A Review of Machine Learning Techniques for Student Performance and Quality Enhancement in Higher Education
DOI:
https://doi.org/10.66635/tb57es04Keywords:
Machine learning (ML), learning management systems (LMS), Master of Computer Applications(MCA)Abstract
Higher education faces ongoing challenges in boosting student success rates and institutional quality amid vast data from learning management systems (LMS), assessments, and student interactions. Machine learning (ML) has become a cornerstone for transforming this data into predictive insights, enabling early detection of struggling students, personalized support, and strategic quality improvements. This systematic literature review synthesizes empirical studies (primarily 2015–2025) on ML for performance prediction and quality enhancement. It categorizes key methods classification, regression, clustering, and ensembles while evaluating their applications, advantages, and drawbacks, with special attention to skill-focused professional degrees like Master of Computer Applications (MCA), where coding proficiency, projects, and ongoing evaluations are pivotal. Recent trends show tree-based ensembles (e.g., Random Forest, XGBoost) achieving superior accuracy (often 85–95%) in predicting GPA and dropout risk, though interpretability issues persist. Major gaps include limited domain-specific models for professional curricula, scarce real-time systems, underutilization of explainable AI (XAI), and insufficient cross-institutional validation. Proposed future paths involve hybrid models, deep learning for multimodal data, XAI integration for transparency, and tailored analytics for programs like MCA to promote ethical, scalable, and impactful educational advancements.
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