Personalized Learning and Performance Assessment Using Machine Learning Algorithms - A Survey

The rapid digitization of education has resulted in unprecedented growth in learner-generated data, creating new opportunities for data-driven decision-making in teaching and learning processes. Traditional educational systems, which rely on uniform instructional models and static assessment strategies, are increasingly inadequate for addressing the heterogeneity of modern learner populations. Personalized learning has emerged as a promising paradigm that emphasizes adaptive instruction and continuous performance evaluation tailored to individual learner characteristics. In parallel, machine learning techniques have demonstrated substantial potential in analyzing complex educational data to support objective and scalable learner assessment. This review paper synthesizes contemporary research and methodological insights related to machine learningbased personalized learning and learner performance assessment, drawing extensively from a recent dissertation-based empirical framework. The review critically examines the role of supervised learning models, data preprocessing strategies, evaluation metrics, and ethical considerations in educational analytics. Particular emphasis is placed on balanced performance evaluation, model generalization, interpretability, and practical deployment in real-world learning environments. The review concludes that machine learning-driven performance assessment constitutes a robust foundation for personalized learning systems, provided that methodological rigor, transparency, and human oversight are maintained.