Informatics in 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.

The design of algorithms is one of the hardest topics of high school computer science. This is mainly due to the universality of algorithms as solution methods that guarantee the calculation of a correct solution for all potentially infinitely many instances of an algorithmic problem. The goal of this paper is to present a comprehensible and robust algorithms design strategy called “constructive induction” that enables high school students to discover solution methods for a large variety of algorithmic problems. The concept of constructive induction is based on searching for a universal method for solving any instance of an algorithmic problem when solutions of smaller problem instances are available.

In this study, we aimed to investigate the impact of cooperative learning on the computational thinking skills and academic performances of middle school students in the computational problem-solving approach. We used the pretest-posttest control group design of the quasi-experimental method. In the research, computational problem-solving activities regarding 6th graders' goals of the "heat and matter" unit, were applied individually by Group 1 and cooperative learning by Group 2. These activities required students to use computational thinking skills and code using the Python programming language. The study involved 34 students from the 6th grade of a private middle school located in the capital city of Turkey. The Computational Thinking Test (CTt) and an academic achievement test were used as pre-tests and post-tests to monitor students' computational thinking skills and academic performances. Additionally, computational problem-solving activities were scored to track the progress of students' computational thinking abilities. Non-parametric Mann Whitney U and Wilcoxon T-tests were utilized to analyze the progression of pupils' computational thinking abilities and academic success, and ANCOVA was used to analyze CTt scores. Qualitative data were collected through semi-structured interviews at the end of the process to determine students' views on the computational problem-solving process. Results revealed a significant increase in students' academic achievement and computational thinking skills in both groups. A comparison of post-test scores showed no significant difference between groups. It is anticipated that the research results will make meaningful contributions to the literature concerning the progress of computational thinking skills in secondary school students.

As our society has advanced in the era of digital transformation, education has been transformed from knowledge-centered to competency-centered to solve future problems in the light of unpredictable changes and events in our lives. Programming education provides the basic knowledge needed, and fosters higher-order thinking skills in the process of generating and converging ideas to solve problems. However, in Korean elementary schools, it is mostly based on a lecture-based instructional design and focuses on knowledge delivery, which has limited the educational effects of programming. However, productive failure (PF) focuses on learning concepts in authentic problems, and lets the students generate different solutions and discuss them in an acceptable environment, with the result that they fail to solve the problem. Therefore, this study developed a PF-based educational program and tested it on sixth-grade students in a Korean elementary school. The results showed that the computational thinking (CT) and creative problem-solving (CPS) skills of the experimental group were significantly greater than those of the control group, with a medium effect size for CT and a high effect size for CPS skills. To generalize the results and increase the applicability, follow-up studies should expand the subject of the study, develop specific teaching guidelines for teachers, and invent various learning problems appropriate to the students’ level and different domains of learning.

This study aims to explain the relationships between secondary school students' digital literacy, computer programming self-efficacy and computational thinking self-efficacy. The study group consists of 204 secondary school students. A relational survey model was used in the research method and three different data collection tools were used to collect data. The structural equation model was used in data analysis to reveal a model that explains and predicts the relationships between variables. According to the results of the research, it was determined that digital literacy of secondary school students affected their computer programming self-efficacy, digital literacy affected their computational thinking self-efficacy, and computer programming self-efficacy affected their computational thinking self-efficacy. It was also found that digital literacy skills have an indirect effect on secondary students' computational thinking self-efficacy on computational thinking self-efficacy.