Sentiment Analysis of Students’ Feedback on Faculty Online Teaching Performance Using Machine Learning Techniques

Abstract

The pandemic has given rise to challenges across different sectors, particularly in educational institutions. The mode of instruction has shifted from in-person to flexible learning, leading to increased stress and concerns for key stakeholders such as teachers, parents, and students. The ongoing spread of diseases has made in-person classes unfeasible. Even if limited face to face classes will be allowed, online teaching is deemed to remain a practice to support instructional delivery to students. Therefore, it is essential to understand the challenges and issues encountered in online teaching, particularly from the perspective of students. This knowledge is crucial for supervisors and administrators, as it provides insights to aid in planning intervention measures. These interventions can support teachers in enhancing their online teaching performance for the benefit of their students. A process that can be applied to achieve this goal is sentiment analysis. In the field of education, one of the applications of sentiment analysis is in the evaluation of faculty teaching performance. It has been a practice in educational institutions to periodically assess their teachers’ performance. However, it has not been easy to take into account the students’ comments due to the lack of methods for automated text analytics. In line with this, techniques in sentiment analysis are presented in this study. Base models such as Naïve Bayes, Support Vector Machines, Logistic Regression, and Random Forest were explored in experiments and compared to a combination of the four called ensemble. Outcomes indicate that the ensemble of the four outperformed the base models. The utilization of Ngram vectorization in conjunction with ensemble techniques resulted in the highest F1 score compared to Count and TF-IDF methods. Additionally, this approach achieved the highest Cohen’s Kappa and Matthews Correlation Coefficient (MCC), along with the lowest Cross-entropy, signifying its preference as the model of choice for sentiment classification. When applied in conjunction with an ensemble, Count vectorization yielded the highest Cohen’s Kappa and Matthews Correlation Coefficient (MCC) and the lowest Cross-entropy loss in topic classification. Visualization techniques revealed that 65.4% of student responses were positively classified, while 25.5% were negatively classified. Meanwhile, predictions indicated that 47% of student responses were related to instructional design/delivery, 45.3% described the personality/behavior of teachers, 3.4% focused on the use of technology, 2.9% on content, and 1.5% on student assessment.