This study presents a systematic and deployment-oriented analysis of machine learning (ML) techniques for learning style identification in adaptive digital environments. A total of 57 peer-reviewed studies published between 2020 and 2025 were analysed using a PRISMA-guided methodology. Beyond descriptive synthesis, the review systematically examines algorithmic paradigms, multimodal data integration strategies, evaluation protocols, and deployment readiness characteristics. The findings reveal that classical supervised models remain prevalent in small-scale applications, while deep learning and ensemble methods demonstrate improved performance in high-dimensional behavioural datasets. However, significant heterogeneity exists in validation strategies, fusion architectures, and system scalability. To address these limitations, this study proposes a deployment-oriented architectural framework that integrates: 1) context-aware model selection, 2) structured multimodal fusion design, 3) layered explainability mechanisms, and 4) a four-level deployment maturity evaluation model. The framework provides a unified system-level perspective that shifts emphasis from isolated performance optimization toward scalable, interpretable, and integration-ready ML system design. This work contributes a structured computational blueprint for developing robust and deployment-aware learning style identification systems in intelligent educational platforms.
Adeyemo et al. (Thu,) studied this question.
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