Enhancing English Language Teaching and User Experience in Virtual Environments: A Systematic Review on Gamification and Personalised Learning
Abstract
Introduction: Virtual learning environments (VLEs) have become central to English language teaching (ELT), although persistent disengagement suggests that design must go beyond content delivery. Gamification and personalised learning (PL) contribute to enhanced user experience (UX) and better learning outcomes, but evidence on their combined effects remains fragmented.
Purpose: This systematic literature review (SLR) explains how gamification and PL influence UX, motivation, engagement and achievement in ELT-oriented VLEs, identifies effective design practices, and maps the implementation challenges that constrain them.
Method: Following PRISMA-2020 guidelines, 46 empirical studies (2015-2024) were retrieved from Scopus, Web of Science, ERIC, and Dialnet. Two extraction matrices captured bibliographic, contextual and analytical data; methodological quality was appraised with MMAT-2018. Comparative and narrative syntheses linked design features to four outcome clusters: motivation/engagement, UX, academic performance, and learner satisfaction.
Results: Challenge-based game mechanics, points and rewards reliably increased motivation and engagement, especially when integrated with adaptive feedback. PL strategies (adaptive difficulty, learner-directed paths and tailored content) produced the strongest dual gains in satisfaction and achievement. High UX emerged only when interfaces minimised cognitive load and feedback was timely. Gaps persist between short-term motivational spikes and durable learning, and competitive elements induce anxiety in novices. Key obstacles include limited digital literacy, bandwidth constraints and sparse reporting on implementation fidelity.
Conclusion: Gamification and PL can substantially enhance UX and selected learning outcomes in ELT-oriented VLEs, but only when designs align with curricular goals, resource realities and learner profiles. Future research should pursue longer mixed-method trials, transparent adaptivity and scalable models for low-resource contexts.
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