Identifying Key Factors Influencing Teaching Quality: A Computational Pedagogy Approach

Abstract

Although previous research has explored the correlation between teacher characteristics and teaching quality, effective methods for identifying key factors that influence teaching quality are still lacking. This study aims to address this issue by developing an identification methodology based on a computational pedagogy research paradigm to identify the key characteristics of teachers and courses that influence their teaching quality. We developed quantitative models to quantify the characteristics of teaching quality, based on those identified in previous studies. Correlation and multiple correlation analyses were conducted to identify the key influencing characteristics, and grey correlation analysis was used to calculate the degree of correlation between these key characteristics and teaching quality. Our methodology was applied to 27 computer science discipline teachers and 82 courses, and validated with teaching data from eight additional teachers. Our findings demonstrate the effectiveness of our method in identifying the key influence characteristics of teachers and courses on teaching quality and confirm significant correlations between these key influential characteristics and teaching quality. This innovative approach provides new insights and tools for predicting and improving the teaching quality across disciplinary majors. Our research has significant implications for future education studies, particularly for the development of effective methods for identifying key factors that influence teaching quality. By providing a more comprehensive understanding of the key factors that influence teaching quality, our study can inform the development of evidence-based strategies to improve the teaching effectiveness for different disciplinary majors.

Analysis of Teaching Quality Factors at Aviation Vocational Education

“…Resource learning in vocational education must provide practical experience in the field or the actual work environment, giving directed and structured guidance on the learning process and harmony with the development technology. It provides a more comprehensive understanding of the critical factors that influence the quality of teaching, proving the development of effective strategies for improving the teaching of various major disciplines (Yao & Lin, 2023). Resources learning must impact learning quality practice.…”

Self-Reporting of Teacher–Student Performance in Virtual Class Interactions in Biological Sciences during the SARS-CoV-2/COVID-19 Pandemic

“…In that sense, this research also allowed the characterization of the nature of didactic interactions, as well as the teacher's and the student's own performances as perceived by the student body, under the underlying theoretical model. The methodological strategy employed converges with previously conducted studies in its focus on an analysis of the performances related to didactic interactions in the virtual modality [25][26][27][28][29][30][31][32].…”

“…The new reality of teaching-learning during the pandemic has also motivated didactic interactions and virtual practical classes to become scenarios for the study of the didactic performances of teachers and students [24]. Thus, the characterization of teacher-student didactic performances in face-to-face classes can be adapted to identify teacher-student performances in didactic interactions in virtual environments [25,26]. However, teaching in virtual environments has its own difficulties.…”