Profiling Pre-service Teachers’ Computational Thinking
The Role of Metacognition and Coding Experience
DOI:
https://doi.org/10.18608/jla.2026.9077Keywords:
computational thinking, pre-service teachers, prior coding experience, metacognitive strategies, latent analysis, research paperAbstract
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.
References
Aldemir, T., Moon, J., Bicer, A., Byun, G., Perez, C., & Sabanwar, V. (2025). How non-CS pre-service teachers conceptualize and engage with computational thinking through 3D game design: Insights from an XR-enhanced maker experience. Computers & Education: X Reality, 6, Article 100099. https://doi.org/10.1016/j.cexr.2025.100099
Argelagós, E., Garcia, C., Privado, J., & Wopereis, I. (2022). Fostering information problem solving skills through online task-centred instruction in higher education. Computers & Education, 180, Article 104433. https://doi.org/10.1016/j.compedu.2022.104433
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905
Bers, M. U. (2017). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge. https://doi.org/10.4324/9781315398945
Bower, M., & Falkner, K. (2015). Computational thinking, the notional machine, pre-service teachers, and research opportunities. In Proceedings of the 17th Australasian Computing Education Conference (ACE 2015) (pp. 37–46). Australian Computer Society.
Butler, D., & Leahy, M. (2021). Developing pre-service teachers’ understanding of computational thinking: A constructionist approach. British Journal of Educational Technology, 52(3), 1060–1077. https://doi.org/10.1111/bjet.13090
Chaabi, H., Azmani, A., & Dodero, J. M. (2025). Bridging computational thinking research and practice of pre-service and in-service teachers. Humanities and Social Sciences Communications, 12, Article 666. https://doi.org/10.1057/s41599-025-04895-9
Chan, S.-W., Looi, C.-K., Ho, W. K., Huang, W., Seow, P., & Wu, L. (2021). Learning number patterns through computational thinking activities: A Rasch model analysis. Heliyon, 7(9), Article e07922. https://doi.org/10.1016/j.heliyon.2021.e07922
Cheng, Y.-P., Lai, C.-F., Chen, Y.-T., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2023). Enhancing student’s computational thinking skills with student-generated questions strategy in a game-based learning platform. Computers & Education, 200, Article 104794. https://doi.org/10.1016/j.compedu.2023.104794
Chichekian, T., Cutumisu, M., & Savard, A. (2025). Preservice teachers’ computational thinking profiles. In A. Rajala, A. Cortez, R. Hofmann, A. Jornet, H. Lotz-Sisitka, & L. Markauskaite (Eds.), Proceedings of the 19th International Conference of the Learning Sciences (ICLS 2025), 10–13 June 2025, Helsinki, Finland (pp. 47–55). International Society of the Learning Sciences.
Cutumisu, M., Adams, C., Yuen, C., Hackman, L., Lu, C., & Samuel, M. (2019). Callysto Computational Thinking Test (CCTt) Teacher Version [Measurement instrument]. https://callysto.ca
Dagienė, V., & Futschek, G. (2008). Bebras international contest on informatics and computer literacy: Criteria for good tasks. In R. T. Mittermeir & M. M. Sysło (Eds.), Informatics education - Supporting computational thinking (pp. 19–30). Springer. https://doi.org/10.1007/978-3-540-69924-8_2
Dong, W., Li, Y., Sun, L., & Liu, Y. (2024). Developing pre-service teachers’ computational thinking: A systematic literature review. International Journal of Technology and Design Education, 34(1), 191–227. https://doi.org/10.1007/s10798-023-09811-3
Ferguson, R. (2014). Learning analytics: Drivers, developments and challenges. Italian Journal of Educational Technology, 22(3), 138–147. https://www.learntechlib.org/p/183381/
Ferguson, S. L., Moore, E. W. G., & Hull, D. M. (2020). Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. International Journal of Behavioral Development, 44(5), 458–468. https://doi.org/10.1177/0165025419881721
Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Hoyles, C., & Noss, R. (2015). A computational lens on design research. ZDM Mathematics Education, 47(6), 1039–1045. https://doi.org/10.1007/s11858-015-0731-2
Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary pre-service teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26(2), 175–192. https://doi.org/10.1007/s10956-016-9663-z
Knight, S., Wise, A. F., Ochoa, X., & Hershkovitz, A. (2017). Learning analytics: Looking to the future. Journal of Learning Analytics, 4(2), 1–5. https://doi.org/10.18608/jla.2017.42.1
Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3(2), 154–171. https://doi.org/10.1007/s40751-017-0031-2
Liao, C. H., Chiang, C.-T., Chen, I.-C., & Parker, K. R. (2022). Exploring the relationship between computational thinking and learning satisfaction for non-STEM college students. International Journal of Educational Technology in Higher Education, 19(1), Article 43. https://doi.org/10.1186/s41239-022-00347-5
Liu, Z., Gearty, Z., Richard, E., Orrill, C. H., Kayumova, S., & Balasubramanian, R. (2024). Bringing computational thinking into classrooms: A systematic review on supporting teachers in integrating computational thinking into K-12 classrooms. International Journal of STEM Education, 11(1), Article 51. https://doi.org/10.1186/s40594-024-00510-6
Mouza, C., Yang, H., Pan, Y.-C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3), 61–76. https://doi.org/10.14742/ajet.3521
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024
Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 46–55). ACM Press. https://doi.org/10.1145/3027385.3027406
Ramirez-Salgado, A., Wusylko, C., Weisberg, L., Delgado, J., & Israel, M. (2025). Preparing elementary pre-service teachers to integrate computational thinking in the curriculum. ACM Transactions on Computing Education, 24(4), Article 42. https://doi.org/10.1145/3744751
Román-González, M., Moreno-León, J., & Robles, G. (2019). Combining assessment tools for a comprehensive evaluation of computational thinking interventions. In S.-C. Kong & H. Abelson (Eds.), Computational thinking education (pp. 79–98). Springer. https://doi.org/10.1007/978-981-13-6528-7_6
Sadik, O., Ottenbreit-Leftwich, A., & Nadiruzzaman, H. (2017). Computational thinking conceptions and misconceptions: Progression of pre-service teacher thinking during computer science lesson planning. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 221–238). Springer. https://doi.org/10.1007/978-3-319-52691-1_14
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, Article 103798. https://doi.org/10.1016/j.compedu.2019.103798
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509. https://doi.org/10.1177/0002764213479363
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Wise, A. F., & Schwarz, B. B. (2017). Visions of CSCL: Eight provocations for the future of the field. International Journal of Computer-Supported Collaborative Learning, 12(4), 423–467. https://doi.org/10.1007/s11412-017-9267-5
Wu, T.-T., Asmara, A., Huang, Y.-M., & Permata Hapsari, I. (2024). Identification of problem-solving techniques in computational thinking studies: Systematic literature review. SAGE Open, 14(2). https://doi.org/10.1177/21582440241249897
Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K–12 classrooms. TechTrends, 60, 565–568. https://doi.org/10.1007/s11528-016-0087-7
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution 4.0 International License.
