A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

Abstract

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 post-hoc 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, innovative pedagogies, and teaching transformations. It also, on the other hand, serve as an incentive for more robust research that idiosyncratically look into their implications for the students’ learning outcomes and assessment done in this study.

Keywords

• Text mining
• teaching assessment
• TEL-based education
• learning models
• students evaluation
• sentiment analysis