Factors related to differences in digitally measured student perceptions of teaching quality

ABSTRACT

Modern digital technologies enable the efficient collection and processing of student perceptions of teaching quality. However, students’ ratings could be confounded by student, teacher, and classroom characteristics. We investigated students’ ratings of 26 teachers who used the digital tool Impact! in their mathematics lessons with 14- and 15-year-old students (n = 717). A Bayesian item response theory (IRT)-model approach was used to model potential associations. High-performing students on average rated their teacher higher than low- and middle-performing students. More likeable and more experienced teachers received higher ratings from their students, and the higher the class’s average math grade, the higher the students rated their teachers. Other variables investigated in this study (e.g., student and teacher gender, class size) were not associated with student perceptions of teaching quality. Both related and unrelated factors are discussed. Some implications of the findings for practice, limitations of the study, and suggestions for further research are presented.

KEYWORDS:

• Student perceptions
• teaching quality
• associated factors

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Dutch Research Council (NWO) under grant number 36.201.009.

Notes on contributors

Hannah J. E. Bijlsma

Hannah J. E. Bijlsma is a researcher at the Section of Teacher Professionalization ELAN of the University of Twente, a primary school teacher (Grade 1), and a Dutch School Inspector. She is interested in teaching quality, teacher learning, and the improvement of teaching. Her dissertation is about the validity and impact of student perceptions of teaching quality.

Cees A. W. Glas

Cees A. W. Glas is an emeritus professor at the Department of Research Methodology, Measurement and Data Analysis, of the Faculty of Behavioural, Management and Social Sciences of the University of Twente in the Netherlands. The focus of his work is on the estimation and testing of latent variable models in general and of item response theory models.

Adrie J. Visscher

Adrie J. Visscher is a full professor at the University of Twente and holds an endowed chair in data-based decision making at the University of Groningen. He is interested in how teachers can be supported in optimizing both lesson quality and their impact on student learning through various types of feedback and by training teachers to differentiate teaching in line with students’ varying instructional needs.