Informatics in Education

Source code comments are usually treated in software engineering as documentation artifacts that support readability, maintainability, and long-term comprehension. In programming education, however, comments may also function as pedagogical scaffolds by helping learners externalize reasoning, clarify intent, and reflect on code. This article presents a PRISMA-informed qualitative systematic review with layered evidence on the pedagogical role of source code comments in programming education. Searches in Scopus and ERIC produced 50 unique records; 36 were assessed in detail and resolved into 18 primary synthesis studies, 7 supporting/contextual studies, and 11 advanced-stage exclusions. Because the evidence base is heterogeneous, the review uses qualitative layered synthesis rather than meta-analysis. The findings suggest that comments are best understood pedagogically as explanation-centered learning supports, especially for code comprehension, self-explanation, debugging and reflective reasoning, and formative assessment of student thinking. Direct comment-focused evidence remains limited and concentrated mainly in highereducation and novice programming contexts; adjacent explanation-centered studies clarify plausible mechanisms but do not by themselves establish comment-specific effects. The review concludes that comments can become pedagogically meaningful when deliberately integrated as scaffolds for explanation, comprehension, and reflection.

Debugging is integral to programming. It comes into play as soon as novices make their first mistakes in creating programming artifacts. It is also consistently reported to be a skill that is difficult to learn as well as to teach effectively. Research in Informatics Education has often focused on the process of debugging, by breaking it down in steps connected by temporal and causal dependencies. In this work, we focus instead on debugging as a skill, from the standpoint of Cognitive Load Theory, and break it down into a tree-shaped model of subskills that enable one another. Debugging may thus be seen as a meta-skill that requires the coordination of multiple others. From the standpoint of Cognitive Load Theory, such a skill is cognitively expensive, which may explain the learning-related difficulties tied to debugging. Using the framework of the four-component instructional design, we hypothesize a categorization of each debugging subskill as either recurrent or nonrecurrent, dividing those that are applied consistently to different contexts from those that require problem solving. All subskills may be practised and potentially assessed with targeted exercises, whose design depends on their recurrent/nonrecurrent nature. We provide extensive examples of such exercises. Our decomposition of debugging into subskills is a novel way to address debugging in educational contexts and complements the work done on debugging processes. Although it is currently a theoretically grounded conjecture, the model provides concrete guidance for instructors on analyzing existing materials and planning cognitive-load-informed learning trajectories.

The integration of computational thinking (CT) into mathematics education has attracted increasing attention; however, there is limited methodological clarity about how CT can be systematically embedded in primary mathematics instruction. In particular, empirical research linking CT to multi-step mathematical problem-solving remains scarce. This paper presents the design and implementation of a task-based intervention model developed within the DigiMaths4All project. The model incorporates CT into primary mathematics through a structured framework for task selection and design, combined with implementation in a technology-enhanced learning environment (ViLLE). Mathematical tasks focus on arithmetic fluency and multi-step problem solving, while CT tasks are tailored to operationalize key processes such as decomposition, abstraction, algorithmic thinking, and debugging. The intervention was carried out through a class-based randomized controlled trial in primary education, comparing traditional instruction with technology-supported approaches, including an integrated mathematics CT model. The study uses a mixed-methods approach, incorporating assessments and learning analytics data to examine implementation processes. The main contribution of this paper is methodological. It offers a replicable framework for (1) designing interventions that incorporate CT into mathematics, (2) selecting and constructing aligned task sets, and (3) implementing these tasks within an analytics-driven digital environment. The findings enhance understanding of how CT can be operationalized to support mathematical problem solving in primary education.

This study addresses an implementation problem for informatics education: whether teachers’ reported participation in AI-related professional learning, interpreted as realised access to one professional learning condition for teacher AI literacy, is associated with declared need or instead follows existing patterns of digital, professional and organisational advantage. The study does not measure teacher AI literacy, AI competence, computing teaching practice, computational-thinking instruction or classroom implementation directly. Rather, it analyses reported participation in AI-related professional learning as a realised opportunity condition for developing teacher AI literacy at scale. Using TALIS 2024 data from 108,136 lower-secondary teachers nested within 10,840 schools across 55 education systems, three-level multilevel linear probability models and random slopes at the education-system level were estimated. Results showed substantial cross-system inequality in reported participation. Variance decomposition located 8.5% of total variation at the education-system level and 9.7% at the school level. Declared need was only partially associated with reported participation: teachers reporting low or moderate need were more likely to have participated in AI-related professional learning than those reporting no need, whereas teachers with the highest need showed no significant advantage. Digital self-efficacy and professional collaboration were consistently associated with higher participation. At the school level, digital resource shortages and school digital leadership support were significant predictors. Random-slope estimates showed that the association between high declared need and participation varied significantly across education systems. The findings suggest that equitable teacher AI literacy requires deliberate opportunity structures, not only competence frameworks or voluntary participation in professional learning.

Education is about supporting humans in their growth, with a special focus on exploring their intellectual potential. Learning to act following a given (even complex) pattern is losing its educational value very fast, because all well described activities can be automated. Education therefore should focus on developing those cognitive process dimensions of pupils where technology cannot compete with humans (Dagienė et al. (2020), Hromkovič and Lacher (2017), Hromkovič et al. (2020)). The contribution of this paper is conceptual. In the paper we show that starting with the algorithmic view on the historical development of number representations and calculations offers a natural, more understandable way for teaching mathematics in primary schools. We show that going consequently from concrete to abstract empowers pupils to be able to design own representations of numbers, rediscover the execution of arithmetic operations on their own, and even develop elementary calculations in own designed number systems. We show here how a successful process of rediscovery of arithmetic algorithms can be designed by using classical algorithm design methods as “induction” and “divide and conquer”. We show how that algorithmic thinking can essentially contribute to improving education in mathematics.

We investigate the pedagogical impact of Graphical Loop Invariant Based Programming (GLIBP) in an introductory programming course. This approach encourages students to visually model the objects and variables handled in the loop, before implementing it. To evaluate the efficiency of this GLI model, a four-condition A/B/C/D test was conducted across two problems, with students receiving varying levels of scaffolding (from no support to a fully constructed GLI). Analysis of students’ code showed that a well-designed GLI reduced errors related to the loop guard and the update of variables. However, many students struggled to understand or represent a GLI. The fill-in-the-blank GLI version, in particular, often added cognitive load rather than reducing it. Three recommendations emerged: train students to interpret a provided GLI when writing code; second, teach students to sketch their own model by recognizing similarities to previously solved problems; finally, guide students with questions to ensure all necessary variables and relationships are properly identified.

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.