abstract
- An ex-post facto study is presented. Qualitative data collected through a Student Evaluation of Teaching (SET) instrument was classified into positive and negative comments and suggestions from students to faculty. The objective was to understand how to profit from this dataset to generate information for improving teaching competencies training. The classification process was made through AI systems. A total of 1892 qualitative comments were selected for the analysis. Results permit the management of dataset information through a general approach to enrich the understanding of teaching competencies that need to be attended as well as a personalized way to focus on strengths and weaknesses depending on the subjects taught by each professor. Some future studies on this line are recommended for making teaching competency training decisions more efficient. For instance, an analysis of SET by discipline and curricular affinity among subjects, in a general and another in a personalized way. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.