A Comprehensive Study to Analyze Student Evaluations of Teaching in Online Education

Abstract

The rise of online education has changed the way students usually learn by making educational materials easier to get to and creating a global learning community. While online education offers numerous benefits, it is also crucial to acknowledge its certain drawbacks, such as the potential reduction in interaction between students and teachers, which might increase signs of isolation among students and impede opportunities for collaborative learning. Therefore, Student Evaluations of Teaching (SET) play a critical role in identifying areas for improvement from the students' standpoint, thereby promoting constructive communication between students and teachers. This research conducts a comparison among the traditional Educational Data Mining (EDM) techniques to find out the best-performing classifier for analyzing student evaluations of teaching online. It is accomplished by first extracting the dataset from the student evaluations of teaching at X-University and then applying six different classifiers to the dataset that were extracted. The results demonstrated that Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) exhibited a notably high level of accuracy compared to other classification techniques. The findings of this research will provide guidance for future researchers in applying a wider range of classification techniques to extensive datasets and in implementing the necessary adjustments to achieve superior results.