Student Performance Prediction

Student performance prediction applies educational data mining and machine learning to estimate academic outcomes frominformation such as attendance, assessment marks, assignment completion, prior grades, and participation. The purpose of thispaper is to describe a practical framework that can help educators identify students who may benefit from additional academicsupport. The proposed workflow covers data collection, preprocessing, feature engineering, model training, evaluation, andpresentation of predictions through an interpretable dashboard. Candidate methods include Logistic Regression, Decision Tree,and Random Forest models. Performance should be assessed with metrics suited to the task, including accuracy, precision, recall,F1-score, and error analysis where the target is a numerical mark. The paper also considers data privacy, bias, explainability, andthe risk of treating a prediction as a fixed judgment about a learner. No original experimental dataset or measured model result isclaimed in this conceptual study. Instead, it presents a design that can be implemented and evaluated using institutionallyapproved data, with predictions used as decision support rather than as a replacement for teacher judgment.