Self-determined motivation for data-based decision-making: A relevance intervention in teacher training

• Main Authors: Felix Dübbers, Martin Schmidt-Daffy
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Cogent Education
• Subjects: data-based decision-making; self-determination theory; relevance intervention; teacher training; application intentions; field experiment
• Online Access: http://dx.doi.org/10.1080/2331186X.2021.1956033

While teachers’ core responsibility is to provide high-quality instruction, they are also expected to engage in data-based decision-making (DBDM), e.g., to analyse and use data to improve instruction. We developed a relevance intervention to promote student teachers' self-determined motivation and application intentions for DBDM and implemented it into a large compulsory university course. In a randomized controlled trial, participating students were either repeatedly prompted to reflect about the relevance of DBDM contents (relevance-condition) or asked to summarize DBDM contents (summary-condition). Students in the relevance-condition reported more self-determined forms of motivation, more autonomy-satisfaction, were more willing and self-confident to apply DBDM as teachers than students in the summary-condition. The intervention’s effect on application intentions was fully mediated by an increase in self-determined motivation. Students’ knowledge of DBDM could not be increased by the intervention. Implications for improving university educational training for student teachers are discussed.