Analysis of Textual Feedback of Students for Course Evaluation in Universities Through Machine Learning Algorithms

Abstract

Many educational institutions worldwide make significant efforts to collect student feedback to understand their perspectives on the courses and faculty. This feedback is used to enhance the institution's environment. In this modern world, institutions use data collection techniques to gather feedback. However, they lack the proper techniques to analyze and utilize this data to improve the educational quality of the institute using textual feedback. This study presents techniques for analyzing the written feedback from students, which was collected for course evaluation over a year. This paper focuses on techniques including Multinomial Naive Bayes Classifier, Long Short-Term Memory(LSTM), and Random Forest to enhance the outcomes of sentiment analysis. Ultimately, our efforts resulted in the LSTM achieving 97.45% accuracy during model testing for three types of sentiments: positive, neutral, and negative. This paper also aims to identify a clear research gap in this field and discusses the work of other researchers, including their less accurate models from the past. We also discuss the processes of collecting a sufficient amount of data to train this model, and then utilize a set of 25,689 data points for training. Furthermore, this paper primarily focuses on enhancing the quality of education. The initial model has been implemented at Balochistan UET Khuzdar, and it has produced satisfactory results. In the future, efforts will be made to find the perfect way to enhance the quality of education.