metacognitive-strategies
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- PaperJournal of Learning Analytics14 Mar 2026
Profiling Pre-service Teachers’ Computational Thinking
Tanya Chichekian, Maria Cutumisu, Annie Savard, Yi-Mei Zhang
Researchers used multimodal data and latent profile analysis to identify three computational thinking profiles (Novice, Developing, Proficient) among 128 pre-service teachers. They found that metacognitive strategies and prior coding experience predict profile membership, and that proficient learners were more task efficient with fewer perceived challenges.
Original abstract
Computational thinking (CT) is a vital skill set for pre-service teachers who will need to foster computational literacy in K–12 classrooms, yet the factors influencing their CT skills remain less understood than those for K–12 students or in-service teachers. This study leverages multimodal data to investigate how pre-service teachers (n=128) differ in CT skills, the predictive role of metacognitive strategies and prior coding experience, and variations in online behaviours. Using latent profile analysis, we identified three profiles based on digital literacy, problem-solving, and coding comfort (Novice, Developing, and Proficient), revealing heterogeneity in CT, and supporting non-linear skill acquisition. Linear discriminant analysis revealed that metacognitive strategies and prior coding experience significantly predict profile membership, validating the interplay of technical and cognitive factors in the development of CT skills. Behavioural data from an interactive problem-solving task showed that, compared to Novices and Developing learners, Proficient learners were more task efficient and perceived fewer challenges during task completion. Implications for designing a learning analytics dashboard to visualize profiles and behavioural metrics to support adaptive, equitable, and personalized teacher training are discussed, thereby enhancing pre-service teachers’ readiness to integrate CT into K–12 education.