Aspect based sentiment analysis in students’ evaluation of teaching

Abstract

Student evaluations of teachings (SETs) are essential for assessing educational quality. Natural Language Processing (NLP) techniques can produce informative insights from these evaluations. The large quantity of text data received from SETs has surpassed the capacity for manual processing. Employing NLP to analyze student feedback offers an efficient method for understanding educational experiences, enabling educational institutions to identify patterns and trends that might have been difficult, if not impossible, to notice with a manual analysis. Data mining using NLP techniques can delve into the thoughts and perspectives of students on their educational experiences, identifying sentiments and aspects that may have a level of abstraction that the human analysis cannot perceive. I use different NLP techniques to enhance the analysis of student feedback in the form of comments and provide better insights and understanding into factors that influence students’ sentiments. This study aims to provide an overview of the various approaches used in NLP and sentiment analysis, focusing on analyzing the models and text representations used to classify numerical scores obtained from the text feedback of a corpus of SETs in Spanish. I provide a series of experiments using different text classification algorithms for sentiment classification over numerical scores of educational aspects. Additionally, I explore two Aspect Based Sentiment Analysis (ABSA) models, a pipeline and a multi-task approach, to extract broad and comprehensive insights from educational feedback for each professor. The results of this research demonstrate the effectiveness of using NLP techniques for analyzing student feedback. The sentiment classification experiments showed favorable outcomes, indicating that it is possible to utilize student comments to classify certain educational scores accurately. Furthermore, the qualitative results obtained from the ABSA models, presented in a user-friendly dashboard, highlight the efficiency and utility of employing these algorithms for the analysis of student feedback. The dashboard provides valuable insights into the sentiments expressed by students regarding various aspects of their educational experience, allowing for a more comprehensive understanding of the factors influencing their opinions. These findings highlight the potential of NLP in the educational domain, offering a powerful tool for institutions to gain a deeper understanding of student perspectives and make data-driven decisions to enhance the quality of education.