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Similarities and differences between CT and mathematical thinking. Adapted from Sneider et al. (2014). 

Similarities and differences between CT and mathematical thinking. Adapted from Sneider et al. (2014). 

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This paper examines the growing field of computational thinking (CT) in education. A review of the relevant literature shows a diversity in definitions, interventions, assessments, and models. After synthesizing various approaches used to develop the construct in K-16 settings, we have created the following working definition of CT: The conceptual...

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... (2005) defined mathematical thinking as global across many problems and "… governs one's ways of understanding" (p. 31). Mathematical thinking consists of three parts: beliefs about math, problem solving processes, and justification for solutions. The main commonality between CT and mathematical thinking is problem solving processes (Wing, 2008). Fig. 1 shows the full set of shared concepts of computational and mathematical thinking: problem solving, modeling, data analysis and interpretation, and statistics and ...

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... As an important problem-solving ability in the intelligent era, computational thinking (CT) refers to the basic level of literacy that citizens should possess in the 21st century (Wing, 2006(Wing, , 2010. The origin of CT lies in a thinking process focused on problem solving in real situations (Shute et al., 2017), and this concept includes the five core elements of abstraction, decomposition, algorithmic thinking, generalization and evaluation (Selby & Woollard, 2013). Moreover, the core focus of programming teaching (PT) is also the solution of problems, and this notion includes the four key elements of problem decomposition, the application of algorithms, abstraction and automation (Shute et al., 2017). ...
... The origin of CT lies in a thinking process focused on problem solving in real situations (Shute et al., 2017), and this concept includes the five core elements of abstraction, decomposition, algorithmic thinking, generalization and evaluation (Selby & Woollard, 2013). Moreover, the core focus of programming teaching (PT) is also the solution of problems, and this notion includes the four key elements of problem decomposition, the application of algorithms, abstraction and automation (Shute et al., 2017). Therefore, PT is considered to be the best approach to the cultivation of CT due to its unique potential to cultivate logical thinking and innovative ability (Zhang & Ji, 2018;Scherer 2016). ...
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Computational thinking is considered to be an important competence in the intelligent era, and the incorporation of computational thinking as an integral part of school education beginning in childhood has been proposed. However, the ways in which computational thinking can be taught more effectively the context of in K-12 programming teaching remain unclear. This paper reports the results of a meta-analysis of 28 empirical studies on K-12 programming teaching that were published in international education journals in the 21st century to determine which teaching methods and programming tools are most effective in promoting the computational thinking of K-12 students. The results show that (1) programming teaching can promote the improvement of K-12 students’ computational thinking (ES = 0.72, z = 9.9, P < 0.01), with an overall effect at the upper-middle level (95% CI[0.60,0.83]); (2) scaffolding programming (ES = 1.84, z = 11.9, P < 0.01) and problem-based programming (ES = 1.14, z = 5.57, P < 0.01) are the most effective teaching methods and can significantly promote the development of K-12 students’ computational thinking (chi² = 40.58, P < 0.01); (3) since differences in the effect of programming tools between groups are not significant (Chi² = 6.47, P = 0.09), it is impossible to determine which programming tools are most effective; and (4) intervention duration (ES = 0.72, z = 11.9, P < 0.05, 95% CI[0.60, 0.83]) and learning scaffold (ES = 0.83, z = 6.27, P < 0.05, 95% CI[0.57, 1.09]) are both key moderating variables that affect the improvement of computational thinking. Based on these results, suggestions are provided for future research and practice.
... Computational thinking is defined as a process for solving problems efficiently and effectively, including design thinking and engineering (i.e., efficient solution design), and systems thinking (i.e., system understanding and modeling) (Shute et al., 2017). CT and mathematical thinking have common elements (e.g., problem-solving, mathematical modeling, and statistics and probability) (Sneider et al., 2014). ...
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Over the past several years, creativity has been recognized as an important skill for success in STEM education, engineering design and computational thinking. There is limited research on how to apply Conceive, Design, Implement, Operate (CDIO) engineering design in STEM courses and how it affects students' creativity. This study strategically integrated computational thinking (CT), STEM, and CDIO engineering design, based on Action-Process-Object-Schema (APOS) theory, into a course. This study has adopted a framework to design creative learning materials and examined how this framework impacts students' creativity. This study used a pretest–posttest nonequivalent-groups design. The study investigated a STEM course which had 40 participants (n = 40, male = 11, female = 29) for 12 weeks. 6 weeks were spent using traditional teaching and 6 weeks integrating a CDIO framework into computational thinking. All the students in the course were college students with teacher college backgrounds. To examine the impact of our course, we used a questionnaire to examine the students’ creativity before and after the STEM course was applied. Our results show that our course had a significantly impact on our students’ creativity, particularly in the case of the male students. This finding is consistent with the findings of other research studies. This study also offers some suggestions for teachers who wish to improve their learning materials.
... The research started by gathering literature relevant to the research topic from chosen multifaceted databases following the method of systematic review (Shute et al., 2017. As data collection proceeded, conference and journal literature was mainly reviewed. ...
Thesis
The rate at which machine learning (ML) and artificial intelligence (AI) are being applied in everyday activities arose the interest in teaching the coming generation ML at an early age. This will prepare the pupils and get them acquainted with the technology they use every day. Also, this will make ML education in K-12 a developing research area of study. However, several attempts have been in progress to research teaching ML to K-12 students for a decade. This study aimed to identify the present status of K-12 machine learning education through a systematic literature review between 2011 and 2021. Data were collected from four reputable databases. Meanwhile, this study discovered that the first paper was published in Israel at a conference in 2012. Most of the articles published in this field were produced between 2020 and 2021, and more conference proceeding papers were published than journals in this field. However, 133 authors contributed to 50 articles studied, while the USA tops the list of authors’ countries. Likewise, the high school received more attention than the other K-12 grade level. Researchers used workshop study settings more than other study settings, while plugged activities were majorly adopted. Meanwhile, most researchers adopted the qualitative research method, while most researchers also used interviews as a data collection method. This review will instigate continuous discussion and attract attention to ML K-12 education among educational researchers, practitioners, and the government.
... This study is theoretically based on the concepts of situated learning theory and social-cognitive theory. CT is seen in problem solving processes (Román-González et al., 2017;Shute et al., 2017;Yadav et al., 2016). The literature on cognitive psychology and socio-cultural learning accepts that knowledge is constructed through problem-solving (Billett, 1996). ...
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In recent years, computational thinking (CT) initiatives have been increasing in both research and practice. Although the importance of students' resilience and computational identity in the CT development process is recognized, more research is needed on their role on students' CT skills. Therefore, little is known about whether differences in students' CT performance and CT self-efficacy (CTSE) are related to computational identity and academic resilience for programming (ARP). This study aims to understand how secondary school students' latent profiles are distributed according to computational identity and ARP using a person-centered approach. Afterward, the current research examines how these profiles differ according to CT test performance and CTSE scores. The participants of the study consisted of 601 secondary school students. Latent profile analysis revealed four profiles based on computational identity and resilience: (a) low, (b) low to moderate, (c) moderate to high, (d) high. The effect of profile membership and gender on CTSE and CT test performance was determined by two-way ANOVA analysis. CTSE score increases in profiles where the level of identity and resilience increases. The impact of profiles and gender interaction on CTSE is significant. Low profile male students have significantly lower CTSE scores than other groups. While profiles affected CT performance significantly, no difference is found by gender.
... Despite this nascent but educationally meaningful viewpoint, the "problemsolving" dimension in CT has been generally more prevalent, portraying how CT can provide skills to understand and solve concrete real-life problems with computational methods. This dimension has been characterized with such core concepts and practices as abstraction, decomposition, algorithms, evaluation, and generalization that students are expected to learn (Barr and Stephenson, 2011;Grover and Pea, 2013;Shute et al., 2017). Establishing on this dimension, the theoretical definition of CT in ICILS 2018 (Fraillon et al., 2019b, pp. ...
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Introduction Despite the growing importance of teaching and learning computational thinking (CT) through programming in schools, research has shown major individual differences in teachers’ instruction emphasis and students’ skills in these topics. Objective This study aims to shed further light on the role that teachers’ and students’ programming motivation plays in CT. Methods The topic is approached from the viewpoint of the self-determination theory, which can help to understand teachers’ instruction and students’ learning. Our sample consisted of Finnish Grade 8 teachers ( N = 1,853) and students ( N = 2,546) who participated in the International Computer and Information Literacy Study (ICILS) in 2018. Focusing on teachers’ CT instruction emphasis, students’ CT test scores, and the Intrinsic Motivation Inventory, we investigate (1) distributions of teachers’ and students’ responses to intrinsic and extrinsic programming motivation questions, (2) associations between teachers’ and students’ programming motivation and their background factors, and (3) associations between programming motivation and teachers’ CT instruction emphasis and students’ CT test scores. The data was analyzed by examining descriptive statistics, computing mean differences and correlation coefficients and by performing (multiple) linear regression models. Results The results showed that teachers had high extrinsic programming motivation, but the extent of their intrinsic programming motivation varied widely based on their prior programming teaching experience, subject taught, and gender. Students, in turn, reported both high intrinsic and extrinsic motivation toward programming, but boys were generally more motivated for programming than girls. High programming motivation was moderately related to teachers’ higher CT instruction emphasis and students’ higher CT test scores. Conclusion The findings give a strong incentive to pay attention to increasing especially girls’ programming motivation and providing teachers with positive CT experiences relevant to their subject and with a particular objective to increase intrinsic motivation especially among teachers who lack prior programming teaching experience and interest in the topic.
... These include CT conceptual models by Computing at School (Csizmadia et al., 2015), International Society for Technology in Education Barr & Stephenson, 2011), and others (e.g. Brennan & Resnick, 2012;Korkmaz et al., 2017;Selby & Woollard, 2013;Shute et al., 2017;Weintrop et al., 2016). ...
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Most studies suggest that students develop computational thinking (CT) through learning programming. However, when the target of CT is decoupled from programming, emerging evidence challenges the assertion of CT transferability from programming. In this study, CT was operationalized in everyday problem-solving contexts in a learning experiment ( n = 59) that investigated whether learning programming enhances students’ CT skills. Specifically, this study examined the influence of a novel, systematic and micro instructional strategy that is grounded in abstraction and comprised of four independent but related processes – discover, extract, create, and assemble (DECA) towards simplification of problem-solving. Subsidiary questions explored the effects of students’ age, gender, computer proficiency, and prior programming experience on the development of CT. No significant difference was found between the CT skill and programming knowledge of the groups at the posttest. However, within-group paired t-tests showed that the experimental group that integrated DECA had significant improvement in CT but not in the control group across the pretest-posttest axis. Implications of the inconclusive finding about the transfer of programming skills to CT are emphasized and the arguments for disentangling CT from programming are highlighted.
... In the review made by Shute et al. (2017), entitled "demystifying computational thinking, " he addresses the concept as the set of skills of decomposition, abstraction, algorithm design, debugging, iteration, and generalization, understood as necessary skills for problem-solving. Regarding computational thinking and programming, they are analyzed at the same level; however, it does not consider that computational programming through teaching allows acquiring computational thinking skills, since it is necessary to abstract and decompose a problem before coding a program, and also, depending on the complexity, it will be necessary to abstract at several levels, and once programmed, debugging and checking that what is done does what it should solve will be necessary, thus incorporating computational thinking step by step. ...
... It is highlighted in Shute Shute et al. (2017), a research developed, in which a scale was created to measure computational thinking (Román- González et al., 2017;in Shute et al., 2017), which includes a 28-item scale and takes about 45 min to complete. It focuses on programming concepts, such as directions and sequences, loops, conditionals, and simple functions. ...
... It is highlighted in Shute Shute et al. (2017), a research developed, in which a scale was created to measure computational thinking (Román- González et al., 2017;in Shute et al., 2017), which includes a 28-item scale and takes about 45 min to complete. It focuses on programming concepts, such as directions and sequences, loops, conditionals, and simple functions. ...
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Recent studies suggest that computational thinking, composed of the skills of abstraction, decomposition, algorithmization, debugging, and problem-solving, is the fundamental skill for scientific, technological, and economic development for the twenty-first century. However, this diagnosis that is unveiled in rich countries remains nebulous for poor countries. The problem is that education in computational thinking is fundamental for countries to insert themselves in the international arena in an advantageous way and thus achieve the welfare goals for the population of each country. The objective of this research was to make a bibliographic review that shows the state of the art in the teaching of computer programming and computational thinking in the 5 continents. In the review, the advances in the countries of Europe, North America, Oceania, and Asia were observed, whereas in Latin America and Africa, the advances are still basic in some countries and non-existent in others. This review is based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The main search terms were “Computational thinking” and “Teaching computer programming.” The search was performed in the ACM, Conference on Computational Thinking Education (Hong-Kong), Google Scholar, WOS, and SCOPUS databases, from October until December 2020, whose publication year was from 2016 onward. One of the main results found is that the teaching of computational thinking in England was implemented in schools in 2014; in Germany, it has been implemented since 2016 at a transversal level in universities; in South Korea, China, and Taiwan, it has been implemented since 2016. However, in Latin America and Africa governments, the subject is still not considered.
... Derivado de lo anterior, a través de diversos estudios de investigación González-González, 2019;Lyon & J. Magana, 2020;Sáez-López et al., 2016;Shute et al., 2017;Weintrop et al., 2016) se identifican amplitud de conceptos en torno al PC, por lo tanto, no existe un consenso sobre su fundamento conceptual y su forma de aplicación en diferentes contextos educativos. En la revisión documental es reiterativo encontrar que los artículos hacen referencia a la diversidad de abordajes conceptuales que se dan alrededor del PC y los cuales están en gran medida enfocados hacia la construcción operacional del concepto, con el fin de tener bases teóricas sólidas para integrarlo en los currículos educativos. ...
... (González-González, 2019) Aproximación hacia la resolución de problemas mediante el uso de estrategias de descomposición, diseño de algoritmos, abstracción y razonamiento lógico. (Shute et al., 2017) Base conceptual requerida para resolver problemas a través de algoritmos con asistencia o no de computadores, cuyas soluciones sean reutilizables en diferentes contextos. (Yadav et al., 2017) Metodología para enseñar programación, siendo la programación el siguiente paso en el marco del pensamiento computacional. ...
... Sin embargo, el PC se desarrolla con el uso o no del computador; en este sentido, se encuentra la investigación planteada desde (Shute et al., 2017) en el cual el PC es un conjunto de prácticas enlazadas con diferentes formas de abordar problemas. Como complemento a esa visión en se plantea el PC como un conjunto de herramientas mentales que permiten a las personas reducir problemas en subtareas, representar problemas de manera adecuada e interpretar datos. ...
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Se aborda el concepto de desarrollo, así como los marcos en que las políticas son aplicadas en los territorios indígenas de México, particularmente en el norte de Chiapas, desde la segunda mitad del siglo XX. Se realizó un análisis teórico sobre este paradigma y se mencionan los fracasos y conjeturas de dicha propuesta, desde el análisis de su evolución histórica reciente, sus nociones modernas y su devenir en desarrollo alternativos y alternativas al desarrollo. A su vez, se explora la noción de lo que representa lo socioambiental y el lugar que ocupa como precepto en la configuración del Territorio. Se estudió la base conceptual del desarrollo y sus ampliaciones considerando que sus impactos han devenido no solo en percepciones aisladas o endémicas de escala local, sino que configuran políticas públicas globales, que irrumpen y regulan los territorios, provocando, en algunos casos, deterioro de las condiciones de vida y crisis socioambientales. Paralelamente se hace un recuento de las diversas acepciones y dimensiones del territorio y su complejidad natural, económica y cultural. Se separa la idea del territorio como posición filosófica y se plantea como espacio ubicable que se vive, determina y regula por las relaciones de poder y las apropiaciones simbólicas y naturales que contiene. Finalmente, se analizó la historia del territorio Zoque, particularmente alrededor del volcán El Chichón poniendo en perspectiva las relaciones hombre-naturaleza y las afectaciones y fracturamientos sociales que han dotado al territorio de una alta vulnerabilidad ante la entrada de políticas públicas irreconciliables con la sustentabilidad local. Palabras Claves: desarrollo; socioambiental; sostenible; territorio; zoques.
... During the exploration phase in robotics STEM, as depicted in Table 9, the participants tended to detach and reassemble the robotics components based on similarities in their shape, size or colour. This ability to uncouple and reattach the robotic components into a particular function represented the decomposition principle (Shute et al., 2017). Participants think freely about activities and start building new understanding. ...
Article
The delivery of science, technology, engineering and mathematics (STEM) learning to improve an individual’s competence and future career interests has become a critical scientific undertaking for teachers and researchers alike. A plethora of research has proposed various hands-on robotics activities built on constructivist theories, thereby facilitating the development of knowledge based on reality for scientific and non-scientific stakeholders. Robotics may become an essential focus point within technology provision, which is an essential underlying characteristic for the seminal development of computational thinking (CT). However, despite the potential benefit of CT in developing an individual’s problem-solving skills, strategies for improving this ability through hands-on robotics activities largely remain underexplored. This paper highlights the constructs drawn from hands-on robotics activities in a STEM workshop designed for pre-service teacher students. The qualitative research design involved eight participants to investigate the responses of pre-service teachers to a hands-on robotics activity intended to provide STEM material. The research findings emphasise the correlations between the CT principles and STEM learning phases and underscore the roles played by educational robotics to enhance previous literature on learning experience.
... Computational thinking (CT) has been considered a vital skill for 21st-century citizens (Shute et al., 2017) because it is by nature an analytic process that allows students to represent problems and use computing devices to solve problems (Wing, 2006(Wing, , 2011 pioneered CT as a process that leverages computer science concepts to understand, analyze and solve problems beyond computation and programming. Grover & Pea (2018) defined CT as the "thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer -human or machine -can effectively carry out" (p. ...
... We identified three patterns of model development: incremental, filter-based, and radical feature creation. Based on these patterns, we can infer strategies that students utilize in creating, evaluating, and comparing features and identify opportunities that enable students to develop understanding of model learning patterns from data (Shute et al., 2017;Weintrop et al., 2016). For example, in radical feature creation, students might notice that the model accuracy sometimes decreases and sometimes increases (e.g., Liam's case). ...
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As artificial intelligence (AI) technologies are increasingly pervasive in our daily lives, the need for students to understand the working mechanisms of AI technologies has become more urgent. Data modeling is an activity that has been proposed to engage students in reasoning about the working mechanism of AI technologies. While Computational thinking (CT) has been conceptualized as critical processes that students engage in during data modeling, much remains unexplored regarding how students created features from unstructured data to develop machine learning models. In this study, we examined high school students’ patterns of iterative model development and themes of CT processes in iterative model development. Twenty-eight students from a journalism class engaged in refining machine learning models iteratively for classifying negative and positive reviews of ice cream stores. This study draws on a theoretical framework of CT processes to examine students’ model development processes. The results showed that students (1) demonstrated three patterns of iterative model development, including incremental, filter-based, and radical feature creation; (2) engaged in complex reasoning about language use in diverse contexts in trial and error, (3) leveraged multiple data representations when applying mathematical and computational techniques. The results provide implications for designing accessible AI learning experiences for students to understand the role and responsibility of modelers in creating AI technologies and studying AI learning experiences from the angle of CT processes.