Component Of Computational Thinking Research Articles

Developing a Teaching Model to Enhance Computational Thinking for Higher Vocational College Students

The goals of this study were: 1) to study the problems and teaching needs of enhancing computational thinking for higher vocational college students. 2) to develop a teaching model to enhance computational thinking for higher vocational college students. 3) to study the effectiveness of implementing the teaching model to enhance computational thinking for higher vocational college students. The samples were 86 students and 3 teachers in the preliminary survey phase, and 36 students in the implementation phase. Data analysis was done with mean, percentage, standard deviation, content analysis and paired-samples t-test. The research findings showed that the proposed teaching model consisting of eight components and six teaching steps to enhance computational thinking for higher vocational college students. Its significance includes to enhance 4 components of computational thinking: decomposition, pattern recognition, abstraction, algorithm. The experimental students’ post-test scores were statistically significantly higher than the pre-test by .05. The results revealed that, the proposed teaching model can effectively enhance the computational thinking for higher vocational college students. Based on the findings of this study, the researchers recommended that teacher combining new technologies such as AI, to provide students with more scaffolding and teaching support and further investigate the effectiveness of this teaching model in enhancing computational thinking in non-programming teaching.

A bibliometric analysis of computational thinking skills: definition, components and assessment tools

This paper provides an in-depth analysis of computational thinking (CT) skills and their assessment. It discusses the definition and components of CT. Various assessment tools, such as surveys, exams, self-assessment instruments, and performance tests, are explored. A bibliometric analysis reveals a growing trend in publications related to CT skills since 1993. The study employs citation analysis, co-citation analysis, and co-keyword analysis, identifying clusters of related work and emphasizing the core ideas of CT skills, their components, and their assessment. The findings highlight the intellectual synergy between publications, particularly the strong conceptual and thematic links between works that often cite each other. The discussion underscores the importance of CT components like abstraction, decomposition, and algorithms, as foundational elements across disciplines, particularly in education and computer science. Additionally, the integration of CT into educational curricula, such as robotics, programming, and STEM, demonstrates its growing significance. The conclusion identifies several gaps in the current understanding of CT, particularly the need for a standardized, widely accepted definition that encompasses all aspects of CT. It also emphasizes the limited focus on the assessment of CT skills and calls for the development of validated and reliable assessment tools. Furthermore, the paper highlights the need for more research on the impact of teacher training programs on CT development, to ensure effective integration at the school level.

Exploration Of Computational Thinking Skills Of Junior High School Student In Term Of FI And FD Cognitive Style

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.

Development of Integrated Biodiversity Student Worksheets With Computational Thinking

Computational thinking is used in learning to improve problem-solving skills in Biology subjects. Classroom learning emphasizes students memorizing biological science concepts in problem-solving, resulting in students' computational thinking skills needing to be improved. So, efforts to overcome this so that students are actively involved in the learning process and to improve students' computational thinking skills in understanding biodiversity material are by providing teaching materials such as LKPD. This study combines the concept of biodiversity with computational thinking components in worksheets so that students can develop computational skills while understanding Biology concepts. This development research aims to develop student worksheets by integrating computational thinking into biodiversity material. This development research was conducted using the 4-D method through 4 stages: define, design, develop, and disseminate. Data was collected using a questionnaire instrument by two validators consisting of language experts, material experts, and teaching material experts, as well as a readability test instrument by three teachers and ten students in class X at SMAN 3 Depok. The results of this study are in the form of student worksheets, which were successfully developed and suitable for use in learning because they obtained a valid category based on assessments from validators and a highly valid category from biology teachers and students.

Computational Thinking Assessment in Primary and Secondary Education: A Meta-synthesis of tools, Methods and Pedagogical Approaches

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.

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.

Improving mathematical proof based on computational thinking components for prospective teachers in abstract algebra courses

Understanding and constructing mathematical proofs is fundamental for students in abstract algebra courses. The computational thinking approach can aid the process of compiling mathematical proofs. This study examined the impact of integrating computational thinking components in constructing mathematical proofs. The researcher employed a sequential explanatory approach to ascertain the enhancement of algebraic proof capability based on computational thinking through the Wilcoxon test. A total of 32 prospective teachers in mathematics education programs were provided with worksheets for seven meetings, which were combined with computational thinking stages. Quantitative data were collected from initial and subsequent test instruments. Moreover, three prospective teachers were examined through case studies to investigate their mathematical proof capability using computational thinking components, including decomposition, abstraction, pattern recognition, and algorithmic thinking. The study's findings indicated that CT intervention enhanced students' logical reasoning, proof-writing abilities, and overall engagement with abstract algebra concepts. The findings illustrate that integrating computational thinking into learning strategies can provide a framework for developing higher-order thinking skills, especially in proving, which are essential for studies in mathematics education programs.

Computational Thinking Processes in Solving the Corona Epidemic Model: Pre-service Maths Teachers

In the 21st century, pre-service mathematics teachers are expected to have problem-solving skills that are effective, efficient, and solutive and are in line with the mindset of computer experts. In learning mathematics, the concept of computational thinking (CT) is also needed and at this time, many still have difficulty solving mathematical problems in general, especially in solving problems in epidemic mathematical models. The subjects of this study were twenty-seven pre-service mathematics teacher students who took mathematical modeling courses. The researcher used the purposive sampling technique to select two research samples. The research method used was a descriptive qualitative research method in exploring the thinking process of pre-service mathematics teacher students in solving the problem of modeling the epidemic spread of disease. The results showed that the thinking process of the first subject began with identifying the problem and existing information by writing down the data in the form of a graph so as to get a certain pattern, which was then used as the basis for the process of transforming the problem into mathematical language. By adding assumptions related to the existence of environmental limitations in the next epidemic model, the concept of differential equations, in which there are integral properties and natural logarithms, can be used to find the solution to the epidemic model. The second subject was unable to solve the integral at hand. The researcher discovered that pre-service mathematics teacher students who correctly solved the problem in the mathematical model used CT components, namely decomposition, abstraction, pattern recognition, algorithm and mathematical literacy.

Computational Thinking Integration into Pre-Service Science Teacher Education: A Systematic Review

ABSTRACT Computational Thinking (CT) has emerged as a fundamental aspect of modern science education, especially within pre-service teacher education. This study examines the current landscape of CT integration in pre-service science teacher education, drawing insights from an analysis of 18 empirical studies conducted since the implementation of the Next Generation Science Standards in 2013. The findings reveal the predominantly exploratory nature of research in this area and underscore the prevalence of positive outcomes associated with CT integration. Moreover, the study identifies prominent pedagogical strategies such as problem-based learning and the engineering design process, highlighting their integral roles in CT integration. Additionally, the findings uncover the interconnectedness of modeling and simulation, computational problem-solving, and systems thinking practices within CT-integrated science interventions. This suggests the necessity for a more holistic approach, including the integration of unplugged activities, the generalization component of CT, and diverse science disciplines, encompassing life science and Earth/space science. Ultimately, these findings emphasize the imperative for further investigation to comprehensively explore CT integration within pre-service science teacher education, aligning closely with contemporary educational standards and practices.

High-Capacity Robots in Early Education: Developing Computational Thinking with a Voice-Controlled Collaborative Robot

This study investigates the potential of a voice-controlled collaborative robot to promote computational thinking (CT) skills in preschoolers aged 4 to 6 in Southern Chile. During a week-long workshop, 34 children engaged in activities designed to develop several CT components while interacting with a robotic arm. Pre- and post-workshop assessments utilizing the TechCheck-K tool revealed significant improvements in CT skills. The findings of this preliminary study demonstrate the effectiveness of voice-controlled robots as innovative tools in early education, significantly enhancing young learners’ understanding of CT concepts, practices, and perspectives. This research advocates for further exploration of robotic integration in educational settings, highlighting their potential to improve or extend preschool learning environments and foster CT skill development.

Computational Thinking Measurement of CS University Students

The measurement of computational thinking ability among computer science (CS) university students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of computational thinking in CS students. Utilising two key instruments, namely, the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of computational thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and programme specialisations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to the measurement of variations in students’ computational thinking and programming skills by gender, university level, and location. By shedding light on the significance of computational thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.

The effect of computer science unplugged on abstraction as a sub-component of computational thinking

Since computational thinking is a high-level cognitive skill and its conceptual framework has not yet been clarified, the assessment of this skill is problematic. While there is no consensus in the literature about the components of computational thinking, the abstraction component is located at a point where opinions intersect. In addition, it is predicted that computer science unplugged activities will improve computational thinking, and there is limited research examining this effect. As a result, this research aims to demonstrate the alleged effects of computer science unplugged activities on abstraction skill which is considered the critical component of computational thinking. The study, which was planned with a mixed methods research design, lasted 8 weeks. Quantitative data were obtained from the abstraction skill test developed by the researchers, while qualitative data were obtained from observations and task- based interviews after each activity. Quantitative findings showed that computer science unplugged activities significantly improve abstraction skills and thus computational thinking. Qualitative findings not only revealed that developments in the six abstracting steps caused this significant difference but also identified abstracting steps where the effect of each activity was limited. Suggestions for organizing activities to improve these steps are given. Finally, suggestions are presented for the conceptual framework of computational thinking based on abstraction.

Fostering computational thinking through digital storytelling: a distinctive approach to promoting computational thinking skills of pre-service teachers

The present study illustrates digital storytelling (DS) as an open-ended learning environment, which is one of the ways of preparing pre-service teachers (PST) to practice computational thinking (CT) in their future teaching. An 8-week teacher training program was organized and presented. The study aims to investigate the program’s impact on the PST's CT skills, CT-integrated DS design skills, and their perspectives. A study group of fifty-two second-year PSTs took part in the Instructional Technologies course. An embedded mixed-method design was used. The CT scale was used as a pre-test and post-test to examine the development of the PST’s CT skills for quantitative data collection. The qualitative data comprise the CT-integrated DS projects developed by the PST during the training and the reflection reports they wrote at the end of the training. The paired samples t-test was used in the analysis of quantitative data. The DS projects were evaluated using the rubrics with the story/storyboard, DS, and CT skills dimensions. Reflection reports were analyzed using thematic analysis. The findings revealed a significant improvement in the PST’s CT skills after the training program. The qualitative findings show the PST’s perspectives on CT definitions, the perceived benefits of CT and DS, and the use of DS to promote CT. The qualitative findings also present evidence for the CT-integrated DS design skills of the PST. Therefore, the study presents early findings showing that CT components could be integrated with DS and used in teacher education effectively without any pressure to use programming tools. The study also shows how DS is gradually integrated with the CT components as a suggestion for future studies.

Dublin Descriptors for Problem Scenarios in Problem Based Learning

Problem-based learning has been one of the major pedagogies that support various learning styles, utilizing problems as a key element in studies. While there are many ways to solve a problem, Computational Thinking can be one of them. Typically, we formulate learning outcomes and design assessments to attain these goals. However, there are cases where a single outcome may not suffice, especially with multi-layered problems. In such scenarios, we propose using Dublin Descriptors and evaluate the study using system design problems. Two case studies were designed, requiring students to reflect on the components of computational thinking. Reflections in the second case study were designed according to the written Dublin Descriptors. We conducted qualitative and quantitative analyses on data from 37 students in two case studies to identify themes related to Bloom's and Dublin Descriptors, using In Vivo, Descriptive, and Focus coding in two rounds of analysis. Statistical inferences suggest that the method holds promise for cases requiring advancement beyond Level 4. Dublin Descriptors show promise in cases where information needs to be communicated from ideas to inferences. Keywords—Computational Thinking; Dublin Descriptors, Problem-Based learning; Reflections, System Design

Reflections and Decomposition in Problem Based 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.

Predicting Computational Thinking in Elementary Science Lessons Using a Multilevel Model Approach

Computational thinking (CT) is an essential problem-solving skill that students need to successfully live and work with developing technologies. There is an increasing call in the literature by researchers and policy leaders to integrate CT at the elementary level into core subjects to provide early and equitable access for all students. While some critics may claim the concepts and skills of CT are developmentally advanced for elementary age students, subjects such as science can provide real-world and relevant problems to which foundational CT components can be applied. By assessing how CT concepts and approaches integrate authentically into current science lessons, policymakers, and district leaders can be more intentional in supporting implementation efforts. This research used an exploratory survey design to examine the frequencies of CT concepts (decomposition, algorithms, abstraction, and pattern recognition) and approaches (tinkering, creating, debugging, perseverance, and collaboration) that exist in science in K–5 schools in a northeast state in the United States as reported by elementary science teachers (n = 259). Hierarchical linear modeling was used to analyze the influence of teacher and district factors on the amount of time CT concepts and approaches were integrated in the science lessons. Experience, grade level, confidence, and participation in a research–practice partnership were found to be significant predictors of CT. This study contributes to a better understanding of variables affecting CT teaching frequency that can be leveraged to impact reform efforts supporting CT integration in science.

The Analysis of Pre-Service Physics Teachers’ Computational Thinking Skills in Designing Computer Simulations of Projectile Motion Using the Scratch Application

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.

Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses

According to numerous researchers, a clear and direct correlation exists between Computational Thinking (CT) and courses falling under the purview of Science, Technology, Engineering, and Mathematics (STEM), thereby advocating for the integration of CT into the curricula of STEM courses. Nonetheless, it is noteworthy that only a few studies have scrutinized this correlation in-depth. Most such studies connect the correlation tacitly and predominantly concentrate on the empirical assessment of CT within the curriculum of one STEM discipline. This research seeks to evaluate the Computational Thinking abilities of 80 high school students in Greece and discern the extent of correlation with their academic performance in STEM and Greek language courses. A longitudinal survey was executed to accomplish this objective, commencing with administering a test designed to gauge the fundamental components of Computational Thinking. It is worth noting that this test draws its inspiration from internationally recognized computer competitions and serves as a credible assessment tool. Subsequently, an assessment was carried out to ascertain the degree of correlation between students’ Computational Thinking aptitude and their written performance in the subjects encompassed by the STEM category and the Greek language courses. The outcomes of this investigation revealed the presence of a statistically significant correlation between students’ Computational Thinking proficiency and their performance in these academic subjects, further extending to the academic direction of study chosen by the students. Based on the findings of this research, implications and pedagogical recommendations are delineated while concurrently acknowledging the limitations encountered during this study.

BERPIKIR KOMPUTASIONAL SISWA SMP DALAM MENYELESAIKAN MASALAH MATEMATIKA

The important skill to be applied in the 21st century is computational thinking (CT). Students need to develop computational thinking skills by being trained to solve open-ended problems. The descriptive qualitative method is the method used in this research. This research aims to describe the computational thinking skills of junior high school students in solving open-ended mathematical problems by explaining each component of computational thinking, namely abstraction, decomposition, algorithmic thinking, generalization, and debugging. The subjects of the research were several 8th-grade students from a private junior high school in Surabaya, consisting of 55 students who had been given TKM and TPM. Then, 3 students with different categories who provided unique answers were selected. Student in the low computational thinking category (BK1) do not fulfill all computational components. Student in the moderate category (BK2) have incomplete results in the final solution, and the problem-solving methods provided are also inadequate, resulting in imperfect final outcomes. Student in the high computational thinking category fulfill all computational components. Student (BK3) provides correct and varied problem-solving methods and solutions.
 

Educational Robotics: Development of computational thinking in collaborative online learning.

In the previous study the work experience on organization of teaching Robotics to secondary school students at school lessons and in study groups was introduced. This study which was conducted within 2019 and 2021 covered the period of distant learning caused by COVID-19 pandemic and even post-pandemic period, when a part of school students continued learning online. The study deals with the problem of developing school students' computational thinking in online learning. We consider computational thinking as a set of cognitive skills of solving educational and cognitive problems. The research questions raised were aimed at solving the problem of the influence of Educational Robotics on developing computational thinking. During the research we have found out that due to the adaptability of robots, Educational Robotics, the development of individual learning programs, and the arrangement of collaborative online learning are instruments and a solution to the problem of developing computational thinking. The main components of computational thinking, which were studied within those 3 years, are the following: algorithmic thinking, ability to program, and efficiency in team work. The influence of the learning strategy we chose enabled us to determine the level of computational thinking and its dependence on learning Robotics. We used statistical criteria in order to summarize the results of our research. The statistics provided suggests progress in the indicator tracked. Based on the experimental data received we approximated reliability (R2) and relevant exponential equation (trend lines). The research we carried out also has led to the general conclusion that Educational Robotics helps to create synergistic learning environment for stimulating students' motivation, collaboration, self-efficacy and creativity.