A4 Refereed article in a conference publication
Analytics for the assessment of computational thinking
Authors: Bilbao, J.; Bravo, E.; García, O.; Rebollar, C.; Dagienė, V.; Masiulionytė-Dagienė, V.; Jankauskienė, A.; Laakso, M.-J.; Kaarto, H.; Lehtonen, D.; Parviainen, M.; Güven, I.; Gulbahar, Y.; Öztürk, T.; Tan Yenigün, N.; Pluhár, Z.; Sarmasági, P.; Rumbus, A.; Pears, A.
Editors: Gómez Chova, Luis; González Martínez, Chelo; Lees, Joanna
Conference name: International Technology, Education and Development Conference
Publication year: 2025
Journal: INTED proceedings
Book title : INTED2025 Proceedings
Volume: 19
First page : 6861
Last page: 6868
ISBN: 978-84-09-70107-0
ISSN: 2340-1087
eISSN: 2340-1079
DOI: https://doi.org/10.21125/inted.2025.1764
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : No Open Access publication channel
Web address : https://doi.org/10.21125/inted.2025.1764
Computational thinking is a fundamental competence in contemporary education, which enables individuals to approach problems in a logical and structured manner. This new competence is not only crucial for computer science professionals, but is also applicable in various disciplines and contexts of daily life. Computational thinking is essential in education because it fosters problem-solving skills, critical thinking, and creativity. In the digital age, computational thinking is not just a technical skill, but a way of thinking that can transform the way we approach the challenges and opportunities of the modern world. Assessing this skill requires precise analytical tools and methods. Assessing computational thinking is a complex process that requires a combination of qualitative and quantitative methods. Analytical rubrics, portfolio analysis, and standardized tests are essential tools that help provide a comprehensive and accurate assessment of students’ skills in this field. In our project, we also work to assess computational thinking using Bebras-type tasks and applying data analysis. Data analysis also facilitates the continuous improvement of teaching and assessment methods. By monitoring and analysing data over time, educators can identify which strategies are most effective and make adjustments to improve learning outcomes. Furthermore, data can help develop new tools and resources for teaching computational thinking. In this article, we present the assessment instrument, Comath, a research-based instrument with two rounds of piloting in six counties with subject-matter experts and over 4500 students and 100 teachers. We use tasks related to computational thinking, and we present some of the results obtained so far.