Component Of Computational Thinking Research Articles

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.

Assessing Computational Thinking: Development and Validation of the Algorithmic Thinking Test for Adults

This study describes the development and validation process of a computational thinking (CT) test for adults. The team designed a set of items and explored a subset of those through two qualitative pilots. Then, in order to provide validity evidence based on the test content, a team of 11 subject-matter experts coded the initial pool of items using two different systems of categories based on CT components and contents. Next, the items were piloted on a sample of 289 participants, 137 experts in CT, and 152 novices. After a series of confirmatory factor analyses, a unidimensional model that represents algorithmic thinking was adopted. Further analyzing the psychometric quality of the 27 items, 20 of them with excellent reliability indices were selected for the test. Thus, this study provides a tool to evaluate adults’ CT: the Algorithmic Thinking Test for Adults (ATTA), which was developed according to psychometric standards. This article also reflects on the nature of CT as a construct.

Unleashing the Potential of Abstraction From Cloud of Computational Thinking: A Systematic Review of Literature

Although abstraction is widely understood to be one of the primary components of computational thinking, the roots of abstraction may be traced back to different fields. Hence, the meaning of abstraction in the context of computational thinking is often confounded, as researchers interpret abstraction through diverse lenses. To disentangle these conceptual threads and gain insight into the operationalisation of abstraction, a systematic review of 96 empirical studies was undertaken. Analysis revealed that identifying features of entities, extracting relevant features, discovering patterns, creating rules and assembling the parts together were the core actions of abstraction. With the primary aim of simplifying practical procedures, abstraction was operationalised as the sophistication of a program, the matching of patterns, the creation of alternative representations, the transfer of solutions, the measurement of a learner’s activity and reading program codes. There is an obvious need for researchers to align the conceptual meanings they have established of abstraction with the practical facts of operationalisation. The need to empirically validate emerging models and the implications for future research are discussed.

School-university cluster of disciplines developing the calculative-algorithmic component of computational thinking

Problem relevance. Due to the multi-departmental concepts and the different content of educational programs of schools and universities, a serious problem arises of the succession and continuity of the education system along the “vertical” in general and subject teaching in particular. Another didactic problem is the need to ensure interdisciplinary connections of basic courses in the traditional disciplinary model of the educational process for more effective and expedient formation of certain student’s competencies sets. In this regard, it is of interest to create new organizational and meaningful approaches to training specialists without a significant restructuring of the traditional educational process.The purpose of the article is to substantiate a collaborative model of subject training of students in a school-university cluster of disciplines, which ensures the succession and continuity of education at school and university.Methodological basis. On the example of three disciplines “Programming”, “Computanional Methods”, “Information Technologies in Education”, a cluster model of teaching schoolchildren and students in the direction of training “Mathematics and Computer Science” has been designed and implemented. A feature of the considered school-university cluster of disciplines is a unified methodological base of target, meaningful and didactic elements that form and develop the calculative-algorithmic component of the computational thinking of students. The basis of the means and methods of teaching in the cluster is made up of cognitive techniques and a platform of “computational and algorithmic primitives” — solving elementary task template. A recursive approach is used in the methods of cluster subject teaching of schoolchildren and students.Results and Conclusions. The model of the created disciplinary cluster “Programming — Computanional Methods — Information Technologies in Education” contributes to the formation and development of the calculative-algorithmic component of the computational thinking of schoolchildren and students, and also forms their assigned groups of competencies. The school-university cluster of disciplines ensures real succession and continuity of school and university education, without unnecessary, sometimes artificial, labor-intensive additional organizational and methodological means and techniques. The approach under consideration can be used to create clusters of disciplines in various educational areas, allowing their meaningful collaboration and forming given competencies sets and schoolchildren’s and student’s cognitive abilities.

Discovering the Role of Problem-Solving and Discussion Techniques in the Teaching Programming Environment to Improve Students' Computational Thinking Skills

Computational thinking skill is one of the essential abilities to be learned and perfected by students of this century. Studies have shown that in the teaching and learning of programming courses, discussion and problem-solving techniques have been widely used. However, studies based on the suitability of such teaching techniques for the development of the computational thinking skills of students are, however, lacking. In this context, this research was conducted to define the teaching techniques used by university lecturers when teaching a computer programming subject and to explore how the techniques can influence the development of the computational thinking skills of students. This research was based on a combination of qualitative and quantitative approaches involving a semi-structured interview and a survey method, respectively. The research sample consisted of eight (8) university lecturers recruited from several Malaysian public universities, who had been teaching computer science to undergraduates. The results showed that in teaching computer programming, a majority of the respondents used discussion and problem-solving methods, with each assisting students to gain computer programming skills and learn certain components of computational thinking. As such, it is recommended that teaching practitioners incorporate the discussion and problem-solving techniques in the teaching and learning of programming courses. The incorporation of such strategies will help students develop good computer programming and computational thinking skills encompassing all the fundamental elements. The results also revealed that the respondents had no experience in using the metacognitive technique. As such, it is also proposed that future research should focus on this technique to investigate any possible effects that it may have on the growth of the computer programming and computational thinking skills of undergraduates.

Still a New Kid on the Block? Computational Thinking as Problem Solving in Code.org

Computational thinking is a means to understand and solve complex problems through using computer science concepts and techniques. While there is an increase in the number of initiatives focusing on coding, whether they (a) address computational thinking and problem-solving skills and (b) use methods of teaching problem solving is yet to be explored. As a preliminary step, this study focuses on examining 3rd grade lesson plans at Code.org. The analysis identified various components of computational thinking and problem solving as well as specific problem-solving teaching methods used to address these skills. Besides such cognitive outcomes, the findings also revealed affective, reflective, and social aspects of learning supported in the lessons.

Understanding Biological Evolution Through Computational Thinking

Computational thinking is a contemporary science and engineering practice that has been introduced to the US science classrooms due to its emphasis in the Next Generation Science Standards (NGSS). However, including computational thinking into science instruction may be challenging. Therefore, for biological evolution (an essential theory within biology that spans across temporal and organizational scales), we recommend integrating computational thinking into evolution teaching to overcome misconceptions, reinforce the nature of science (NOS), and allow student embodiment (as students become emerged in their models, i.e., personification). We present a learning progression, which outlines biological evolution learning coupled with computational thinking. The defined components of computational thinking (input, integration, output, and feedback) are integrated with biology student roles. The complex nature of both teaching computational thinking and biological evolution lends toward a learning progression that identifies instructional context, computational product, and computational process and spans from simple to complex. Two major themes of biological evolution, unity and diversity have each been paired with both computational thinking and specific corresponding NGSS standards at levels of increasing complexity. There are virtually no previous studies which relate computation and evolution across scales, which paves the way for questions of importance, support, benefits, and overall student achievement in relation to the advancement of science in education.

Characterizing Computational Thinking for the Learning of Tertiary Educational Programs

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.

The influence of SRA programming on algorithmic thinking and self-efficacy using Lego robotics in two types of instruction

This study investigates the development of algorithmic thinking as a part of computational thinking skills and self-efficacy of primary school pupils using programmable robots in different instruction variants. Computational thinking is defined in the context of twenty-first century skills and describes processes involved in (re)formulating a problem in a way that a computer can process it. Programming robots offers specific affordances as it can be used to develop programs following a Sense-Reason-Act (SRA) cycle. The literature provides evidence that programming robots has the potential to enhance algorithmic thinking as a component of computational thinking. Specifically there are indications that pupils who use SRA-programming learn algorithmic skills better and achieve a higher level of self-efficacy in an open, scaffold learning environment than through direct instruction. In order to determine the influence of the instruction variant used, an experimental research design was made in which pupils solved algorithm-based mathematical problems (grid diagrams) in a preliminary measurement and their self-efficacy determined via a questionnaire. As an intervention, pupils learn to solve programming issues in pairs using “Lego NXT” robots and “Mindstorms” software in two instruction variants. The post-measurement consists of a Lego challenge, solving mathematical problems (grid diagrams), and a repeated self-efficacy questionnaire. This research shows an increase of our measures on algorithmic thinking dependent on the amount of SRA usage (though not significant). Programming using the SRA-cycle can be considered as the cause of the measured effect. The instruction variant used during the robotic intervention seems to play only a marginal role.