A Survey on Interpretable Machine Learning Techniques for Student Adaptability Prediction in Educational Systems
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Abstract
Education is an important part of growing knowledge, skills and adaptability in today's rapidly evolving society. The
digital revolution has led to the widespread adoption of online learning platforms, smart tutoring systems, and data-driven learning
environments in education, enhancing learning effectiveness and accessibility. This is because the difficulty of predicting students'
adaptability has become an important research topic, helping to understand learning difficulties, improve learning outcomes and
personalize learning. This survey explores how interpretable machine learning (IML) techniques can be used to predict student
adaptability in the context of education. It explores machine learning and deep learning models such as Decision Trees, Random
Forests, Support Vector Machines, XGBoost and Long Short-Term Memory (LSTM) networks for learning student behaviour,
engagement and learning patterns. Furthermore, the paper explores the concept of Explainable Artificial Intelligence (XAI) methods
(including SHAP analysis, LIME, feature importance analysis, and rule-based explanations) that contribute to making AI in
education more transparent and trusted. In smart educational environments, the results indicate that applicable AI methods enhance
the adaptive learning approaches, timely interventions, provide individual assistance to students, and provide clear analytics.
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