Informatics in Education

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.

This study investigates the effect of programming courses on the computational thinking (CT) skills of elementary school students and the learning effectiveness of students from different backgrounds who are studying programming. We designed a OwlSpace programming course into an elementary school curriculum. Students in fourth and fifth grades were taught the fundamentals of programming. We measured and analyzed the effectiveness of their CT skills and self-efficacy in CT. The researchers analyzed the changes in the CT of different gender, different grade, and different past experience students in programming courses and then made specific recommendations for information technigy teachers and related units. The results demonstrate that students learned and improved their CT skills by taking OwlSpace programming course. Additionally, gender, grade, and past experience are found to have no impact on the students’ learning that means the course can improve students ability without limited any characteristics.

There is an increasing interest in the integration of computational thinking (CT) in the K-12 curriculum. By integrating CT into other disciplines, the aim is to equip students with essential skills to navigate domain-specific challenges. This study conducts a systematic review of 108 peer-reviewed scientific papers to analyze in which K-12 subjects CT is being integrated, learning objectives, CT integration levels, instructional strategies, technologies and tools employed, assessment strategies, research designs and educational stages of participants. The findings reveal that: (a) over two-thirds of the CT integration studies predominantly focus on science and mathematics; (b) the majority of the studies implement CT at the substitution level rather than achieving a transformation impact; (c) active learning is a commonly mentioned instructional strategy, with block-based languages and physical devices being frequently utilized tools; (d) in terms of assessment, the emphasis primarily lies in evaluating attitudes towards technology or the learning context, rather than developing valid and reliable assessment instruments. These findings shed light on the current state of CT integration in K-12 education. The identified trends provide valuable insights for educators, curriculum designers, and policymakers seeking to effectively incorporate CT across various disciplines in a manner that fosters meaningful skill development with an interdisciplinary approach. By leveraging these insights, we can strive to enhance CT integration efforts, ensuring the holistic development of students' computational thinking abilities and promoting their preparedness for the increasingly interdisciplinary domains of digital world.

This study investigated the effects of 3D model building activities with block codes on students' spatial thinking and computational thinking skills. The study group consists of 5th grade students in a secondary school in the Central Anatolia region of Turkey. For the study, a pretest-posttest control group was utilized within the experimental design. A total of 66 students participated, 23 in the experimental group and 43 in the control group. While the activities prepared on the Tinkercad platform were applied in the experimental group, the courses were taught using the traditional teaching method in the control group. The study covers a period of three-weeks in the course information technologies and software. The study used the computational thinking levels scale and spatial thinking test scales as data collection instruments. The data was analyzed using both descriptive statistics and independent samples t-tests. Based on the study findings, there were no significant differences observed in the levels of computational thinking skills levels and spatial thinking test scores between the experimental and control groups.

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games’ content, leading them to develop a critical stance towards the game data model and representations.

The creative programming language Processing can be used as a generative architectural design tool, which allows the designer to write design instructions (algorithms) and compute them, obtaining graphical outputs of great interest. This contribution addresses the inclusion of this language in the architecture curriculum, within the context of digital culture and alternative approaches to how digital tools are used and learned. It studies the different processes related to Computational Thinking that are triggered in the prototyping of computer applications and that lead to creativity. The similarity between architectural design and programming is analysed, both in problem solving (abstraction, decomposition, iterative revisions -debugging-, etc.) and in the use of mechanisms of a digital nature (loops, randomness, etc.). The results of the design and testing of a pilot course are shown, in which the way of teaching, learning and using this programming language is based on the graphical representation of problems through sketches.