High-quality vs low-quality teaching: A text-mining study to understand student sentiments in public online teaching reviews

Abstract

Purpose

The purpose of this study is to examine student sentiments regarding high-quality vs low-quality teaching.

Design/methodology/approach

This study uses a text mining technique to identify the positive and negative patterns of student sentiments from student evaluations of teaching (SET) provided on Ratemyprofessors.com. After identifying the key positive and negative sentiments, this study performs generalized linear regressions and calculates cumulative logits to analyze the impact of key sentiments on high- and low-quality teaching.

Findings

Results from 6,705 SET provided on Ratemyprofessors.com indicated that students express different sets of sentiments regarding high- vs low-quality teaching. In particular, the authors found positive sentiments such as passionate, straightforward, accessible, hilarious, sweet, inspiring and clear to be predictive of high-quality teaching. Additionally, negative sentiments such as disorganized, rude, difficult, confusing and boring were significantly related to low-quality teaching.

Originality/value

This study is one of the first few studies confirming that high- and low-quality teaching are not completely opposite to each other from the student’s perspective. That is, the presence of high-quality teaching does not necessarily mean the absence of low-quality teaching. As such, this study provides an important theoretical base for future researchers who wish to explore approaches for improving faculty teaching in the higher education setting. Additionally, this study offers educators some recommendations that may help students experience positive sentiments while minimizing negative sentiments.

Citation

Chou, S.Y., Luo, J. and Ramser, C. (2021), "High-quality vs low-quality teaching: A text-mining study to understand student sentiments in public online teaching reviews", Journal of International Education in Business, Vol. 14 No. 1, pp. 93-108. https://doi.org/10.1108/JIEB-01-2020-0007