AI-Based Personalized Learning Analytics for Higher Education Systems

  • Authors

    • Donald Michie Professor of Machine Intelligence, University of Edinburgh, United Kingdom. Author
    • Roger Needham Professor, University of Cambridge, United Kingdom. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2024PI2P9F

    Published 02-04-2024

  • Artificial Intelligence, Learning Analytics, Personalized Learning, Higher Education, Educational Data Mining, Machine Learning, Adaptive Learning, Predictive Analytics, Explainable AI, Learning Management Systems

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    D. Michie and R. Needham, “AI-Based Personalized Learning Analytics for Higher Education Systems”, IJETMR, vol. 7, no. 1, pp. 01–18, Feb. 2024, doi: 10.67228/30715636/IJETMR-2024PI2P9F.

  • Abstract

    Artificial Intelligence (AI) has revolutionized the field of education by facilitating smart, personalized and adaptive learning experiences. The traditional higher education system tends to use a single approach to teaching which is unsuitable for the wide range of learning styles, abilities and attainment of individual learners. Personalized Learning Analytics (PLA) leverages AI to deliver tailored learning experiences, proactive interventions, and data-informed instructional choices, which enhance student learning outcomes. This study offers a comprehensive review and conceptual model on implementing AI-based personalized learning analytics in the higher education context. The framework is designed to leverage these diverse educational data sources, such as Learning Management Systems (LMS), assessment systems, attendance records, behavioral data, and patterns of student interactions, to create predictive models that pinpoint students who are at risk, suggest individualized learning resources, and tailor instructional approaches. The way explainable AI techniques are integrated further increases transparency, trustworthiness, and interpretability of learning recommendations for educators and students. Moreover, the framework highlights the ethical use of AI, such as handling privacy-sensitive data and ensuring responsible learning analytics. The proposed design seeks to boost student performance, student engagement, college retention rates, and institutional planning and decision-making, while also lowering drop-out rates. The paper covers recent advancements, technological underpinnings, research challenges, and prospects for AI-driven personalized learning analytics that could transform higher education into an intelligent, adaptive, and student-centric environment that meets the changing needs of digital learning and Industry 5.0.

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