Game learning analytics (GLA) is an emerging technology that facilitates teachers’ evidence-based pedagogical design and assessments. Despite its affordances and potential in K–12 classrooms, teachers’ integration of GLA in teaching practices remains largely unexplored. This study implemented an educational game on collaborative problem solving (CPS) and a GLA system for assisting K–12 teachers in evaluating students’ CPS skills and processes and quest performance and engagement. Based on the integrative model of behavioural prediction, this study aimed to examine 1) the extent to which personal, environmental, and technological factors affected teachers’ usage intention and behaviour towards the GLA system, 2) the effects of moderators on the intention–behaviour relationship, and 3) how the structural model relationships differed across teachers with various individual characteristics. Survey data from 300 in-service teachers from Chinese primary and secondary schools were collected and analysed using partial least squares structural equation modelling. Results indicated that our model demonstrated strong in-sample and out-of-sample predictive power. In particular, teachers’ attitudes, subjective norms, and self-efficacy influenced their behavioural intention, while technological pedagogical content knowledge, school support, and behavioural intention predicted their actual behaviour. In addition, technostress acted as a significant moderator of the intention–behaviour relationship. Moreover, teachers’ gaming preferences, teaching subjects, and years of teaching explained the heterogeneity of their GLA usage. This study contributes to a theoretical understanding of and methodological advancements in studying teachers’ usage intention and behaviour on GLA and yields practical implications for the design and implementation of GLA in K–12 classrooms.