Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

Abstract

Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching–learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching–learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers’ performances and recommendations based on data derived from students’ evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student’s recommendation for the teachers may be based on information about the students’ gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students’ recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed ( n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students’ comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments ( p = .000). While the males appear to be slightly borderline in terms of emotions ( p = .056) and sentiment ( p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students’ recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1...

Evaluation of Students’ Educational Management Quality Based on Intuitionistic Fuzzy Information

“…e practical application shows that the evaluation model is reasonable and effective; we introduce the teaching quality evaluation system into higher vocational schools [6]. ere are many other similar related studies, which prove that more and more attention has been paid to teaching quality, and the application value of fuzzy theory in teaching quality evaluation has been favored by more and more people.…”

Impact of digital technologies upon teaching and learning in higher education in Latin America: an outlook on the reach, barriers, and bottlenecks

“…Although the modern-day educational technologies have shown to be effective and promising towards teaching and learning (Ferguson et al, 2014;Herodotou et al, 2019;Ndukwe & Daniel, 2020;Sánchez-Mena et al, 2019). TEL-based education requires new skills (digital literacy) including institutional infrastructures to support the transition from traditional learning spaces/environments to the web-based (digital) learning platforms (Okoye et al, 2022;Shambour & Abu-Hashem, 2022). Notwithstanding the different educational initiatives (either deployed or ongoing), this study notes for the past eight years, only one comprehensive study (UNESCO, 2012) has been conducted to assess the prominence and application of digital technologies in education in LATAM.…”

“…Also, TEL-based Education (digitized-education) have attained flexibility and mobility in its mode of delivery or paradigms (Aguilera-Hermida et al, 2021;Diaz-Nunez et al, 2021;Okoye et al, 2021;del Rio-Chillcce et al, 2021;TEC, 2020b). This ranges from the innovative (pedagogical) frameworks or theories for teaching (Exter et al, 2019;Ndukwe & Daniel, 2020;Okoye et al, 2022), to the integration of educational technologies (EdTech) that are used to bridge the gap between the modern and traditional models for teaching/learning Shambour & Abu-Hashem, 2022), and in turn, provides new paradigms or practices for achieving sustainability and scalability in the use of EdTech for teaching or educational purposes (Clark et al, 2020;Okoye et al, 2021Okoye et al, , 2022Tondeur et al, 2020;UNESCO, 2015UNESCO, , 2021bYu & Jo, 2014).…”

Analysis of Emotional Stress of Teachers in Japanese Teaching Process Based on EEG Signal Analysis

“…Foreign scholars' definition of emotion management can be divided into three categories. The first type is service management, which emphasizes that emotional management is an activity conducive to one's own survival and development and serves individuals [ 8 ]. Individuals manage their emotions according to the relationship between social situation and themselves and their own coping ability.…”