Predicting Student Performance through Multi-Channel Classification in Educational Data Mining

Abstract - This paper explores the use of data mining techniques in education, specifically for predicting student performance based on historical academic data. The study focuses on designing a multi-channel classifier that enhances prediction accuracy by integrating multiple classification techniques. The proposed system classifies students based on their academic records and identifies subject dependencies. Experimental results demonstrate that the multi-channel approach significantly enhances classification accuracy over individual classifiers. Key Words: Educational Data Mining, Student Performance Prediction, Multi-Channel Classification, Data Mining Algorithms.