Informatics in Education

Computational thinking (CT) is widely recognized as a key 21st-century competence, yet its integration across disciplines remains unclear for many educators. This study explores how prospective teachers identify and express CT through scripts representing computational processes in school subjects of their choice. The challenge of integrating CT in teacher preparation programs in non-STEM-related fields is also addressed. Using a mixed-methods approach, we analyze projects and accompanying reflective analyses from 375 prospective teachers who created Scratch-based scripts aligned with computational processes in STEM and non-STEM subjects. Data analysis yielded a taxonomy of pedagogical strategies reflecting diverse instructional approaches. The study underscores the value of guided, discipline-specific CT activities in teacher preparation programs and highlights how script development of computational processes fosters both subject-matter understanding and computational thinking. The results suggest holistic lens in evaluating CT integration and offer evidence-based insights for embedding CT meaning-fully into teacher preparation programs across disciplines.

Computational Thinking (CT) is widely recognised as a transversal competence essential for learning, problem solving, and knowledge transfer across disciplines. However, its effective integration into school education remains strongly dependent on the availability of assessment instruments that are pedagogically meaningful, psychometrically sound, and applicable across diverse educational contexts. This paper presents COMATH, a cross-national assessment instrument designed to evaluate CT in students aged 9–14. The instrument adopts a phase-based development and validation framework that integrates Bebras-inspired tasks, Item Response Theory, factor-analytic methods, learning analytics, and teacher and student feedback. The assessment was iteratively developed and piloted between 2023 and 2025 in six European countries, with data collected from 6,480 students and 155 teachers. The findings demonstrate that a phased assessment approach enables systematic calibration of task difficulty, robust evaluation of item functioning, and meaningful interpretation of student performance across age groups and national contexts. The results further highlight how well-designed CT assessment can support instructional decision-making rather than serve solely as a summative measure. The study argues for conceptualising CT assessment as a dynamic and iterative process that links measurement, psychometric validation, and pedagogical use in school education.

This study explores the application of large language models (LLMs) to create computational thinking tasks for the Bebras International Challenge through a single-case study approach. Using exemplar-based prompting with seven authentic Bebras tasks from the 2024 cycle as contextual input, a task was developed that was subsequently accepted for inclusion in the 2025 international Bebras challenge. Comparison with the exemplar tasks confirmed that the generated content drew from multiple sources rather than replicating any single task, combining grid-based constraint satisfaction, rule-based filtering, and logical deduction into a novel navigation puzzle with engaging narrative context. International expert reviewers evaluated the task using established Bebras quality criteria, confirming successful alignment with core pedagogical requirements including age-appropriateness, clarity, and cultural neutrality. However, two significant gaps emerged in the broader authoring workflow: accessibility compliance in the researcher-authored visual components and technical inaccuracies in the LLM-generated informatics framing. Following collaborative revision by international editors that addressed these concerns while preserving the LLM’s creative contributions, the task achieved acceptance for international use. The findings reveal a collaborative pipeline comprising contextual preparation, LLM-guided generation, human technical implementation, expert community review, and collaborative revision. Results from this case suggest that LLMs can efficiently generate educationally sound creative foundations while requiring integrated human expertise to meet specialised standards and ensure inclusive design, with the task’s acceptance providing encouraging evidence for the viability of this collaborative approach.

While graph theory plays a foundational role in informatics and computational thinking (CT), its instruction in elementary education remains underexplored, particularly through embodied or arts-based methods. This study examines a low-tech, psychodramabased pedagogical intervention designed to introduce graph theory as a data structure to fifth-grade students in a Brazilian public school. Students engaged in dramatizing connections and structural changes within friendship networks, enabling experiential learning of concepts such as adjacency, traversal, and modification of graph-like structures. Data were collected through teacher interviews, classroom observations, and post-intervention assessments. Findings indicate strong student engagement, symbolic appropriation of key graph concepts, and the development of abstraction and reflection skills central to computational thinking. These results suggest that educational psychodrama offers a culturally responsive, embodied strategy for introducing core CT concepts in early education, expanding the repertoire of practices in computing education.

The introductory programming disciplines, which include the teaching of algorithms and computational logic, have high failure and dropout rates. Developing Computational Thinking in students can contribute to learning programming fundamentals by building algorithmic and problem-solving skills. However, keeping students engaged in training such skills is still a challenge. In this sense, this work proposes an intervention for teaching Computational Thinking in the initial semesters of the Technician in Informatics and Bachelor of Computer Science courses, using gamification as a motivational strategy and the Quizizz software as a gamified platform. To evaluate the results, a mixed-method case study was used to perform a quantitative and qualitative analysis of the data and, subsequently, integrate them. The results obtained were discussed based on the Theory of Self-Determination, indicating that students demonstrated a high level of oriented autonomy and motivation to learn, regardless of the performance obtained.

Tables are fundamental tools for handling data and play a crucial role in developing both computational thinking (CT) and mathematical thinking (MT). Despite this, they receive limited attention in research and design. This study investigates pupils’ attitudes toward and approaches to working with tables in informatics education, focusing on the systematic development of CT. Specifically, we examine how primary school pupils (aged 8–10) think and act when engaging with two-dimensional frequency tables integrated into Programming with Emil. Our objective is to deepen the understanding of pupils’ cognitive processes when entering data into tables and interpreting their contents. Data were collected through individual semi-structured interviews and analysed using qualitative inductive coding. Identified codes were then iteratively consolidated into broader categories and final themes. Key findings include: (a) the opportunity to solve a problem by programming a character is highly motivating for pupils, and (b) the appropriate integration of different contexts and concepts, such as tables, into an engaging programming environment has the potential to foster advanced cognitive skills beyond CT.

Algebraic Thinking (AT) and Computational Thinking (CT) are pivotal competencies in modern education, fostering problem-solving skills and logical reasoning among students. This study presents the initial hypotheses, theoretical framework, and key steps undertaken to explore characterized learning paths and assign practice-relevant tasks. This article investigates the relationship between AT and CT, their parallel development, and the creation of integrated learning paths. Analyses of mathematics and computer science/informatics curricula across six countries (Finland, Hungary, Lithuania, Spain, Sweden, and Türkiye) informed the development of tasks aligned with consolidated national curricula. Curricula were analysed using statistical methods, and content analysis to identify thematic patterns. To validate the effectiveness of the developed tasks for AT and CT, an assessment involving 208 students in K-12 across various grade levels (students aged 9–14) was conducted, with results analysed both statistically and qualitatively. Subsequently, a second quantitative study was carried out among teachers participating in a workshop, providing further insights into the practical applicability of the tasks. The research process was iterative, encompassing cycles of analysis, synthesis, and testing. The study also paid special attention to unplugged activities – tasks that help students learn CT without using computers or digital tools. A local workshop in Hungary, where 26 tasks were tested with students from different grade levels, showed that developing CT and AT effectively requires more time and practice, especially in key topics. The findings underscore the importance of integrating AT and CT through thoughtfully designed learning paths and tasks, including unplugged activities, to enhance students’ proficiency in these areas. This study contributes to the development of innovative educational programs that address the evolving digital competencies required in contemporary education.

The assessment of computational thinking (CT) is crucial for improving pedagogical practice, identifying areas for improvement, and implementing efficient educational interventions. Despite growing interest in CT in primary education, existing assessments often focus on specific dimensions, providing a fragmented understanding. In this research, a CT system of assessments for primary education was assembled and applied in a cross-sectional survey study with 1306 students from the 6th grade in a region of Spain. A three-way ANOVA and correlation analyses explored the effects of programming experience, educational context, and gender on CT skills and self-efficacy. Results highlighted a significant effect of programming experience but no significant effects of context or gender, alongside low overall correlations between CT skills and self-efficacy. These findings highlight the need to avoid focusing CT assessments on a single variable and support the combined use of multiple assessment instruments to measure CT accurately and effectively.

This study aims to provide a descriptive and bibliometric analysis of the trend of artificial intelligence (AI) application in the development of computational thinking (CT) skills in publications from 2007 to 2024. A total of 191 articles were obtained from Scopus database with certain keywords, and analyzed using Biblioshiny and VOSviewer. The results show that publications fluctuated in 2007–2014, then increased sharply since 2019, with a compound annual growth rate (CAGR) of 22.8% in the period 2019–2024. Early publications received the highest number of citations, such as in 2007 (18 citations), while recent studies show a more even distribution of citations, reflecting a shift from basic to applied research. This analysis highlights the important role of AI in enhancing CT development through learning strategies, educational technology, and cross-disciplines. The impact of AI implementation is seen in various aspects of education, such as learning strategies, educational media, and the relationship between CT and other skills. These findings demonstrate the importance of leveraging AI to support the development of CT in education, which can improve the quality of learning and enrich educational experiences globally.

Research on collaborative learning of computer science has been conducted primarily in programming. This paper extends this area by including short tasks (such as those used in contests like the Bebras Challenge) that cover many other computer science topics. The aim of this research is to explore how problem-solving in pairs differs from individual approaches when tackling contest tasks.