e1f8ca7d-bc93-407b-a6a0-0fd5181e402b20250606030953324naun:naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037131202513120251910.46300/9109.2025.19https://npublications.com/journals/educationinformation/2025.phpJavierBilbaoApplied Mathematics Department, University of the Basque Country, UPV/EHU, SpainEugenioBravoApplied Mathematics Department, University of the Basque Country, UPV/EHU, SpainOlatzGarcíaApplied Mathematics Department, University of the Basque Country, UPV/EHU, SpainCarolinaRebollarApplied Mathematics Department, University of the Basque Country, UPV/EHU, SpainMikko-JussiLaaksoUniversity of Turku, FinlandHeidiKaartoUniversity of Turku, FinlandDaraneeLehtonenUniversity of Turku, FinlandMarikaParviainenUniversity of Turku, FinlandAstaJankauskienėKlaipeda Gedminu Progymnasium LithuaniaArnoldPearsKTH Royal Institute of Technology, SwedenIsmailGüvenAnkara University, TurkeyYaseminGulbaharAnkara University, TurkeyTugbaÖztürkAnkara University, TurkeyNilüfer TanYenigünÖzkent Akbilek Middle School, TurkeyZsuzsaPluhárEötvös Loránd University, HungaryPálSarmaságiEötvös Loránd University, HungaryAnikóRumbusEötvös Loránd University, HungaryValentinaDagienėVilnius University, LithuaniaVaidaMasiulionytė-DagienėVilnius University, LithuaniaComputational thinking has gained an important place in modern education, enabling individuals to approach problem-solving in a logical and structured manner. This cross-curricular competence is important and applicable in any field of science, not just for computer science professionals. By fostering problem-solving, critical thinking, and creativity, among other skills, computational thinking is crucial in today's education. In the digital age, computational thinking is not just a technical skill, or one related to programming and robotics, but a way of thinking that can transform, or at least provide a different perspective, the way we approach everyday challenges and opportunities in our daily lives. To assess this new competency, analytical tools and methods that are not too general are needed. To achieve this, that is, to assess computational thinking, the process is currently complex and requires a combination of qualitative and quantitative methods. In this way, analytical rubrics, portfolio analysis, and standardized tests are essential tools that help provide a comprehensive and accurate assessment of students' skills related to this competence. In our project, we also work on assessing computational thinking using Bebras-type tasks and applying data analysis. Data analysis facilitates the continuous improvement of teaching and assessment methods. By monitoring and analyzing data over time, educators can identify the most effective strategies and make adjustments to improve learning outcomes. In this paper, we introduce COMATH, an assessment tool grounded in research, which has undergone two phases of piloting across six counties. This process involved collaboration with subject-matter experts and the participation of over 4500 students and 100 teachers. We employ tasks designed to evaluate computational thinking and share some of the findings we have gathered to date.5302025530202553616https://npublications.com/journals/educationinformation/2025/a142008-007(2025).pdf10.46300/9109.2025.19.6https://npublications.com/journals/educationinformation/2025/a142008-007(2025).pdf10.1007/s11423-015-9394-5R. Kimmons, B. G., Miller, J., Amador, C. D., Desjardins, C. Hall, “Technology integration coursework and finding meaning in pre-service teachers’ reflective practice,” Educational Technology Research and Development, Vol. 63, 2015, pp. 809- 829. https://doi.org/10.1007/s11423-015-9394-5. 10.33022/ijcs.v13i6.4465I. Saputra, A. Kurniawan, M. Yanita, E. Y. Putri, M. Mahniza, “The Evolution of Educational Assessment: How Artificial Intelligence is Shaping the Trends and Future of Learning Evaluation,” The Indonesian Journal of Computer Science, Vol. 13, nº 6, 2024, pp. 9056- 9074. https://doi.org/10.33022/ijcs.v13i6.4465. 10.34105/j.kmel.2012.04.020K. D. Mattingly, M. C. Rice, Z. L. Berge, “Learning analytics as a tool for closing the assessment loop in higher education,” Knowledge Management & ELearning, Vol. 4, nº 3, 2012, pp. 236. https://doi.org/10.34105/j.kmel.2012.04.020. 10.1111/bjet.12720A. Larrabee Sønderlund, E. Hughes, J. Smith, “The efficacy of learning analytics interventions in higher education: A systematic review,” British Journal of Educational Technology, Vol. 50, nº 5, 2019, pp. 2594- 2618. https://doi.org/10.1111/bjet.12720. 10.1080/13562517.2013.827653D. Clow, “An overview of learning analytics,” Teaching in Higher Education, Vol. 18, nº 6, 2013, pp. 683-695. https://doi.org/10.1080/13562517.2013.827653. 10.3390/app11010237A. Namoun, A. Alshanqiti, “Predicting student performance using data mining and learning analytics techniques: A systematic literature review,” Applied Sciences, Vol. 11, nº 1, 2020, p. 237. https://doi.org/10.3390/app11010237. 10.1007/s10964-020-01289-9C. Tejada-Gallardo, A. Blasco-Belled, C. TorrellesNadal, C. Alsinet, “Effects of school-based multicomponent positive psychology interventions on well-being and distress in adolescents: A systematic review and meta-analysis,” Journal of youth and adolescence, Vol. 49, nº 10, 2020, pp. 1943-1960. https://doi.org/10.1007/s10964-020-01289-9. 10.1016/j.compedu.2021.104192A. Silvola, P. Näykki, A. Kaveri, H. Muukkonen, “Expectations for supporting student engagement with learning analytics: An academic path perspective,” Computers & Education, Vol. 168, 2021, pp. 104192. https://doi.org/10.1016/j.compedu.2021.104192. 10.4018/978-1-7998-7103-3A. Azevedo, J. M. Azevedo, J. O. Uhomoibhi, E. S. I. Ossiannilsson, Advancing the power of learning analytics and big data in education, IGI Global, 2021. 10.1007/s10758-024-09773-yM. Bayrak Karsli, S. Cilligol Karabey, E. Kaba, M. Guler, M. Aydemir Arslan, E. Kursun, “Research Trends in Teaching Analytics: Bibliometric Mapping and Content Analysis,” Technology, Knowledge and Learning, 2024, pp. 1-25. https://doi.org/10.1007/s10758-024-09773-y. 10.1007/s12528-021-09299-7S. Aljawarneh, J. A. Lara, “Data science for analyzing and improving educational processes,” Journal of Computing in Higher Education, Vol. 33, 2021, pp. 545-550. https://doi.org/10.1007/s12528-021-09299-7 10.1007/s10639-023-12033-8A. Stojanov, B. K. Daniel, “A decade of research into the application of big data and analytics in higher education: A systematic review of the literature,” Education and Information Technologies, Vol. 29, 2024, pp. 5807-5831. https://doi.org/10.1007/s10639- 023-12033-8. 10.15460/eder.5.2.1680D. Lehtonen, “Constructing a design framework and design methodology from educational design research on real-world educational technology development,” Educational Design Research, Vol. 5, nº 2, 2021, pp. 1- 29. https://doi.org/10.15460/eder.5.2.1680. 10.1109/isie.2018.8433753R. Kanth, M. J. Laakso, P. Nevalainen, J. Heikkonen, “Future educational technology with big data and learning analytics,” in 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), Cairns, QLD, Australia, 2018, pp. 906-910. 10.1002/9781118956588.ch16R. S. Baker, T. Martin, L. M. Rossi, Educational data mining and learning analytics, The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications, 2016, pp. 379-396. J.M. Wing, “Computational thinking,” Communications of the ACM, Vol. 49, no. 3, 2006, pp. 33-35. https://doi.org/10.1145/1118178.1118215. 10.15388/infedu.2021.21P.J. Denning, M. Tedre, “Computational thinking: A disciplinary perspective,” Informatics in Education, Vol. 20, nº 1, 2021, pp. 361-390. https://doi.org/10.15388/infedu.2021.21. 10.1098/rsta.2008.0118J.M. Wing, “Computational thinking and thinking about computing,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 366, nº 1881, 2008, pp. 3717-3725. https://doi.org/10.1098/rsta.2008.0118. 10.46300/9109.2024.18.2J. Bilbao, E. Bravo, O. Garcia, C. Rebollar, M.J. Laakso, H. Kaarto, D. Lehtonen, M. Parviainen, A. Jankauskiene, A. Pears, I. Güven, Y. Gulbahar, F.Ö. Öncül, N. Tan Yenigün, Z. Pluhár, P. Sarmasági, V. Dagiene, V. Masiulionyte-Dagiene, “Introducing computational thinking and algebraic thinking in the European educational systems,” International journal of education and information technologies, Vol. 18, 2024, pp. 11-19. https://doi.org/10.46300/9109.2024.18.2. 10.1186/s43031-021-00033-yN. Shin, J. Bowers, J. Krajcik, D. Damelin, “Promoting computational thinking through project-based learning,” Disciplinary and Interdisciplinary Science Education Research, Vol. 3, 2021, pp. 1-15. https://doi.org/10.1186/s43031-021-00033-y. 10.1007/s10763-021-10227-5C. Wang, J. Shen, J. Chao, “Integrating computational thinking in STEM education: A literature review,” International Journal of Science and Mathematics Education, Vol. 20, 2022, pp. 1949-1972. https://doi.org/10.1007/s10763-021-10227-5. 10.1145/2576872A. Yadav, C. Mayfield, N. Zhou, S. Hambrusch, J.T. Korb, “Computational thinking in elementary and secondary teacher education,” ACM Transactions on Computing Education (TOCE), Vol. 14, nº 1, 2014, pp. 1-16. https://doi.org/10.1145/2576872. 10.1111/bjet.13256S.C. Kong, M. Lai, “Effects of a teacher development program on teachers' knowledge and collaborative engagement, and students' achievement in computational thinking concepts,” British journal of educational technology, Vol. 54, nº 2, 2023, pp. 489- 512. https://doi.org/10.1111/bjet.13256. 10.15388/infedu.2016.02V. Dagiene, G. Stupuriene, “Bebras - A Sustainable Community Building Model for the Concept Based Learning of Informatics and Computational Thinking,” Informatics in education, Vol. 15, nº 1, 2016, pp. 25-44. https://doi.org/10.15388/infedu.2016.02. 10.1002/cae.22442D. Hooshyar, “Effects of technology-enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking,” Computer Applications in Engineering Education, Vol 30, nº 1, 2022, pp. 64-76. https://doi.org/10.1002/cae.22442. 10.1109/fie49875.2021.9637416A. Pears, M. Tedre, T. Valtonen, H. Vartiainen, “What Makes Computational Thinking so Troublesome?” in 2021 IEEE Frontiers in Education Conference (FIE), Lincoln, NE, USA, 2021, pp. 1–7. J. Bilbao, E. Bravo, O. Garcia, C. Rebollar, “Introducing Computational Thinking in Calculus for Engineering,” International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Vol. 58, 2024, pp. 239-246. https://doi.org/10.48550/arXiv.2502.10400.