Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance

Abstract

The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. Special emphasis was placed on the use of algorithms such as decision trees. The study includes an analysis of factors that affect students' academic performance such as previous academic achievement in educational activities, as well as social and psychological factors. Classification models were applied using the KNIME platform and the WEKA tool to analyse students' performance in three courses: database technology, artificial intelligence, and image processing in the ICT degree program. The results showed that the use of decision trees can effectively classify students' performance and determine the success and failure rate. The cruel outright mistakes, RMS error and relative supreme mistake all showed 0% whereas the kappa esteem got from the analysis extended between 0.991 and 1.00 which significantly concurs with most statistical values.