The Education 4.0 Framework calls for Higher Education Institutions (HEIs) to innovate their curriculum for developing the competencies of the future. Tecnologico de Monterrey started a transition from an active-learning educational model to Tec21, a challenge-based learning educational model focused on competency development. After one semester of this transition, the COVID-19 pandemic disrupted education worldwide, causing most universities to adapt to online education. We found the opportunity to analyze the institutional Student Evaluation of Teaching (SET) survey at different stages of the COVID-19 pandemic, prior to the outbreak, in the transition to online learning, and after the transition to fully online course delivery. We performed this analysis separately for the two coexisting educational models and each of the schools at the university. We also compared the SET scores for the spring semester of 2021, when the two educational models had a comparable number of students. We found that SET scores were not negatively impacted by the COVID-19 pandemic hinting toward positive implications about the institutional response to the pandemic. Another finding is that the Tec21 educational model has received higher SET scores, which implies a positive perception by students. There were a few exceptions to these results, which we address explicitly; for instance, the COVID-19 pandemic might have affected SET scores in the School of Medicine and Health Sciences. Further research is necessary to evaluate the implementation of the Tec21 model comprehensively.

Technology-enhanced learning (TEL) is now at the heart of teaching and learning process in many higher education institutions (HEIs). Today, educators are faced with the challenges of pedagogically specifying what tools, methods, and technologies are used to support the teachers and students, and to help maintain/sustain a continuous education and practices. This study shows that there is an opportunity in the use of (educational) datasets derived about the teaching and learning processes to provide insights for fostering the education process. To this effect, it analyzed the students' evaluation of teaching (SET) dataset (n = 471968) collected within a higher education setting to determine prominent factors that influences the students' performance or the way (TEL-based) education is being delivered, including its didactical impact and implications for practice. Theoretically, the study employed a mixed methodology grounded on integration of the Data-structure approach and Descriptive decision theory to study the rationality behind the students' evaluation of the teaching and performance. This was done through the Textual data quantification (qualitative) and Statistical (quantitative) analysis. Qualitatively, the study applied the Educational Process and Data Mining (EPDM) model (a text mining method) to extract the different sentiments and emotional valence expressed by the students in the SET, and how those characteristically differ based on the period and type of evaluation they have completed (between 2019 to 2021). For the quantitative analysis, the study used a multivariate analysis of covariance (MANCOVA) and multiple pairwise comparisons posthoc tests to analyze the quantified information (average sentiment and emotional valence) extracted from the SET data to determine the marginal means of effect the different SET types and evaluation period have on the students' learning outcomes/perception about the teaching-learning process. In addition, the study empirically discussed and shed light on the implications of the main findings for TEL-based Education, particularly implemented by the HEI during the analyzed periods. The scholastic indicator from the study shows that while the flexible digital models or instructional methods are effective for continuous education,The associate editor coordinating the review of this manuscript and approving it for publication was Biju Issac .