A Smart Learning Analytics Model for Dropout Risk Estimation Using Classification Techniques
Keywords:
Decision Tree, Learning Management Systems (LMS), Logistic Regression, Naïve Bayes, Random Forest, Support Vector MachineAbstract
Student dropout remains a significant concern in modern education systems, affecting institutional performance, learner progression, and socio-economic development. The expansion of digital learning environments has generated large volumes of educational data, enabling data-driven approaches for predictive analysis. This study presents an intelligent predictive framework for early student dropout risk detection using machine learning classification techniques. The proposed system integrates academic performance indicators, engagement metrics from Learning Management Systems (LMS), and demographic attributes to construct a comprehensive predictive model. Data preprocessing techniques including missing value handling, normalization, and categorical encoding are applied to enhance data quality. Multiple classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine are implemented and evaluated using performance metrics including accuracy, precision, recall, and F1-score. Experimental findings indicate that ensemble-based models, particularly Random Forest, demonstrate improved predictive accuracy and stability compared to traditional classifiers. The developed framework enables early identification of at-risk students, supporting proactive intervention strategies. The results highlight the effectiveness of learning analytics and machine learning in transforming educational data into actionable insights for improving student retention and academic success.
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