Component Of Computational Thinking Research Articles

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

AbstractThe 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.

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.

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.

Components of computational thinking in citizen science games and its contribution to reasoning for complexity through digital game-based learning: A framework proposal

Education has undergone many changes in teaching and learning, intensified by the significant technological developments that have responded to the fourth industrial revolution and other emergent situations. In this context, developing information and communication technologies has become vital in supporting new ways and learning models in the various educational levels to address a complicated environment where individuals must have complex and computational skills to respond to challenges. This study proposes a complex thinking framework that links citizen science and digital game-based learning to develop university students’ computational thinking skills. The results indicate that (a) it is possible to consider the sub-competencies of complex thinking in the design of a digital citizen-science game to develop computational thinking, and (b) the digital game-based learning framework for citizen science topics can potentially increase students’ engagement and teamwork in data collection and analysis while building their knowledge and computational thinking skills, and their complex thinking competency and sub-competencies.

Student’s Computational Thinking Ability in Solving Trigonometry Problems in the Review of Self-Regulated Learning

This research is motivated by the habits we often encounter in learning, especially in mathematics. Each student has different computational thinking abilities. Computational thinking ability is a thinking ability that supports problem-solving solutions. Computational thinking components include decomposition, pattern recognition, abstraction, and algorithm design. This research aims to: 1) Describe the computational thinking abilities of students with high self-regulated learning in solving trigonometry problems, 2) Describe the computational thinking abilities of students with moderate self-regulated learning in solving trigonometry problems, 3) Describe the computational thinking abilities of students with low self-regulated learning in solving trigonometry problems. This research used a qualitative approach with a case study type of research. This research was conducted at SMKN 2 Tulungagung which was attended by all students of class XI TKRO 3, totaling 32 students. Of the 32 students, 6 students will be selected as subjects who are classified based on the level of self-regulated learning. Data collection techniques used are observation, tests, interviews, and documentation. Data analysis techniques were carried out through the stages of data collection, data presentation, and conclusion. The results of this research indicate that: 1) students with high self-regulated learning can fulfill 3-4 indicators of computational thinking skills in solving trigonometry problems, 2) students with moderate self-regulated learning can fulfill 2-3 indicators of computational thinking skills in solving trigonometry problems, 3) and students with low self-regulated learning can fulfill 0-1 indicators of computational thinking skills in solving trigonometry problems

A Complementary View to Computational Thinking and Its Interplay with Systems Thinking

Computational Thinking (CT) pervasively shares its methods, practices, and dispositions across other disciplines as a new way of thinking about problem-solving. Few studies have been carried out studying CT from an Information Systems (IS) perspective. This study elaborates on how systems thinking (ST), an acknowledged theory in the IS field, bonds to CT to address some well-known common issues related to CT such as reductionism and dogmatism, and to supplement the computing nature of CT with behavioral and societal facets involved in its implications. We studied how ST is applied to CT research in the literature. To do so, two primary approaches have been identified that link ST and CT. First, ST is embedded in CT practices meaning that ST is considered as a component of CT. Second, ST and CT are parallelly studied, and ST is considered as a supplementary concept to CT. Correspondingly, we propose a complementary approach that looks at CT from the ST lenses to provide a clearer picture of CT in an educational context. Moreover, we expect this new perspective can help to broaden the development of educational CT concepts and scenarios by including new notions such as framework, interpretation, norms, paradigm, and context.

Introducing schoolchildren to computational thinking using smartphone apps: A way to encourage enrollment in engineering education

AbstractComputational thinking is the process of solving a problem in a way that can be readily automated by a computer. Decomposition, pattern recognition, abstraction, and algorithm design are four major components of computational thinking. We conducted an experiment to study the effectiveness of smartphone apps for teaching computational thinking to 7‐year‐old children. The first experimental group was taught computational thinking using four apps, one for each component. The second experimental group was taught computational thinking using the chalk and board approach. The control group was not imparted any lesson in computational thinking. The intervention period lasted 8 weeks and was followed by a posttest and a retention test conducted after another 2 weeks. The children showed interest in learning computational thinking and could solve problems that are inspired by real‐world engineering problems during the intervention period. They could analyze and debug (77%) their own work. The performance of the children in the two experiment groups was significantly better than those in the control group in the posttest and the retention test (F = 26.470, p < .05). This means that 7‐year‐old children can learn computational thinking from suitable mediums. No significant difference was observed in the performance of the children in the posttest and the retention test (p > .05), denoting that children can retain the concepts of computational thinking. Including computational thinking lessons at the K‐12 level can encourage children to enroll in engineering programs in the future. We recommend standardization of content and development of age‐specific apps for teaching computational thinking.

An Investigation of Affective Factors Influencing Computational Thinking and Problem-Solving

This study investigated the effects of affective factors on computational thinking and problem-solving. Computer science subjects are becoming part of the regular curricula in K-12 and higher education to enhance computational problem-solving skills. However, affective factors influencing computational thinking skills and computational thinking components predicting problem-solving skills have yet to be fully explored. This paper proposed a conceptual model to predict (a) four affective factors that influence computational thinking self-efficacy and (b) six computational thinking components that affect problem-solving self-efficacy. Structural equation modeling was used to analyze self-report data from college students to examine the direct relationships among study variables. The findings showed that two affective factors (i.e., programming self-efficacy and computer science usefulness) significantly predicted computational thinking self-efficacy and influenced problem-solving self-efficacy. Also, two computational thinking components (i.e., algorithm and debugging) were the significant determinants of problem-solving self-efficacy. The results validate the importance of affective factors in computer science education and suggest specific computational thinking activities that should be emphasized in computer science curricula to facilitate problem-solving skills.

An intelligent model to assess the level of the computational algorithmic component of students’ computational thinking

The importance of developing students’ computational thinking (CT) determines the need to devise an objective and valid method for assessing its level. In this regard, it is relevant to build a universal model for diagnosing computational thinking on the basis of mathematical methods. The article aims to develop a model for diagnosing students’ computational algorithmic thinking. This model makes it possible to automate assessment procedures and contributes to developing techniques for enhancing this cognitive ability of learners. The diagnostic evaluation of students’ computational algorithmic component of CT includes an assessment of their subject knowledge, cognitive abilities, and cognitive activity. In this regard, we identified three diagnostic criteria. The content criterion (CC) is determined by the knowledge acquired through subject-specific learning. The operational criterion (OC) is a set of identified intellectual strategies necessary to solve problems using computers. The cognitive activity criterion (CAC) is a cognitive-psychological response to the cognitive process. It is manifested in the intensification of learning activity, a strong focus on a proactive attitude, independence, and a creative approach to learning. It is necessary to establish indicators to provide three assessment levels for each criterion, namely low, medium, and high. The integrated assessment of the level of the computational algorithmic component of a student’s CT is determined by the total of these three criteria values. For the adopted diagnostic model, we have developed a method based on cluster analysis to determine the CT level. The object of recognition is a learner with an information vector (CC, OC, CAC). The weights of the vector elements are determined by experts. The array of students with their information vectors is divided into three classes: L1 (low level), L2 (medium level), L3 (high level). The diagnostics of the computational algorithmic component level of a particular student’s CT consists in determining their belonging to the corresponding group. The method devised to assess the level of the computational algorithmic component of students’ CT was implemented as a software product. The proposed original model for diagnosing students’ CT provides an opportunity to automate assessment procedures. It also facilitates the integrated assessments of the diagnostic characteristics of the learning process. The materials of this work are of interest to teachers and employees of the educational departments of universities, and also have theoretical significance for the development of the theory and practice of diagnosing educational results.

Impact of programming on primary mathematics learning

The aim of this study is to investigate whether a programming activity might serve as a learning vehicle for mathematics acquisition in grades four and five. For this purpose, the effects of a programming activity, an essential component of computational thinking, were evaluated on learning outcomes of three mathematical notions: Euclidean division (N = 1,880), additive decomposition (N = 1,763) and fractions (N = 644). Classes were randomly assigned to the programming (with Scratch) and control conditions. Multilevel analyses indicate negative effects (effect size range −0.16 to −0.21) of the programming condition for the three mathematical notions. A potential explanation of these results is the difficulties in the transfer of learning from programming to mathematics.

Discovering computational thinking in everyday problem solving: A multiple case study of route planning

AbstractBackgroundThe idea of computational thinking is underpinned by the belief that anyone can learn and use the underlying concepts of computer science to solve everyday problems. However, most studies on the topic have investigated the development of computational thinking through programming activities, which are cognitively demanding. There is a dearth of evidence on how computational thinking augments everyday problem solving when it is decontextualized from programming.ObjectivesIn this study, we examined how computational thinking, when untangled from the haze of programming, is demonstrated in everyday problem solving, and investigated the features of such solvable problems.MethodsUsing a multiple case study approach, we tracked how seven university students used computational thinking to solve the everyday problem of a route planning task as part of an 8‐week‐long Python programming course.Results and ConclusionsComputational thinking practices are latent in everyday problems, and intentionally structuring everyday problems is valuable for discovering the applicability of computational thinking. Decomposition and abstraction are prominent computational thinking components used to simplify everyday problem solving.ImplicationsIt is important to strike a balance between the correctness of algorithms and simplification of the process of everyday problem solving.

Computational Thinking Process of Prospective Mathematics Teacher in Solving Diophantine Linear Equation Problems

<p style="text-align: justify;">Prospective teachers facing the 21st century are expected to have the ability to solve problems with a computer mindset. Problems in learning mathematics also require the concept of computational thinking (CT). However, many still find it challenging to solve this problem. The subjects in this study were twenty-one prospective mathematics teachers who took number theory courses, and then two research samples were selected using the purposive sampling technique. This study uses a qualitative descriptive method to describe the thinking process of prospective teachers in solving Diophantine linear equation problems. The results showed that the first subject's thought process was started by turning the problem into a mathematical symbol, looking for the Largest Common Factor (LCF) with the Euclidean algorithm, decomposition process, and evaluation. The second subject does not turn the problem into symbols and does not step back in the algorithm. The researcher found that teacher candidates who found solutions correctly in their thinking process solved mathematical problem used CT components, including reflective abstraction thinking, algorithmic thinking, decomposition, and evaluation. Further research is needed to develop the CT components from the findings of this study on other materials through learning with a CT approach.</p>

The importance of computational thinking training for primary school teachers

The importance of modern schools in developing students’ problem-solving skills, including through digital tools, is described in the article, which includes the development of basic coding skills and digital literacy, as well as the ability to solve problems and make decisions based on planning and analysis of situations. Computational thinking (CT) is built on the foundation of these abilities. The authors contend that the employment of specialized digital tools promotes the development of computational thinking and that purposeful creation of computational thinking improves teachers’ and students’ digital competence. The concept of CT, as well as existing definitions and components, are examined in this article. A list of courses from various countries’ curriculum on which CT is studied in primary school is provided. The importance of CT as a fundamental talent for everyone is underlined, and it should be developed through the integration of several disciplines to solve problems. The link between the CT components and the key competencies that should be instilled in kids as part of the primary education curriculum. The findings of a survey of more than 60 primary school teachers from various regions of Ukraine about their understanding of the concept of computational thinking and their experience in the development of skills related to all components of computational thinking in primary school students are presented. A review of open educational resources from around the world is offered to assist teachers in enhancing students’ CT skills. The strategy for putting the CT development concept into practice for future teachers and primary school teachers is offered, taking into account the ability to employ specific digital tools.

Difficult Concepts and Practices of Computational Thinking Using Block-based Programming

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.

Sequential Relational Decomposition

The concept of decomposition in computer science and engineering is considered a fundamental component of computational thinking and is prevalent in design of algorithms, software construction, hardware design, and more. We propose a simple and natural formalization of sequential decomposition, in which a task is decomposed into two sequential sub-tasks, with the first sub-task to be executed before the second sub-task is executed. These tasks are specified by means of input/output relations. We define and study decomposition problems, which is to decide whether a given specification can be sequentially decomposed. Our main result is that decomposition itself is a difficult computational problem. More specifically, we study decomposition problems in three settings: where the input task is specified explicitly, by means of Boolean circuits, and by means of automatic relations. We show that in the first setting decomposition is NP-complete, in the second setting it is NEXPTIME-complete, and in the third setting there is evidence to suggest that it is undecidable. Our results indicate that the intuitive idea of decomposition as a system-design approach requires further investigation. In particular, we show that adding a human to the loop by asking for a decomposition hint lowers the complexity of decomposition problems considerably.