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Technology-enhanced learning (TEL) is now at the heart of the teaching and learning processes in many higher educational institutions (HEIs). Today, educators are faced with challenges of pedagogically specifying what tools or 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 use of the (educational) datasets derived about the teaching and learning processes to provide insights that can be used to foster the education process. To this effect, this study analyzes the students&#x2019; evaluation of teaching (SET) dataset ( n =471968) collected within a higher educational setting, to determine the prominent factors that influence students&#x2019; performance or the way (TEL-based) education is being delivered including its didactical impact and implications. 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&#x2019; 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 (text mining method) to extract the different sentiments and emotional valence expressed by the students in the SET data, and how those characteristically differ based on the period and type of evaluation they completed (between 2019 to 2021). For the quantitative analysis, the study used a multivariate analysis of covariance (MANCOVA) and multiple pairwise comparisons post-hoc tests to analyze the quantified information (average sentiment and emotional valence) that were extracted from the data to determine the marginal means of effect that the different SET types and evaluation period have on the students&#x2019; learning outcomes/perception about the teaching-learning processes, and how those may differ across the studied period (2019-2021). In addition, the study empirically discussed and shed light on the implications of the key findings for TEL-based Education, particularly implemented by the HEI during the analyzed periods. The scholastic indicators from the study shows that while the flexible digital models or instructional methods are effective for continuous education, innovative pedagogies, and teaching transformations, it also on the other hand, serves as an incentive for more robust research that idiosyncratically looks into their implications for the students&#x2019; learning outcomes and assessment done in this study.

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A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students&#x2019; Evaluation of Teaching and Learning Outcome in Education