Computational Thinking Research Articles

Correction to: The infuence of scafolding for computational thinking on cognitive load and problemsolving skills in collaborative programming

Correction to: The infuence of scafolding for computational thinking on cognitive load and problemsolving skills in collaborative programming

Improving the Computational Thinking Abilities of Junior High School Students Through Problem-Based Learning

Computational Thinking is a crucial skill that students must master in the 21st century. Computational Thinking encompasses a systematic process that enables individuals to formulate problems, develop strategies, and find effective, efficient, and reusable solutions. This research aims to enhance students' Computational Thinking abilities through the application of the Problem-Based Learning (PBL) model, which is effective in facilitating active learning and problem-solving. The methodology used in this study is Classroom Action Research (CAR) with a spiral approach developed by Stephen Kemmis and Robin McTaggart. Data collection is conducted through post-tests administered at the end of each cycle phase. The obtained data are analyzed using a designed assessment rubric, wherein students are categorized based on their Computational Thinking abilities and foundational Computational Thinking skills. The results indicate that by the end of Cycle II, of students achieved a moderate to high level of Computational Thinking ability. These findings affirm that the implementation of the Problem-Based Learning learning model can significantly improve students' Computational Thinking skills.

Evolving Computer Science Education Research in Teaching and Learning: A Bibliometric Analysis

This study performed a bibliometric analysis of computer science education (CSE) publications in teaching and learning. The research trend in computer science education was analysed using publications from the Scopus-indexed database between 1970 and 2021. This study aims to summarize this information to assist three groups of stakeholders: educators, researchers and policymakers. Our review goal to document and analyse the trends in CSE research publications for teaching and learning. A total of 800 publications were derived for further assessment based on computer science education in the article title with multiple tools. We used, Microsoft Excel, VOSviewer and Harzing’s Publish or Perish were utilized for frequency analysis data visualization and citation metrics and assessment. The study outcomes were documented with standard bibliometric indicators: publication growth, authorship trends, collaboration, prolific authors, national contribution, most active establishments, top-cited articles, preferred journals, intellectual structure and topical trends. Resultantly, CSE research publications have perpetually grown for the past 51 years since 1970 with the United States highlighting the largest study contributor, followed by Germany and the United Kingdom. The ACM SIGCSE Bulletin published the highest number of CSE research publications. The analysis of the cluster on topical trends indicates that curriculum design and implementation (e.g., assessment, broadening participation), elements of teaching and learning (e.g., constructivism, e-learning, game-based learning) and other areas of interest comprise pedagogical approach (e.g., computational thinking, k-12 education, motivation). Based on the research results, we expand on the findings and propose how authors might enhance the CSE function in curriculum design, implementation and shifts could eventually substitute past study clusters.

From conception to fruition: Co-designing a digital exhibit incorporating embodied cognition to encourage young children’s computational thinking in a Science Discovery Centre

From conception to fruition: Co-designing a digital exhibit incorporating embodied cognition to encourage young children’s computational thinking in a Science Discovery Centre

Promote students’ computational thinking in rural areas: The moderating role of gender

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

Pre-service teachers’ competencies to develop computational thinking: A Portuguese tool to analyse Computational Thinking

Computational thinking (CT) is an essential mathematical skill for problem-solving and students’ future lives. It is integrated into the educational curricula of several countries, including Portugal. Therefore, pre-service teachers (PST) must possess didactic knowledge to effectively develop CT in students. The aim of this study encompassed three main objectives: translating and adapting the computational thinking scale into Portuguese (study 1), validating the scale (study 2), and assessing the perceived levels of CT competencies among PST in Portuguese university students while examining differences between undergraduate and master’s level PST (study 3). The sample consisted of study 1 with 43 participants and study 2 and study 3 with 382 participants. In study 1, temporal stability was assessed indicating strong stability. The internal consistency showed good homogeneity of the items. The exploratory factor analysis revealed consistency with the structure of the original scale. In conclusion, the Portuguese version of the CT scale demonstrates adequate psychometric properties, proving valid and reliable for assessing CT in university students. Additionally, significant differences were observed between undergraduate and master’s degree students, underscoring the importance of tailored training programs to meet the specific needs of undergraduate students.

Computational Thinking and its Relationship with Mathematics Teachers’ Perception of Implementation in Problem-Solving Skills

Computational Thinking and its Relationship with Mathematics Teachers’ Perception of Implementation in Problem-Solving Skills

Enhancing computational thinking in children through logical puzzles: A machine learning approach for early coding education

This recent growth in early education in coding evidences the rising need for the computing-thinking-skills development in children. Though most of the programming curricula are based upon syntax and other basic concepts of coding, computational thinking encompasses broader cognitive skills, such as problem-solving, abstraction, and logical reasoning. This research therefore intends to fill a gap in research and will investigate the ways pre-coding games employ logical puzzles as an enhancement in developing computational thinking skills in children aged 7-10 years and how these affect the acquisition of programming knowledge. In all, there will be two groups assigned: one will be exposed to logical-oriented puzzle training, Group A, and the other will be a control group, Group B, which receives only regular standard lessons in programming introduction. Pre- and post-measures of computational thinking and basic programming skills will be obtained from the participants. The machine learning algorithms, such as Random Forest for prediction in terms of improvement in subject performance in computational thinking and K-Means clustering to identify patterns in their development of skills, will be implemented in their pure or integrated forms to analyze data. Logistic Regression will be executed in order to model the odds of improved performance in programming given an intervention of exposure to puzzles. The research study will, therefore, apply these algorithms in comparing the performance of the two groups and reveal the aspects of computational thinking to which the greatest influence in a positive way through puzzle activities is exerted. It is expected that children exposed to logical puzzles may show significant improvements in both computational thinking and programming skills. These findings should be useful to educators in their effort to show how logical puzzles can be integrated into the curricula of early coding education to build foundational skills and thereby contribute to a refinement of best practices in STEM education.

TEACHING COMPUTATIONAL THINKING IN SAUDI ARABIAN SCHOOLS USING SMARTPHONE APPS

This study investigates the efficacy of smartphone applications in teaching computational thinking to students in Saudi Arabian primary and secondary schools. Computational thinking, encompassing problem-solving, computational logic, and algorithmic thinking, is an essential skill in the modern world. The research assesses various educational apps that support the development of computational thinking and examines their integration into the Saudi curriculum. This study incorporates qualitative and quantitative research techniques to provide an inclusive perspective. Participants included primary and secondary school students from various schools in Saudi Arabia. The research employed pre- and post-tests for quantitative analysis and interviews and questionnaires for qualitative data. Quantitative results include pre- and post-tests of the students in computational thinking with the aid of co-varying with Computational thinking, while interviews and questionnaires with teachers and students offer qualitative data. Best-selling educational applications for ScratchJr, Lightbot, Cargo-Bot, and Kodable are compared concerning how efficiently they introduce programming ideas and encourage rational thinking. Results have suggested that smartphone applications have a positive influence on students’ computational skills. An enhanced standard is also noticed regarding the students’ aspects that involve problem analysis, identification of pattern, and formation of algorithm. The use of applications such as ScratchJr and Lightbot is justified regarding an application’s capacity to improve students’ motivation and willingness to learn computational concepts. Teachers indicate that their students have shown more concern and engagement mainly when the teacher integrates these apps. Hence, based on the findings of the study, it is evident that incorporating Smartphone applications into the curriculum will lead to a reforming change in the teaching approaches towards efficiency as well as feasibility of the learners. These findings are valuable for the Saudi context for policy and teaching enhancement, signaling the need to provide more resources in digital tools and invest in the professional development of teachers to capture the virtues of advanced technology in improving learners’ performance.

TEACHING COMPUTATIONAL THINKING IN LEARNING COMMUNITY: A CASE STUDY OF SOLKAN PRIMARY SCHOOL

Slovene kindergartens and schools have introduced a learning community where teachers learn from and with each other, acquire knowledge, and strengthen students' skills. The success of such connections depends on shared values, attitudes, beliefs, and supportive mutual relationships. Teachers are essential when Slovene school participates in development projects B-RIN, aimed at introducing fundamental computer science and informatics content and computational thinking into kindergarten groups and school classes. When selecting members of the development team, the participants were guided by the criterion that the selected educators and teachers are open to exploring their teaching practices, capable of continuously upgrading and evaluating their pedagogical work, and developing and improving their teaching approaches based on the study of their practice, emphasizing student-centred approaches. The purpose of this contribution is to highlight the implicit theories and attitudes of the development team and school staff regarding the understanding and importance of introducing fundamental B-RIN content and computational thinking into teaching, not only in the STEM field but also in other subject areas, and to present the approaches with which participants have influenced the implicit theories and attitudes of teachers and educators within our VI organization. This is also because training students in computational thinking and fundamental computer science and informatics content significantly impacts the development of metacognitive strategies, such as problem-solving skills, especially for open-ended problems, self-regulation learning skills, perseverance in facing failure, consequently greater resilience, and encouraging creativity. Therefore, it also broadly impacts the mental health of students. Keywords: computational thinking, implicit theories and attitudes, learning community, metacognitive strategies, development team

Investigation of Computational Thinking Skills through Instructional Techniques, Games and Programming Tools

Computational thinking (CT) has become a vital approach to problem-solving. This method has been proven successful in various areas, especially science, technology, engineering, mathematics (STEM), and professional development. The integration of CT in education provides a practical approach to solving problems. This concept paper investigated the various uses of instructional techniques and programming tools to enhance CT skills used in previous empirical research. CT consists of four main pillars: decomposition, which refers to breaking down identified problems into smaller parts; pattern recognition, which involves finding occurring patterns that can be seen; abstraction through identification of important details and removing unnecessary information; and algorithm, where a step-by-step procedure is solution is made. By exploring instructional strategies and programming environments, this paper outlines the possible impacts of these instructional strategies, games, and programming environments on improvement in CT skills, thus providing a theoretical basis for future empirical studies. The strength and combination of these approaches provide a comprehensive learning experience. This paper also suggests instructional techniques and programming tools to enhance underexplored CT skills based on research gaps.

Arduino-Based Mobile Robotics for Fostering Computational Thinking Development: An Empirical Study with Elementary School Students Using Problem-Based Learning Across Europe

The present article explores the impact of educational robotics on fostering computational thinking and problem-solving skills in elementary school students through a problem-based learning approach. This study involved the creation of a framework which includes a robot and two eBooks designed for students and teachers. The eBooks serve as a guide to the construction and programming of a small Arduino-based robot. Through integration with gamification elements, the model features a narrative with three characters to boost a student’s engagement and motivation. Through iteration of heuristic evaluations and practical tests, we refined the initial theoretical framework. An empirical study was conducted in two phases involving 350 students. The first empirical test involved a small group of 21 students, similar to end users, from five European schools. With a 100% completion rate for the tasks, 73.47% of these tasks were solved optimally. Later, we conducted a larger validation study which involved 329 students in a Portuguese school. This second phase of the study was conducted during the 2022–2023 and 2023–2024 school years with three study groups. The results led to a 91.13% success rate in problem-solving activities, and 56.99% of those students achieved optimal solutions. Advanced statistical techniques, including ANOVA, were applied to account for group differences and ensure the robustness of the findings. This study demonstrates that the proposed model which integrates educational robotics with problem-based learning effectively promotes computational thinking and problem-solving skills, which are essential for the 21st century. These findings support the inclusion of robotics into primary school curricula and provide a validated framework for educators.

Game On: A Journey into Computational Thinking with Modern Board Games in Portuguese Primary Education

Recent studies highlight the potential of modern board games (MBGs) to foster computational thinking (CT) skills in students. This research explored the impact of integrating MBGs into a primary education classroom through an embedded concurrent mixed-methods approach, with a pre-experimental design in its quantitative aspect and content analysis in its qualitative dimension, with 20 fourth-grade students from a school in Portugal. The students participated in 10 game sessions, each lasting 50 min, and their CT skills were assessed using Bebras tasks in both the pre-test and post-test phases. Statistical analysis, including the Shapiro–Wilk test for normality and paired sample t-tests, revealed significant improvements in key CT areas, particularly abstraction, algorithmic thinking, and decomposition. Descriptive statistics were also calculated, and content analysis using Nvivo software was conducted on field notes, corroborating the quantitative data. The results suggest that MBGs can serve as a valuable educational tool for developing CT skills in young learners. This study not only highlights the effectiveness of MBGs but also emphasises the need for further research using more robust experimental designs to enhance CT development in educational settings.

The impact of a block-based visual programming curriculum: Untangling coding skills and computational thinking

Backgroundand Context: Computer programming is increasingly being taught to young children worldwide. Quality programming curricula are needed to enable students to explore computer science concepts and develop a positive attitude towards programming. However, few studies have been conducted on the available curricula for young learners. ObjectiveThis study aims to provide an evidence-based, public, and free computer science curriculum for early childhood education by investigating the efficacy of the CAL-ScratchJr curriculum, among second graders. MethodA cluster-randomized controlled trial involving 11 schools and 21 second-grade classrooms was conducted to examine the impact of the 24-lesson curriculum on students’ coding proficiency and computational thinking. FindingsThe results indicated that the CAL-ScratchJr curriculum intervention had a medium positive effect on second graders’ coding skills, but no significant impact on computational thinking. ImplicationsResults caution against incorporating disconnected abstract computational thinking practices in education. The features of the curriculum were discussed.

Investigation of Higher Education Students' Computational Thinking Skill Levels

Computational thinking skill is a skill that has its origins in ancient times but has gained importance today. The competences provided to individuals by computational thinking skills are also of great importance for the education system. Therefore, it is important for students to acquire and develop these skills in order to adapt to the requirements of the modern world. Based on this, in this study, the level of information processing thinking skills of higher education students was examined and the level of this skill was investigated in line with different variables. 'Computational Thinking' scale was used as a data collection tool in the study. Cronbach Alpha internal consistency coefficient of the scale. 96 was calculated as. Demographic characteristics such as gender, department, class and education level were taken into consideration. Quantitative research method was used in the study. The research was conducted with 298 participants studying in higher education. In the analysis of the data, independent t-test and ANOVA analyses were applied to determine the score differences between the groups. As a result of the study, higher education students were found to have high levels of computational thinking skills. Gender was found to be effective on computational thinking skills. As the level of education increased, it was determined that computational thinking skills increased. It was seen that the education given in different departments had an effect on computational thinking skills. Finally, it was found that there were significant changes in students' computational thinking skills as their grade level increased.

A Staged Framework for Computer Vision Education: Integrating AI, Data Science, and Computational Thinking

Computer vision education is increasingly important in modern technology curricula; yet, it often lacks a systematic approach integrating both theoretical concepts and practical applications. This study proposes a staged framework for computer vision education designed to progressively build learners’ competencies across four levels. This study proposes a four-staged framework for computer vision education, progressively introducing concepts from basic image recognition to advanced video analysis. Validity assessments were conducted twice with 25 experts in the field of AI education and curricula. The results indicated high validity of the staged framework. Additionally, a pilot program, applying computer vision to acid–base titration activities, was implemented with 40 upper secondary school students to evaluate the effectiveness of the staged framework. The pilot program showed significant improvements in students’ understanding and interest in both computer vision and scientific inquiry. This research contributes to the AI educational field by offering a structured, adaptable approach to computer vision education, integrating AI, data science, and computational thinking. It provides educators with a structured guide for implementing progressive, hands-on learning experiences in computer vision, while also highlighting areas for future research and improvement in educational methodologies.

Effects of students using different learning approaches for learning computational thinking and AI applications

Effects of students using different learning approaches for learning computational thinking and AI applications

Voices of Elementary Computer Science Teachers: Computer Science Integration Rationales and Practices

Objectives . Computer Science (CS) education has become increasingly prevalent in elementary schools because of multiple rationales, such as the importance of computational literacy and the growing demand for CS-related workforce preparation. As elementary CS standards continue to be adopted by many states, more examples and voices from the field may help educators understand what effective computational thinking (CT)/CS integration looks like in practice. Method . We employed a descriptive qualitative approach to study eight award-winning K-8 CS teachers and aimed to answer three research questions: (1) What were elementary CS teachers’ rationales for CT/CS integration? (2) How do elementary CS teachers integrate CT/CS into their classroom practices? (3) What are the needs of CS teachers for meaningful CT/CS integration in elementary schools? Data were collected through interviews, a questionnaire, and artifacts and analyzed using thematic analysis. Findings . Our findings of elementary CS teachers’ rationales for CT/CS integration encompassed its essential nature as a literacy skill, its potential to promote equity in education, and its alignment with standards across various disciplines. To support CT/CS integration at the elementary level, CS teachers described important aspects with examples from their instructional practices: (a) Real-world applications; (b) hands-on activities; (c) strategies to scaffold and guide student learning; (d) collaboration with classroom teachers; and (e) support from professional development (PD). Conclusion . The study results shared CS teachers’ voices from practice and shed light on the urgent need for more support and PD opportunities for both homeroom teachers and CS teachers in elementary schools, as well as the crucial need for multiple levels of support within K-12 school systems. Continuous efforts in creating systematic and sustainable PD plans and supporting collaborative professional communities for teachers within the instructional context are a must to help prepare our elementary students with the essential CS knowledge and skills they need to thrive in this technology-rich society.

Supporting Educators to Co-Design Interdisciplinary Projects Integrating Educational Robotics and Arts

The growing call to foster interdisciplinary learning has found educators struggling to co-design learning across disciplinary boundaries. Aiming to contribute to the area of interdisciplinary learning design, we explored how to support educators in co-designing interdisciplinary projects integrating Educational Robotics (ER) and Arts, hereafter called Artful ER projects. Our research included developing the "FERTILE" Learning Design methodology as a conceptual tool scaffolding the integration of discipline-oriented viewpoints while educators co-design. Acknowledging that one of the challenges in interdisciplinary collaboration derives from educators’ different intentions for students’ learning outcomes, the "FERTILE" methodology introduces Computational Thinking (CT) skills as an Artful ER project’s primary learning outcome. In this line, the “FERTILE” methodology adapts the Creative Computational problem-solving model, which has been reported to cultivate CT skills through its staged process. Furthermore, we developed the "FERTILE" Community Platform as an online environment providing two-fold support to educators as designers of interdisciplinary learning. The "FERTILE" Community Platform incorporates community functionalities that address collaboration practicalities while educators co-design. Additionally, it integrates authoring functionalities scaffolding educators in designing Artful ER projects based on the "FERTILE" Learning Design methodology. This paper explores how the authoring functionalities of the "FERTILE" Community Platform support educators in designing interdisciplinary learning. We report on a pilot study conducted with Greek and Spanish educators. In this study, we applied a mixed-method research design with a quantitative strand adopting indicators from the Usability Metric for User Experience (UMUX) model and a qualitative strand providing insights into participants’ perceptions. The findings indicate that scaffolding disciplinary elaboration (e.g., robot technical requirements and art forms) and systematising interdisciplinary context (e.g., through project categories) may trigger educators’ mutual understanding. The participants endorsed authoring functionalities that adopted high contextualisation levels for learning design representation to communicate their design ideas across disciplinary boundaries. Also, the participants valued CT skills as the primary outcome of interdisciplinary learning, indicating that CT skills’ cultivation may be the broker among disciplines and trigger educators to overcome disciplinary barriers. Finally, we discuss the findings and implications for refining the "FERTILE" Community Platform as an online environment for educators co-designing Artful ER projects.

Model Implementation Problem-Based Learning with a Differentiated Learning Approach to Improve Abilities, Computational Thinking Student

Based on the results of the non-cognitive diagnostic assessment conducted by the author in class X PPLG 1 SMK Negeri 11 Bandung, the computational thinking abilities possessed by students still need to improve. In addition, based on the results of observations, teachers have not implemented differentiated learning optimally. This study aims to improve students' computational thinking abilities by applying problem-based and product-differentiated learning models. The research method used is Classroom Action Research (CAR), carried out in 3 cycles. The subjects of this study were 20 students of class X PPLG 1. The results of this study show that applying the PBL model and product-differentiated learning can improve student learning outcomes in the DPLG subject, with the achievement of completeness in cycle I at 45%, cycle II at 70%, and cycle III at 45%. Students' computational thinking abilities also increased from achieving completeness of 10% to 75%. The effectiveness of learning carried out in cycles I and II are included in the reasonably practical category, as evidenced by the N-gain of 60% and cycle III of 41%, which can improve students' computational thinking skills.