Computational Thinking in Early Childhood: Psychometric Properties of the BD-HAM (Computational thinking: Debugging, Algorithmic Thinking, Modularity) Test with Rasch Model

The rising interest of educators, researchers, and policymakers around the world as far as the development of computational thinking skills in compulsory education is concerned is echoed in the plethora of research studies discussed in the pertinent literature. However, the successful injection of computation thinking in formal educational settings demands the construction of developmentally appropriate assessment tools. In this paper, we discuss a novel framework for assessing computational thinking skills in early childhood settings. The proposed framework was employed in a relevant quantitative research study conducted in the city of Heraklion, Crete, from February to June 2019, with a sample of 435 first and second graders and within the context of the Environmental Study course. This paper also provides evidence regarding the examination of age, gender, and learning achievements in the Environmental Study course as predictive factors of one of the core computational thinking competencies, namely algorithmic thinking. The research findings revealed that age and learning achievements in the Environmental Study course constitute predictive factors for algorithmic thinking skills in the first and second grade level of primary school. On the contrary, algorithmic thinking skills are not related to first and second graders’ gender. The results of this study provide a solid background for designing and implementing developmentally appropriate tools for cultivating and assessing computational thinking skills in the early years of schooling.

This study aims to explore how coding activities can stimulate computational thinking in early childhood. Specifically, it investigates the effects of coding learning on computational thinking skills in young children and identifies the most effective coding activities for this purpose. An experimental design was employed, involving 100 children aged 4-6 years. Participants engaged in coding activities using platforms such as ScratchJr and Code.org over 12 weeks. Pre and post-intervention assessments measured changes in computational thinking skills, supplemented by qualitative observations and feedback from educators. The findings indicate a significant improvement in computational thinking skills among participants following the coding intervention. Descriptive statistics revealed notable advancements in pattern recognition, algorithmic thinking, and problem-solving abilities. Qualitative data provided insights into the engagement levels and learning experiences of the children. Coding learning has a positive impact on the development of computational thinking in early childhood

To date, extensive work has been devoted to incorporating computational thinking in K‐12 education. Recognizing students' computational thinking stages in game‐based learning environments is essential to capture unproductive learning and provide appropriate scaffolding. However, few reliable and valid computational thinking measures have been developed, especially in games, where computational knowledge acquisition and computational skill construction are implicit. This study introduced an innovative approach to explore students' implicit computational thinking through various explicit factors in game‐based learning, with a specific focus on Zoombinis , a logical puzzle‐based game designed to enhance students' computational thinking skills. Our results showed that factors such as duration, accuracy, number of actions and puzzle difficulty were significantly related to students' computational thinking stages, while gender and grade level were not. Besides, findings indicated gameplay performance has the potential to reveal students' computational thinking stages and skills. Effective performance (shorter duration, fewer actions and higher accuracy) indicated practical problem‐solving strategies and systematic computational thinking stages (eg, Algorithm Design ). This work helps simplify the process of implicit computational thinking assessment in games by observing the explicit factors and gameplay performance. These insights will serve to enhance the application of gamification in K‐12 computational thinking education, offering a more efficient method to understanding and fostering students' computational thinking skills. Practitioner notes What is already known about this topic Game‐based learning is a pedagogical framework for developing computational thinking in K‐12 education. Computational thinking assessment in games faces difficulties because students' knowledge acquisition and skill construction are implicit. Qualitative methods have widely been used to measure students' computational thinking skills in game‐based learning environments. What this paper adds Categorize students' computational thinking experiences into distinct stages and analyse recurrent patterns employed at each stage through sequential analysis. This approach serves as inspiration for advancing the assessment of stage‐based implicit learning with machine learning methods. Gameplay performance and puzzle difficulty significantly relate to students' computational thinking skills. Researchers and instructors can assess students' implicit computational thinking by observing their real‐time gameplay actions. High‐performing students can develop practical problem‐solving strategies and exhibit systematic computational thinking stages, while low‐performing students may need appropriate interventions to enhance their computational thinking practices. Implications for practice and/or policy Introduce a practical method with the potential for generalization across various game‐based learning to better understand learning processes by analysing significant correlations between certain gameplay variables and implicit learning stages. Allow unproductive learning detection and timely intervention by modelling the reflection of gameplay variables in students' implicit learning processes, helping improve knowledge mastery and skill construction in games. Further investigations on the causal relationship between gameplay performance and implicit learning skills, with careful consideration of more performance factors, are expected.

This study aimed to conduct an in-depth evaluation of the activity-based computational thinking teaching practices performed to improve computational thinking and teaching skills of the basic education teachers. Based on the aim of the study, the case study design, one of the qualitative research methods, was selected. As a result of the collaborative work of five experts, a 20-hour education program built on two core competencies, four sub-competencies and eight thinking skills was implemented. The participants were 40 teachers, 20 of whom were classroom teachers and 20 of whom were pre-school teachers. Data were collected from three different sources using five data collection tools in order to conduct an in-depth analysis of the practices. Quantitative and qualitative data collection tools were used in a combined fashion in the research. The data were analyzed through content analysis and non-parametric analyses. Our findings revealed that thanks to the teaching practices performed, classroom teachers had significantly higher problem solving, diverse thinking, algorithmic thinking, and computational thinking total scores, while preschool teachers achieved significantly higher total scores in algorithmic thinking skills and computational thinking. It was observed that the participants defined computational thinking on the basis of 18 different thinking skills. The explanations of the participants about the functions of computational thinking skills were grouped under seven categories. When the principles that should be considered in the teaching of computational thinking skills were examined, it was seen that the need for utilizing scaffolds was stated the most.

The study aimed to determine the effect of the information technologies course on students' computational thinking skills and technology-mediated learning process. The study was conducted on 237 first-year students of the Faculty of Education who were enrolled in the information technology course, and a one-group pretest-posttest design was used. Dependent t-test, independent t-test, and correlation analysis were used to analyze the data. In the results obtained from the study, it was found that the information technologies course did not make a significant difference on the students' computational thinking skills, while it made a significant difference on the effect of technology-mediated learning on the learning process. When the effect of the information technologies course on computational thinking skills and technology-mediated learning process in terms of gender factor was considered as pre-test and post-test, there was a significant difference in favor of male students in terms of computational thinking skills and technology-mediated learning process within the scope of pre-test data, while there was no significant difference within the scope of post-test data. The study also showed that there was a positive and moderate relationship between students' computational thinking skills and their attitudes toward the technology-mediated learning process. In the context of this finding, it can be stated that technology-enhanced learning environments can have a positive effect on the development of computational thinking skills, and that lessons delivered in such learning environments can contribute to the development of students' creativity, algorithmic thinking, critical thinking, problem solving and collaborative working skills.

This study examines the improvement of pre-service teachers’ computational thinking skill levels through an educational technology course redesigned within the computational thinking context. 27 pre-service teachers from the Literacy Education Program enrolled in the Instructional Technologies and Material Development course in a public university in Turkey. Pre-service teachers engaged in some structured activities throughout the course and they were asked to complete a final project. Pre and post-survey results showed that pre-service teachers’ algorithmic thinking skills and computational thinking skills in general were improved after the course. Analysis of final projects also showed that pre-service teachers were able to use their problem solving, algorithmic thinking, and collaborative skills. However, they had difficulty in using their critical thinking skills and creativity. Findings have implications for the design of an educational technology course that pre-service teachers comprehend and practice computational thinking concepts.

This study aims to provide an overview of the computational mathematical thinking abilities of male and female students in solving mathematical problems and the aspects (abstraction, decomposition, algorithmic thinking, and generalization) that influence these differences. This study uses a qualitative method with a case study approach in a junior high school in Jakarta for the academic year 2023/2024. The participants in this study are 36 students. Data were collected through written examinations and interviews and analyzed using the triangulation technique. The results of the study showed different patterns of computational thinking skills between male and female students. Male students excel in decomposition and algorithmic thinking but are weak in abstraction and generalization. Female students excel in abstraction and decomposition but are weak in algorithmic thinking and generalization. Both male and female students master the context of decomposition for computational thinking skills but lack mastery in the context of generalization. In conclusion, gender differences influence specific aspects of computational thinking skills, indicating the need for tailored educational strategies to address these differences.

The purpose of this study is to adapt the computational thinking scale to Chinese. The study group consists of 1015 students. The study was performed in the descriptive scanning model. The final version of the scale was corrected in line with the opinions of the language experts who received the items translated from Turkish to Chinese. Exploratory and confirmatory factor analyses were calculated to determine the validity of the scale. Later, the distinctiveness forces were calculated. To determine the reliability of the scale internal consistency and stability levels were calculated. It has been concluded that the computational thinking scale is a valid and reliable tool in Chinese culture that can be used to determine high school students' computational thinking skills. In addition, it was concluded that the students' computational thinking skills were quite high. In terms of factors, the students' highest level skills are “Creativity” and the lowest ones are “Problem Solving” and “Algorithmic Thinking”. In terms of total scores and factors, computational thinking skills of male students are higher than female students. But problem solving skills are similar. It was concluded that k10 students' computational thinking skills were higher than k11 students in terms of “Problem Solving”, “Critical Thinking” and total scores. Based on the results obtained from this research and the literature, it is recommended that students frequently take part in activities that aim to improve their Problem Solving and Algorithmic Thinking skills, especially within the context of different courses.

Objective: This study aimed to analyze students' response to the use of computational thinking from the perspective of computational tools and to analyze the influence of gender on students' computational thinking skills. Method: Research design using a comparative approach with data collection techniques involved a survey using a Likert scale questionnaire comprising 25 items, covering five dimensions of computational thinking skills: abstraction, decomposition, algorithm thinking, evaluation, and generalization. The study subjects involved five classes: physics, physics education, geography, mining engineering, and vocational-technical education, focusing on students' ability to analyze data using JASP and IBM SPSS. The data analyze methods included: (1). Comparative Analyze; (2). Correlation analyzes (Spearman); (3). Chi-square test. Finding: The results showed that the computational thinking skills of students from various classes varied, with significant correlations between the skill dimensions. Physics and Physics Education stood out with exemplary achievements, while Geography and Mining Engineering also showed good progress. The vocational-technical education program displayed nearly perfect correlations in all aspects of computational thinking skills. Meanwhile, from the gender aspect, gender significantly influenced computational thinking skills (Sig<0.00). The analyze highlighted the differences in computational thinking skills between classes and the significant influence of gender. Implication: This emphasized the importance of developing computational thinking skills in higher education and the need for inclusive approaches to enhance computational excellence among students. The implications of this study give valuable insights for improving the teaching of computational thinking in physics education. Steps that might be addressed include identifying and enhancing weak components, such as abstraction and generalization, and using particular tactics to increase students' knowledge.

Educational robotics or unplugged coding activities in kindergartens?: Comparison of the effects on pre-school children's computational thinking and executive function skills

Computational Thinking is introduced as a problem-solving ability that is important for future generations to master. The mastery of computational thinking skills from an early age prepares children to anticipate competition and pursue success in the future. Robotics devices are widely advocated as interactive learning media to facilitate Computational Thinking development. Educational robotics have grown from ideas that represent critical stages of Computational Thinking. The application of robotics in teaching computational thinking skills is increasingly used and is evident in the literature. The paper reviews the development of an innovative robotics device in facilitating understanding of computational thinking in young children in terms of several aspects of computational thinking indicators. A review was also conducted to see the advantages and disadvantages of commercially available robotics devices. This paper was prepared by applying critical analysis methods to the literature published from 1952 to 2017 in international journals and proceedings. As a recommendation for future research, this paper proposes for an educational robotic development that facilitates the of computational thinking skills, especially for early childhood education.

Computational thinking skills are a relevant approach to future problem-solving. Therefore, these skills need to be integrated into mathematics learning in schools. This research is part of developing mathematical computational thinking skill tests for junior high school students. In this segment, the study aims to analyze the content validity of the mathematical computational thinking skill test. The main stages in this research are define, design, and develop. Expert validation data were collected using Google Form sheets. The analysis technique used is the content validity technique with the V Aiken method. The results of the study revealed that the results of content validation through the assessment of 7 experts, developed a test specification containing 20 items that measure mathematical computational thinking skills with a coefficient (V) in the interval (0.770 – 0.920) with an average of 0.866 or very good category. The test instrument is valid for measuring decomposition indicators, pattern recognition, abstraction, algorithmic thinking, and evaluation indicators. Each indicator is measured by 4 items with a coefficient of V decomposition indicator of 0.868, pattern recognition 0.883, abstraction 0.865, algorithmic thinking 0.833, and evaluation 0.883. The study concludes that the indicators of decomposition, pattern recognition, abstraction, algorithmic thinking, and evaluation indicators are valid in measuring mathematical computational thinking skills.

The purpose of this study is to investigate the computational thinking skills of junior high school students in terms of FI and FD cognitive styles on materials with numerical patterns. The type of research used is qualitative descriptive. The subjects of the study were 57 students from class VIII at one of the public junior high schools in Jepara. The data collection instruments used are computational thinking test questions, GEFT tests, and interviews. Researchers adopted three questions from the Pusmendik Kemdikbud class VIII to test students' computational thinking skills. All questions were validated by three mathematics education experts and tested on 5 grade VIII students before being used. In this study, four components of computational thinking: abstraction, pattern recognition, algorithmic thinking, and generalization were used to analyze students' computational thinking skills. This study focused on exploratory examination of pupils’ computational thinking with high categories, researchers selected 3 students on FI cognitive style and 2 students on FD cognitive style. The results showed that students with a FI cognitive style were able to meet all indicators of computational thinking namely abstraction, pattern recognition, algorithmic thinking, and decomposition. In contrast, students with FD cognitive styles are able to meet three indicators of computational thinking: abstraction, pattern recognition and generalization. Thus, it may be said that the ability to think computationally is related to the cognitive style of students.

Objective: This study aims to examine the influence of computational thinking skills, critical thinking skills, and collaborative thinking skills on the learning outcomes of robotics competencies of Electrical Engineering Education Students. Method: The sample in this study was 150 respondents, all of whom were students of the Electrical Engineering Education Study Program at Universitas Negeri Surabaya. The research data were obtained from filling out the questionnaire and analyzed quantitatively using the SEM PLS analysis technique with the help of the SmartPLS program. Results: This study shows that (1) Critical thinking skills have a positive effect on the educational robotics-based learning system, (2) computational thinking skills have a positive effect on the educational robotics-based learning system, (3) collaborative skills have a positive effect on the educational robotics-based learning system, (4) critical thinking skills have a positive effect on learning outcomes, (5) Computational Thinking Skills have a positive effect on learning outcomes, (6) Collaboration Skills have a positive effect on learning outcomes, (7) educational robotics-based learning systems have a positive effect on learning outcomes. Novelty: Educational robotics-based learning systems can be an ideal platform for developing computational, critical, and collaborative thinking skills among students. The use of robots as interactive and direct learning media through experiments and problem solving. This can help better understand technical concepts and increase confidence in facing complex challenges in the increasingly connected and rapidly changing real world.

This study investigates accounting students’ computational thinking (CT) skills in higher education. Employing a mixed-methods approach, quantitative data were collected from 390 students to assess their skills across five CT dimensions: problem formulation, decomposition, algorithmic thinking, abstraction, and pattern recognition. The results reveal consistently low competency levels, with problem formulation scoring the highest but remaining within the low skills range. Significant gender-based disparities were also observed, with male students outperforming females across all CT dimensions. Qualitative findings further illuminate these results, highlighting limited awareness and understanding of CT, minimal exposure to practical CT applications, and the traditional structure of accounting courses as key barriers to CT skill development. Students reported that their courses primarily focus on theoretical principles and procedural tasks, with little integration of critical thinking or computational skills. Participants emphasized the need for hands-on, case-based learning to bridge the gap between theoretical knowledge and the demands of modern accounting practices. This study highlights the necessity for curriculum reform to integrate CT concepts explicitly in accounting education. Addressing these challenges, including gender disparities and the lack of early exposure to CT, will better equip students with critical thinking, problem-solving, and analytical skills essential for thriving in a data-driven, technology-intensive accounting profession. The findings contribute to the broader discussion on embedding CT into non-STEM education and provide actionable recommendations for enhancing CT skill development in accounting education.