Predicting the Students Performance Using WUM and Machine Learning Technique

Abstract

This project aims to develop a predictive model for assessing students’ academic performance by integrating web usage mining (WUM) and machine learning (ML) techniques. The model leverages user-provided data such as gender, parental profession, reading score, and writing score, along with behavioural insights from online educational platform interactions. The methodology includes data preprocessing, feature extraction, and adaptive algorithm selection to enhance prediction accuracy and adaptability across various student profiles. The integration of WUM provides a robust framework for educational institutions to tailor interventions and support systems effectively.