American International Journal of Advanced Research and Development

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Machine Learning Approaches for Personalized Education Systems

Author(s) Filippo Sciarrone
Country Italy
Abstract Personalized education has emerged as an important approach for addressing differences in learner abilities, preferences, prior knowledge, academic performance, and learning pace. Machine learning provides computational techniques for analyzing learner data and developing individualized educational experiences. This study examines machine learning approaches for personalized education systems through a conceptual and literature-based research design.
The analysis focuses on supervised learning, unsupervised learning, recommendation systems, predictive analytics, learner profiling, adaptive learning, classification, clustering, and educational data mining. Machine learning can analyze assessment results, learning interactions, behavioral patterns, and academic histories to identify learner characteristics and recommend appropriate educational resources. Personalized systems can use these insights to modify learning content, sequence activities, recommend exercises, provide feedback, and identify learners requiring additional support.
However, effective implementation depends on data quality, algorithmic reliability, interpretability, privacy, fairness, teacher involvement, and digital infrastructure. Poor-quality or biased educational data can produce inaccurate learner profiles and inappropriate recommendations. The study concludes that machine learning can strengthen personalized education by supporting data-informed adaptation and individualized learning pathways. Future research should investigate explainable machine learning, privacy-preserving analytics, multimodal learner modeling, responsible recommendation systems, human-AI collaboration, and longitudinal evaluation of personalized learning outcomes.
Keywords Machine Learning, Personalized Education, Adaptive Learning, Learner Modeling, Educational Data Mining, Learning Analytics, Recommendation Systems, Artificial Intelligence, Digital Education, Personalized Learning.
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-30
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