Analyzing the Perception of the Tec21 Educational Model: A Localized Study Using Artificial Intelligence Techniques

This study leverages the artificial intelligence technique of sentiment analysis to gain insights into students' perceptions of the Tec21 competency-based educational model implemented by Tecnológico de Monterrey in 2019, to understand the progress of the model from students' perspective. Specifically, this paper presents the outcomes of a lexicon- and rule-based sentiment analysis conducted to assess and compare students' perceptions of the model upon completion of their first year and upon completion of their professional studies. A questionnaire was employed to assess six specific components of the model using a 1 to 5 Likert scale, in addition to gathering general comments about the model through an open-ended question. One-sample t-tests unveiled significantly positive mean polarity scores of students' opinions and significantly positive perceptions across all six components outlined in the questionnaire at the end of both years, whereas independent two-sample t-tests for the difference between means, considering polarity and the six components, revealed a significant decrease in all seven features.