An Exploration of Teaching-Learning Using Data Mining Classification Algorithms in Higher Education

Abstract

Many technological advances have been made in recent years for enhancing teaching-learning. Students' engagement using activity-based learning is a major concern for the teachers. This study looked at student feedback in order to assist educational stakeholders in taking remedial measures to improve their students' performance. The amount of data saved in educational institutions is significantly expanding during this pandemic period. These data contain hidden information for the improvement of students' performance, teaching, planning, and so on. To achieve the goal of this study, the data mining techniques and the WEKA tool are used to implement the classification algorithms. We used J48, Naïve Bayes, REPTree, PART, and JRip classifiers for the experiments. Therefore, this study will also help the teachers to enhance their teaching mechanism to measure and improve students’ understanding by considering overall factors of learning.