August 11, 2025Open Access

Design and Development of AI-Based Student Performance Prediction System

Key Points

The AI-based system achieved an accuracy of 88% in predicting student performance, allowing early intervention for at-risk students.
Key techniques include machine learning algorithms such as Random Forest and Support Vector Machine for performance analysis.
The approach involves analyzing diverse student data like attendance records, ensuring a comprehensive evaluation of academic success.
This technology supports educators in making informed decisions, potentially lowering dropout rates and improving educational quality.

Abstract

In the contemporary education landscape, predicting student performance plays a vital role in early action and improving academic outcomes. This thesis presents the creation of an AI-enabled Student Performance Prediction Solution that leverages machine learning techniques to accurately forecast students’ academic success and identify those at risk of underperforming. Using Python along with robust libraries such as Scikit-learn and Pandas, the system analyzes diverse student data, including attendance records, assignment scores, exam results, and historical academic performance. Multiple classification algorithms — comprising Random Forest and Support Vector Machine (SVM), Decision Tree, and k- immediate Proximate unit— were implemented and rigorously evaluated. The Random Forest algorithm proved to be the most efficient, attaining an accuracy of 88%, thereby demonstrating its potential in assisting educators with data-driven decision-making. By identifying at-risk students early, the system facilitates targeted support, helping to reduce dropout rates and enhance overall educational quality.

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