Informatics in Education

In this article, we examine a case study of the Bachelor’s degree programme “Computer Science” at the University of Latvia. We explore several factors that enabled it to (a) obtain the European Informatics Quality Label three times, (b) be ranked first in the national employer survey as the most recommended educational Programme for nine years, and (c) adopt a student-centred approach. Using a case study methodology, we highlight several innovations that together make the Programme highly regarded both academically and in the labour market. At the end of the paper, we divide the key outcomes of the study into two sets of innovations. National-level solutions, such as learning outcome comparison and the development of industry terminology with student participation, are important primarily in the local context. Whereas (a) the framework for gaining both industry and academic experience through the Practice Course and Qualification thesis, and (b) curriculum expansion with Special Seminars and the creation of opportunities for students to acquire additional knowledge through Excellence Studies and Remedial Courses, can be transferred internationally.

This article examines pre-service teachers’ data agency, defined as the ability to act according to one’s own values and goals rather than being directed by algorithmic systems. Data agency involves understanding how computational systems, such as algorithms, data-driven profiling, and platform infrastructures, collect, process, and use data, and how these practices shape individuals and society. This article introduces a self-assessment instrument developed to measure data agency and applies it to a sample of 163 Finnish pre-service teachers. The findings show that pre-service teachers evaluated their competencies across different dimensions of data agency rather cautiously. The study highlights the importance of strengthening future teachers’ understanding of the mechanisms behind algorithmic and data-driven decision-making. Such knowledge is increasingly essential for preparing future teachers to address challenges related to datafication, including commercial data collection and algorithmic influencing in contemporary 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.

Computer science (CS) students are expected to grasp numerous CS concepts during their CS education. Researchers have previously pointed to some concepts that are challenging for many students to conquer during their education. In this study, we investigate how CS students encounter indirection, scope, references, and parameter transfer during their studies. We focus on the first three study years, as previous studies have indicated that students do not significantly improve their grasp of these concepts during that time. We surveyed the teachers of courses in three CS study programs, exploring teachers’ perspectives on students’ knowledge of the concepts and how explicitly the concepts are taught and graded. Our investigation highlights several ways in which curricula diverge from previous recommendations and how an understanding of these study programs can support learning outcomes.

Debugging is a vital but challenging skill for beginner programmers to learn. It is also a difficult skill to teach. For secondary school teachers, who may lack time or programming experience, honing students’ understanding of debugging can be a daunting task. Despite this, little research has explored their perspectives of debugging. To this end, we investigated secondary teachers’ experiences of debugging in the classroom, with a focus on text-based programming. Through thematic analysis of nine semi-structured interviews, we identified a common reliance on the teacher for debugging support, embodied by many raised hands. We call this phenomenon the ‘hands-up problem’. While more experienced and confident teachers discussed strategies they use to counteract this, less confident teachers discussed the negative consequences of this problem. We recommend further research into debugging-specific pedagogical content knowledge and professional development to help less confident teachers develop approaches for supporting their students with debugging.

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.

Ethical thinking and reasoning is considered a core component of artificial intelligence (AI) literacy. However, there is a lack of strategies and assessments to promote and measure students’ ethical thinking in AI, particularly in K-12 education. In this paper, we discuss how RAICA, a project-based AI literacy curriculum integrates ethics into its framework and instructional resources. We employ a mixed-methods convergent design to obtain different yet complementary data on ethical thinking as an outcome mediated by diverse RAICA materials. Data analysis revealed that teachers utilized a variety of instructional strategies to foster students’ ethical thinking and that students actively engaged in ethical thinking activities, resulting in a developing understanding of stakeholders and potential benefits/harms of AI. Our work makes a key contribution to AI education by providing empirical evidence to support mechanisms for integration and assessment of ethical thinking within AI literacy curricula.

The integration of Artificial Intelligence (AI) literacy into college curricula is a pressing but complex challenge, particularly in non-technical fields like business. This paper presents a case study of a pedagogical intervention designed to embed AI literacy within a required college mathematics course. The intervention employed a Problem-Based Learning (PBL) framework where 120 business students used tools like ChatGPT and matrix calculators to solve an authentic data-driven business problem. We analyzed data from the final assessment artifact – a digital magazine – to evaluate the development of specific AI literacy competencies. Initial findings from this pilot implementation indicate that the PBL approach was effective in developing students’ skills in data-driven argumentation, critical evaluation of AI-generated outputs, and the ability to connect abstract mathematical models to practical AI applications. The study demonstrates a promising replicable model for integrating AI literacy into foundational courses, but also highlights key challenges, including the need for explicit scaffolding in critical AI evaluation. This paper contributes empirical insights from an initial implementation, offering a practical framework and actionable lessons for educators designing AI literacy curricula.