AI-BASED STUDENT PREFORMANCE PREDICTION

Abstract: Education is increasingly generating large amounts of digital data through examinations, assignments, attendance systems, online learning platforms, and internal assessments. This data can provide valuable information about student learning and academic progress.

Student Performance Prediction using Machine Learning is a system that analyzes historical student information and predicts the likely academic performance of students. The system can consider several factors, including attendance, previous examination marks, assignment performance, internal assessment marks, study hours, classroom participation, and other relevant attributes.

The proposed system consists of multiple stages, including data collection, data preprocessing, feature selection, model training, model testing, and performance prediction. Machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine can be evaluated for the prediction task.

The system can help teachers identify students who may require additional academic support. It can also help educational institutions understand patterns associated with student achievement and improve academic planning.

However, predictions should be treated as supporting information rather than as a final judgment about students. Privacy, fairness, transparency, and human supervision are important when applying machine learning to educational data.

Keywords: Student Performance, Machine Learning, Educational Data Mining, Prediction, Classification, Academic Analytics, Data Science.