No abstract

University teaching practices impact student interest, engagement, and academic performance. This paper presents a study that uses artificial intelligence (AI) to examine students’ preferences for university teaching practices. We asked students in various fields open-ended questions about the best teaching practices they had experienced. Due to the large amount of data obtained, we used the AI-based language model Generative Pretrained Transformer-3 (GPT-3) to analyse the responses. With this model, we sorted students’ testimonies into nine theory-based categories regarding teaching practices. After analysing the reliability of the classifications conducted by GPT-3, we found that the agreement between humans was similar to that observed between humans and the AI model, which supported its reliability. Regarding students’ preferences for teaching practices, the results showed that students prefer practices that focus on (1) clarity and (2) interaction and relationships. These results enable the use of AI-based tools that facilitate the analysis of large amounts of information collected through open methods. At the didactic level, students’ preferences and demand for clear teaching practices (in which ideas and activities are stated and shown without ambiguity) that are based on interaction and relationships (between teachers and students and among students themselves) are demonstrable.

Gathering information from students’ answers to open-ended questions helps to assess the quality of teachers’ practices and its relations with students’ motivation. The present study aimed to use sentiment analysis, an artificial intelligence-based tool, to examine students’ responses to open-ended questions about their teacher’s communication. Using the obtained sentiment scores, we studied the effect of teachers engaging messages on students’ sentiment. Subsequently, we analysed the mediating role of this sentiment on the relation between teachers’ messages and students’ motivation to learn. Results showed that the higher the students’ perceived use of engaging messages, the more positive their sentiments towards their teacher’s communication. This is an important issue for future research as it shows the usefulness of sentiment analysis for studying teachers’ verbal behaviours. Findings also showed that sentiment partially mediates the effect of teachers engaging messages on students’ motivation to learn. This research paves the way for using sentiment analysis to better study the relations of teachers’ behaviours, students’ sentiments and opinions, and their outcomes.