Learning Analytics in Indian Universities: Developing an Early Warning Framework for Student Success

Abstract

Learning Management Systems (LMS) collect behavior data that could help universities identify academic risks before they occur, such as during exams or when students withdraw from a course. While the field of learning analytics has been extensively researched worldwide, evidence on the generalization, effectiveness, and transferability of predictive models and corresponding interventions to Indian universities remains limited. This paper examines the potential of LMS-collected data to detect academic risks early, such as disengagement, poor academic results, lack of assignment submission, and dropout risk. The paper conducts a literature review and conceptual synthesis, focusing on educational data mining, behavior prediction, early warning systems, and learning analytics interventions, particularly in Indian higher education. The review concludes that assessment participation, assignment submission behavior, resource use, and online engagement can support prediction. Still, interpretation should depend on course characteristics, data quality and availability, technological capabilities, and measurement timing. Empirical studies in India show that predicting and analyzing educational engagement is possible, yet evidence on the effectiveness of early warning systems at Indian universities remains limited. We introduce an Indian Early Academic Risk Detection Framework (IEARDF) with five interrelated elements: identifying behavioral markers, contextualizing them, predicting risk, human academic intervention, and decision-making. The study distinguishes predictive performance from intervention effectiveness and emphasizes transparency in decisionmaking, privacy, and equity in student support. The proposed framework provides a basis for empirically validating these concepts.