Relevance

The project has scientific relevance on two levels. First, new insights into the importance of motivational teacher language are to be gained at the content level. On a methodological level, the evaluation of large amounts of data via large language models will enrich classroom research.

Relevance for the field:

A large body of research demonstrates the relevance of a range of teacher motivational beliefs on student outcomes (e.g., Han & Gao, 2023; Klassen & Tze, 2014). However, there is limited research exploring the psychological and instructional mechanisms through which teachers’ motivational beliefs are transmitted to their students. The proposed research project seeks to address this gap by drawing on situated expectancy-value theory (SEVT; Eccles & Wigfield, 2020) to explore how teachers’ motivational beliefs influence students’ motivational outcomes via the use of motivational messages. SEVT proposes that socializers (including teachers) influence the motivational beliefs of students through their beliefs and support behaviours (Eccles & Wigfield, 2020). There is a growing body of evidence suggesting that the use of motivational messages may be a specific teacher support behaviour that is influenced by teachers’ own motivational beliefs (Falcon et al., 2023). Motivational messages have further been shown to affect students’ motivation (Putwain & Symes, 2014; Symes & Putwain, 2016). Yet, although it is known that teachers’ motivational messages are affected by teachers’ motivational beliefs, and in turn, shape students’ motivation, until now, research has not explored whether teacher use of motivational messages is a mechanism that explains why certain teachers’ motivational beliefs influence students’ motivation. We plan to address this research gap by examining the longitudinally mediated links between teacher and students’ motivations through teachers’ motivational message use in class.

In this project, we are particularly interested in the motivational change in mathematics in the course of the 7th or 8th grade, as the motivation of learners decreases, especially at the beginning of secondary school.

Methodological relevance:

In the project, audio recordings of teacher-learner interaction from regular lessons are used. AI-based methods, specifically large-language models (LLMs), are used to code the motivational instructional language. The project contributes to the further development of the methodological repertoire of empirical teaching research.

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