Research Gap

Detailed models for effects of teacher characteristics on teachers' in-class behaviors and of teachers' in-class behaviors on student outcomes exist (e.g. Dörnyei, 2001; Dörnyei & Ushioda, 2013; Kunter et al., 2013), even though findings from various studies do not consistently find the same relationships (c.f. Lauermann & Butler, 2021). Many researchers suggest that teacher characteristics such as teaching enthusiasm and motivation as well as teachers in-class behavior such as teaching-quality and use of motivational teaching strategies are not to be seen as stable traits but are rather malleable (e.g. Lauermann & Butler, 2021) and may be subject to student characteristics (Fauth et al., 2021). Multiple researchers and studies suggest reciprocal effects between teachers and students (e.g. Bardach’s and Klassen, 2021; Bieg et al., in press; Lazarides & Schiefele, 2021): Highly engaged students may motivate the teacher and thereby influence teachers behavior for example, just as teachers' behavior may motivate and engage the students. Such reciprocal effects, however, have rarely been studied and it is not clear (a) which student and teacher variables are reciprocally linked over time and (b) which links are the strongest.

Project Goal

The goal of our study is to ...

1. test predictions from existing models describing effects of teacher variables (e.g., teaching emotions, teaching behavior) on student variables (e.g., interest) in longitudinal datasets (direction teacher to student);

2. extend existing models by adding reciprocal links between student variables and teacher variables (bi-directional).

In other words, we want to subject our working model depicted by Figure 1 to a rigorous and extensive test by drawing on large number of datasets. Specifically, we want to find out which variables primarily contribute to reciprocal links between teachers and students.

Method

To explore reciprocal effects between student and teacher variables we plan to run bivariate Random-Intercept Cross-Lagged Panel Models (RI-CLPMs) between all kinds of teacher and student variables that fit into the following categories: (1) teachers' internal states & processes (e.g., motivation, teaching enthusias), (2) teacher behaviours (e.g., learner support), (3) students' perceptions of teacher behaviours, (4) students' internal states & processes (e.g., interest, motivation), (5) students behaviours (e.g., participation / disruption), and (6) teachers' perceptions of student behaviours (more details in our Call for Data and Collaboration).

To adress current discussions in the literature (e.g. Lazarides & Schiefele, 2021; Wagner et al., 2016; Woolfolk Hoy, 2021) we seperate explicitly between teacher and student ratings of behavioral measures.

Because of inconsistent findings over multiple datasets in the past and in order to explore a broad range of variables we plan to run a coordinated analysis of all data that we will have access to. In this coordinated analysis, we expect to run a multitude of bivariate RI-CLPMs for each dataset: One model per combination of relevant teacher and student variables.

Eventually, we will synthesize the obtained information using meta-analytic evaluation methods.

Call for Data

To allow us to set about this project, we are looking for partners with access to relevant data.

We thus warmly invite you to follow our Call for Data and Collaboration! We are looking for longitudinal datasets with data from teachers and their students (see Call for more detail). Feel free to contact us with any information you have that might be relevant for the project or to share this project-page with colleagues who migh be able to help!

Best Regards,

Alexander Jung,

Cora Parrisius,

and Kou Murayama

References

Bardach, L., & Klassen, R. M. (2021). Teacher motivation and student outcomes: Searching for the signal. Educational Psychologist, 1–15. https://doi.org/10.1080/00461520.2021.1991799

Bieg, S., Dresel, M., Goetz, T., & Nett, U. E. (in press). Teachers' enthusiasm and humor and its' lagged relationships with students' enjoyment and boredom - A latent trait-state-approach. Learning and Instruction, 101579. https://doi.org/10.1016/j.learninstruc.2021.101579

Dörnyei, Z [Z.]. (2001). Motivational strategies in the language classroom. CUP.

Dörnyei, Z [Zoltán], & Ushioda, E. (2013). Teaching and Researching: Motivation. Routledge. https://doi.org/10.4324/9781315833750

Fauth, B., Wagner, W., Bertram, C., Göllner, R., Roloff, J., Lüdtke, O., Polikoff, M. S., Klusmann, U., & Trautwein, U. (2020). Don’t blame the teacher? The need to account for classroom characteristics in evaluations of teaching quality. Journal of Educational Psychology, 112(6), 1284–1302. https://doi.org/10.1037/edu0000416

Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013). Professional competence of teachers: Effects on instructional quality and student development. Journal of Educational Psychology, 105(3), 805–820. https://doi.org/10.1037/a0032583

Lauermann, F., & Butler, R. (2021). The elusive links between teachers’ teaching-related emotions, motivations, and self-regulation and students’ educational outcomes. Educational Psychologist, 56(4), 243–249. https://doi.org/10.1080/00461520.2021.1991800

Lazarides, R., & Schiefele, U. (2021). Teacher motivation: Implications for instruction and learning. Introduction to the special issue. Learning and Instruction, 76, 101543. https://doi.org/10.1016/j.learninstruc.2021.101543

Wagner, W., Göllner, R., Werth, S., Voss, T., Schmitz, B., & Trautwein, U. (2016). Student and teacher ratings of instructional quality: Consistency of ratings over time, agreement, and predictive power. Journal of Educational Psychology, 108(5), 705–721. https://doi.org/10.1037/edu0000075

Woolfolk Hoy, A. (2021). Teacher motivation, quality instruction, and student outcomes: Not a simple path. Learning and Instruction, 76, 101545. https://doi.org/10.1016/j.learninstruc.2021.101545