Student Performance Classification in E-Learning: Insights from Predictive Learning Analytics and Machine Learning
Keywords:
e-learning, predictive learning analytics, machine learning, student performance, classificationAbstract
Predictive learning analytics has emerged as a critical research area in educational technology, integrating machine learning, data mining, and statistical modelling to forecast student outcomes and inform timely interventions. Building on the traditions of educational data mining and learning analytics, predictive learning analytics leverage large-scale learner datasets generated from learning management systems and massive open online courses. This paper conducts a short review of predictive learning analytics studies published between 2021 and 2025, following PRISMA 2020 guidelines. From an initial set of 35 records retrieved through Google Scholar, 10 studies were selected based on defined inclusion criteria. A taxonomy of predictive learning analytics research is developed across four dimensions namely study objectives, techniques, learning environments, and evaluation metrics. This paper reveals four dominant objectives which are early identification of at-risk students, performance prediction, personalised learning support, and institutional decision support. Methodologies span traditional machine learning workflows, deep learning architectures, hybrid models, and system-level frameworks. Notable applications include early warning systems, personalised intervention platforms, and institutional dashboards. However, several challenges persist, including fragmented datasets, limited generalisability, lack of interpretability in deep learning models, privacy and ethical concerns, and inconsistent evaluation practices. This paper highlights PLA’s potential to enhance student retention, personalised instruction, and institutional planning when applied responsibly. This paper concludes with recommendations for educators, emphasising the adoption of explainable models, integration of diverse learner data, and development of course-agnostic approaches to improve scalability and trust in predictive systems.
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