Bebras-Based Assessment for Computational Thinking: Performance and Gender Analysis

The work is devoted to the consideration of improving the quality of teaching students the discipline “Numerical methods” through the development of the cognitive component of computational thinking based on blended learning. The article presents a methodology for the formation of computational thinking of mathematics students, based on the visualization of algorithmic design schemes and the activation of the cognitive independence of students. The characteristic of computational thinking is given, the content and structure of computational thinking are shown. It is argued that a student with such a mind is able to manifest himself in his professional field in the best possible way. The results of the application of the technique are described. To determine the level of development of the cognitive component of computational thinking, a diagnostic model has been developed based on measuring the content, operational and motivational components. It is shown that the proposed method of developing computational thinking of students, taking into account the individual characteristics of students’ thinking, meaningfully based on the theoretical and practical aspects of studying the discipline, increases the effectiveness of learning the course “Numerical methods”. The materials of the article are of practical value for teachers of mathematical disciplines who use information and telecommunication technologies in their professional activities.

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in 21st century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included ‘generalization’ in CTPF of science education instead of ‘parallelization’ which was not found. The implication about teacher education was made based on the CTPF in terms of science education.

Computational thinking is a new concept that is hardly known by most ordinary Malaysians. On a positive note, in 2017, the Ministry of Education of Malaysia implemented a new curriculum by introducing two new secondary school subjects, namely Fundamental of Computer Science and Computer Science. These subjects contain five components of computational thinking called ‘techniques, which are taught to Form 1 and Form 4 students, respectively. Likewise, the Department of Teachers Education of Malaysia also introduced six components of computational thinking in its training programs. In contrast, there is a lack of attention to such a concept given to tertiary education. A preliminary survey was conducted in early 2019, which involved 50 students majoring in 21 educational programs. The findings showed 92% of the respondents had no knowledge regarding the computational thinking skill, signifying an urgent need to determine relevant components that characterize such a skill needed in educational programs, which according to Wing (2006) has numerous components. In this study, the researchers aimed to identify relevant components that characterize the computational thinking skill required for students in learning educational programs. An extensive review of documents published in leading online databases, such as IEEEXplore, Science Direct, and Web of Science, was carried out that yielded 116 articles. Further analysis reduced this number to 29 articles that were related to the characterization of the computational thinking skill with 66 components. Among these components, algorithm, abstraction, and decomposition were the top three components with the highest frequency of being cited in the selected articles, registering percentage points of 9.78%, 7.41%, and 5.35%, respectively. As such, the design and development of new instructional approaches for the teaching of educational programs should emphasize three components to help students develop strong computational thinking skills.

Objective: The benefits of computational thinking have become an increasingly acknowledged and popular subject of investigation among researchers. This research aims to gather detail and comprehensive information regarding the learning of computational thinking skills in mathematics at various educational levels through a systematic literature review approach. Methods: With a focus on education level, instructional media, mathematics content, and the components of computational thinking addressed by previous researchers, this paper applied the PRISMA Systematic Review Protocol to offer a comprehensive synthesis of sixteen empirical studies retrieved from the Scopus database on the implementation of computational thinking in mathematics education. Findings: Most research on fostering computational thinking in mathematics education is concentrated at the elementary and junior high school. To optimize the development of computational thinking in mathematics, teachers should be reminded of strategies to support students, particularly through activities involving simulations using various instructional media. Examples of such media include programming platforms, visualization tools, and interactive simulations and games. Number operations and geometry are the mathematical content most widely used for fostering computational thinking. Algorithmic thinking, a crucial component in fostering computational thinking among elementary school students, helps them develop a strong foundation for understanding higher mathematical concepts. Conclusion: A systematic review of computational thinking in learning mathematics at the various education level is conducted in this study. The chosen studies were systematically analyzed for the advancement of computational thinking in learning mathematics to provide new an insight information for educators and stakeholders. Keywords: computational thinking, mathematics, systematic literature review. DOI: http://dx.doi.org/10.23960/jpmipa/v25i1.pp34-52

The use of computer simulations in higher education has been widely used in various learning materials. Computational thinking ability is one of the abilities needed when using computer simulations during the educational process. This study aims to ascertain the effect of computational thinking on students’ computer simulation abilities in terms of learning outcomes. This study employed a one-group quasi-experimental design, utilizing pre-tests and post-tests for evaluation. Fifty students enrolled in a digital communication program participated in the research study. Computational thinking treatment is given during learning. Students are introduced to the four essential components of computational thinking throughout the educational process, such as algorithmic thinking, pattern recognition, abstraction, and decomposition. The computational thinking instrument's validity score is 0.8, and its reliability score is 0.84, according to the test results. The study's findings demonstrate that students' average N-Gain, is 0.749, with high criteria. The T-test results show that computational thinking influences student learning outcomes in digital communication. The research implications show that computational thinking helps enhance students' abilities for learning.

Computational Thinking is a skill that plays a significant role in improving students' skills, achievements, and global competitiveness. This research aims to determine the Computational Thinking abilities of preservice physics teachers for each indicator by creating a simulation using the Scratch application. This research is a descriptive qualitative study. The respondents involved in the research were 10 pre-service physics teachers at Khairun University. The respondents are students who have taken Computer Programming courses. The saturated sampling technique was used to determine the respondents. The data collection technique for this research is a test. The instrument used is a simple project to simulate projectile motion. The results of this research show that all students were able to complete the abstraction and generalization components perfectly. Furthermore, in the decomposition and algorithmic thinking components, two students made incorrect commands in the Scratch application. However, after the interview, it was revealed that the error was not due to their lack of understanding of physics concepts, but rather due to the limitations of mathematical operations in the Scratch application. Importantly, several students explained that most of the Computational Thinking components had already been developed through their habit of solving physics phenomena cases. Therefore, it can also be concluded that Computational Thinking can train students to solve physics problems effectively.

Computational thinking (CT) and programming are essential competencies in the Fourth Industrial Revolution, spurring interest in learner‐centered and artificial intelligence (AI)‐based education. Combining the active environment of flipped learning with the personalized support of generative AI is a promising new strategy for programming education; however, research integrating these elements remains limited. This study investigated the effects of programming education that incorporates both flipped learning and generative AI on students' CT and problem‐solving skills, with particular attention to variations by university size and educational setting. Participants were students enrolled in introductory software courses at two universities (K and J). Programming lessons integrating generative AI and flipped learning were developed, and changes in students' CT and problem‐solving skills were evaluated using pre‐ and post‐surveys and analyses of programming assignments. The study also examined the relationship between programming‐related CT components (problem decomposition, abstraction, algorithm design, automation, and simulation) and students' programming difficulties, as well as key factors contributing to differences in learning outcomes between institutes. The results revealed significant improvements in the CT and problem‐solving skills of students. Based on these findings, pedagogical strategies were proposed to optimize the use of generative AI and flipped learning in diverse educational contexts. This study provides valuable insights into the application of active, personalized learning in various settings, including introductory software education and online learning.

Problem Based Learning and Computational Thinking together have been applied in several studies, proven to be effective to bring out the best of problem solving. While both of them share several common features, computational thinking is a way of solving problems in problem based learning. One of the major components that direct a student towards critical thinking and self-directed learning are reflections. The components of computational thinking can be used to intervene reflections. This paper proposes to use decomposition for reflections. A multi method study was conducted on 37 students who were asked to write reflections for software architectural problems. The data was qualitatively analyzed using structural, in vivo and focused coding. Further, we used ttest to quantitatively analyze the data after receiving evaluation parameters from the qualitative analysis. The results are presented in this paper along with the themes generated and how reflections are effective when decomposition is explicitly stated. The work positively contributes towards design of effective PBL reflections. Keywords—Computational Thinking; Decomposition; Problem Based learning; Reflections.

Despite the widespread adoption of computational thinking (CT) across educational levels, challenges persist in its assessment due to diverse definitions, frameworks, and its practical application in classroom settings. This meta-synthesis investigates the assessment of computational thinking (CT) in primary and secondary education, synthesizing evidence from 12 reviews across five international databases, focusing on tools, methods, and pedagogical practices employed in assessing CT, with the aim to outline practical approaches for evaluating CT components. The review delves into the primary focuses of these syntheses, the CT skills and components assessed, and the methods and tools utilized, identifying gaps in current practices. The findings highlight a prevalent focus on programming skills, with less emphasis on cognitive processes and collaborative aspects of CT. The synthesis also points to the need for developing assessment tools and methods that encompass the broader spectrum of CT skills, suggesting avenues for future research and practical application in educational settings.

To help novice learners overcome the obstacles of learning computational thinking (CT) through programming, it is vital to identify difficult CT components. This study aimed to determine the computational concepts and practices that learners may have difficulties acquiring and discuss how programming instructions should be designed to facilitate learning CT in online learning environments. Participants included 92 undergraduate students enrolled in an online course. Data were collected from a CT knowledge test and coding journals. Results revealed that four computational concepts (i.e., parallelism, conditionals, data, and operators) and two computational practices (i.e., testing and debugging and abstracting and modularizing) were identified as CT components that were difficult to learn. The findings of this study imply that CT instructions should offer additional instructional supports to enhance the mastery of difficult computational concepts and practices. Further research is necessary to investigate instructional approaches to successful CT learning.

Computational Thinking (CT) has emerged in recent years as a thematic trend in education in many countries. initiatives have been developed for its inclusion in school curricula. There are many pedagogical strategies to promote the development of elementary school students’ CT skills and knowledge. Unplugged learning tasks, block-based programming projects, and educational robotics are 3 of the most used strategies. This paper aimed to analyze the effect of Scratch-based activities, developed during one scholar year, on the computational thinking skills developed and concepts achieved by 4th-grade students. The study involved 189 students from two school clusters organized into an experimental group and a control group. To assess students’ computational knowledge, the Beginners Computational Thinking Test developed by Several Zapata-Cáceres et al. (2020) was used. The results indicate statistically significant differences between the groups, in which students in the experimental group (who performed activities with scratch) scored higher on the test than students in the control group (who did not use Scratch).

This study investigates primarily the use of Computational Thinking in teaching mathematical concepts of Perimeter and Area. For those 3rd and 4th Grade students with learning disabilities, specifically, the extent to which their construction and understanding of mathematical concepts and their development of computational thinking are explored. For these reasons, the experimental research method is the one-group pretest-posttest design that we adopted which was conducted with 26 students, identified as learning disabilities by the committee for Identification and Placement of Gifted and Disabled Student. Data collected are analyzed with the Paired Sample t-test to comparison of these students in their learning outcomes and difference in aspects of construction of mathematical concepts and development of computational thinking after teaching. We find this teaching method enhances the learning outcomes and performance in the Math unit of Perimeter and Area and the performance of the development of computational thinking of these students after the intervention (160 minutes).

Computer scientists have been working towards a common definition of abstraction; however, the instruction and assessment of abstraction remain categorically under-researched. Abstraction is often cited as a component of computational thinking and can be likened to a higher order thinking skill. Most K-12 educators have studied critical thinking (e.g., Bloom's taxonomy, Marzano & Kendall's new taxonomy) more than computational thinking. Overlapping characteristics of abstraction and critical thinking can provide educators with instructional guidance. The qualitative examination of how teachers determine curriculum, deliver instruction, and design assessments in K-12 computer science education contributes insights into current instructional practices and variables for future quantitative study. The instructional strategies, objectives, and assessments of 12 K-12 computer science teachers from three states in the United States were examined in this descriptive qualitative examination of instruction using thematic coding analysis. The majority of teachers interviewed had little to no professional development regarding teaching abstraction. All teachers in the study were unsure what student abstraction abilities should be according to grade level. Teachers' understanding of abstraction ranged from very little knowledge to very knowledgeable. The majority of teachers interviewed did not assess abstraction intentionally. Teachers described successfully teaching abstraction through multiple instructional practices and spiraling curriculum. This examination of abstraction in K-12 computer science education offers practical descriptive insights and illuminates additional variables for researching the instruction of abstraction qualitatively and quantitatively. Anecdotal instructional successes are included in the results.

This research aims to examine the effect of Mind Games activities on problem-solving skills and computational thinking skills of 5th grade students. Sequential explanatory design, one of the mixed research methods, was used in the research. The research was carried out in a quasi-experimental design pretest-posttest without a control group with 17 5th grade students in a secondary school in Adıyaman. The research was carried out with an experimental period of 6 weeks. Before the experimental study, the problem-solving skills and computational thinking skills scales were applied to the students as a pre-test. Kapsul game activity was performed in the first week of the experimental study. ABC Baglama activity was held during the week. Sudoku activity was held in the 3rd week. In the 4th week, Three Stone game activities were held. Kendoku game activity was held in the 5th week. In the 6th week, Kakuro game activity was held. Statistical values in the study were calculated using the SPSS program. To collect quantitative data, a pre-test of problem-solving skills and computational thinking skills was applied to the students before the experimental process. After the experimental process, problem-solving skills and computational thinking skills scales were applied as a posttest. The applied pre-test and post-test results were analyzed with the paired sample t-test. Additionally, to collect qualitative data to strengthen the research, an interview form was applied to obtain the opinions of the students about the experimental process. In line with the findings obtained because of the experimental study, it can be said that the mind game activities have a positive effect on the students' computational thinking and problem-solving skills, and the lesson turns into more fun.

Computational thinking is an important and necessary part of a modern person’s thinking. It has been proven that the development of this way of thinking in students with mental disabilities allows them to navigate quickly in the modern world, identify problems and create complex solutions. Online schooling during the COVID-19 pandemic demonstrated the possibilities of the usage of web technologies in the education of children with mental disabilities. This study aims to evaluate the impact of web technologies on the development of computational thinking of students with mental disorders. The experiment involved 14 students aged 8-12 and 4 tutors. For 8 weeks children were trained in computational thinking and computer science. Assessment of computational thinking was performed with cCT-test by El-Hamamsi et al. before and after the experiment. After conducting computer science lessons using web technologies the respondents showed a higher level of computational thinking (M=15,7, SD=3,69), compared to the results of preliminary testing (M=5,93, SD=2,3). Web technologies can significantly increase the effectiveness of inclusive pedagogy, which establishes the importance of integrating web technologies into the teaching system in inclusive classes of general education schools.