Student Performance Prediction Using Machine Learning Techniques

ABSTRACT

Educational institutions generate large volumes of student-related data including academic records, attendance information, assessment results, and demographic attributes. Analyzing this data can provide valuable insights for improving academic performance and supporting early identification of students at risk of poor academic outcomes. Traditional methods of evaluating student performance rely on manual analysis and statistical approaches, which may not effectively capture complex relationships among multiple influencing factors. Machine learning techniques have emerged as powerful tools for analyzing educational datasets and predicting student performance based on historical data patterns. This research proposes a machine learning-based framework for predicting student academic performance using classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. The model analyzes various student attributes including study time, attendance, previous grades, and socioeconomic factors to predict academic outcomes. The performance of the proposed models is evaluated using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results demonstrate that ensemble learning algorithms provide improved prediction accuracy and can assist educational institutions in identifying students who require additional academic support.

Key words: Educational Data Mining, Student Performance Prediction, Machine Learning, Academic Analytics, Predictive Modeling