A latent profile analysis of teachers’ knowledge about and perceived usefulness of computational thinking and how teacher profiles relate to student achievement

Teacher competencies encompass the dimensions of knowledge and attitudes, yet few studies have analysed teachers' latent profiles in relation to the interaction between these two dimensions. Latent profile analysis, which examines the interplay between variables to distinguish subgroups of individuals, has recently emerged in studies of computational thinking (CT) as a method for identifying student profiles. However, the latent profiles of CT teachers have not yet been analysed. We therefore analysed the latent profiles of a sample of 493 teachers who had participated in a teacher development programme in CT, according to their knowledge and perceived usefulness of CT. Three profiles were identified: (1) highly knowledgeable with high perceived usefulness, (2) knowledgeable with low perceived usefulness, and (3) less knowledgeable with low perceived usefulness. Qualitative data further triangulated these findings. We also examined the learning achievement of 12,105 students taught by these teachers. Students taught by highly knowledgeable teachers with a high perceived sense of usefulness demonstrated significantly better achievement than their peers. Our findings highlight the importance of nurturing both knowledge of and perceived usefulness in CT. They also provide valuable insights for similar teacher development programmes, emphasizing the importance of peer mentoring support for less knowledgeable teachers. Additionally, we underscore the value of having teachers create artefacts using CT for instructional purposes, allowing them to gain firsthand experience of its usefulness. As few studies have explored teachers' latent profiles based on the dimensions of knowledge and attitudes, the contribution of this study extends beyond the domain of CT.