress, while HG students tended to compare their performance with peers. These findings suggest that the embedded SRL support in Vocab+ can mitigate the impact of prior knowledge and promote effective self-regulation, particularly among lower-performing learners in mobile-assisted language learning environments.
Keywords:
Self-regulated learning (SRL)
learning behaviours
lag sequential analysis
English language learning
mobile application
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