The Landscape of Computational Thinking Assessment in Primary Education: A Critical Review of Tools and Research Trends

Document Type : Original Article

Authors

1 Ph.D. Student, Department of Educational Psychology, Faculty of Education and Psychology, Alzahra University, Tehran, Iran. E

2 Associate Professor, Department of Educational Psychology, Faculty of Education and Psychology, Alzahra University, Tehran, Iran.

3 Associate Professor of Mathematics Education, Department of Mathematics Education, Faculty of Mathematics and Computer- Shahid Bahonar University of Kerman & Mahani Math Center, Afzalipour Research Institute, Shahid Bahonar University of Kerman, Kerman, Iran.Kerman, Iran

Abstract
Objective: Computational thinking is a key competency in primary education; however, its valid measurement faces serious conceptual and psychometric challenges. Beyond a mere classification of tools, this study presents a diagnostic analysis of the field through a dual approach: first, mapping the scientific landscape via scientometric analysis; and second, critically evaluating existing assessment tools.
Method: In this systematic critical review (PRISMA 2020), 45 eligible articles were identified by searching Scopus and Google Scholar databases (2006-2025). Analyses were conducted in two phases: 1) Scientometric analysis to map the conceptual structure of the field using R and VOSviewer software, and 2) Qualitative critical analysis of 8 selected key tools regarding their theoretical foundations and psychometric properties.
Results: The scientometric analysis revealed a rapidly growing but unbalanced field. The keyword co-occurrence map indicated a strong focus on "educational applications" and "programming tools," contrasted by a significant absence of assessment concepts such as "validity" and "fairness." This structural gap is directly reflected in the tools, revealing three main limitations: 1) Psychometric weakness due to reliance on traditional metrics (Cronbach's alpha) and neglect of modern standards (such as Omega coefficient and DIF analysis); 2) Conceptual limitations by focusing on algorithmic knowledge while ignoring process skills (debugging) and attitudinal aspects (perseverance); and 3) An instrumental gap for upper primary students.
Conclusions: Based on the findings, no single tool is sufficient for the comprehensive assessment of computational thinking. The maturity of this field requires adopting modern psychometric standards such as the Omega reliability coefficient, designing and localizing tests for primary students in Iran, and creating hybrid assessment frameworks that integrate standardized tests with performance-based assessments. This integrated approach will lead to more accurate skill diagnosis and the design of more effective educational programs.

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