Статті в журналах: "Computational thinking education"

1

Shute, Valerie J., Chen Sun, and Jodi Asbell-Clarke. "Demystifying computational thinking." Educational Research Review 22 (November 2017): 142–58. http://dx.doi.org/10.1016/j.edurev.2017.09.003.

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Yadav, Aman, Chris Stephenson, and Hai Hong. "Computational thinking for teacher education." Communications of the ACM 60, no. 4 (March 24, 2017): 55–62. http://dx.doi.org/10.1145/2994591.

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3

Park, Jungho. "Effects of Storytelling Based Software Education on Computational Thinking." Journal of The Korean Association of Information Education 19, no. 1 (March 30, 2015): 57–68. http://dx.doi.org/10.14352/jkaie.2015.19.1.57.

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4

Kafai, Yasmin B., and Chris Proctor. "A Revaluation of Computational Thinking in K–12 Education: Moving Toward Computational Literacies." Educational Researcher 51, no. 2 (November 5, 2021): 146–51. http://dx.doi.org/10.3102/0013189x211057904.

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Over the past decade, initiatives around the world have introduced computing into K–12 education under the umbrella of computational thinking. While initial implementations focused on skills and knowledge for college and career readiness, more recent framings include situated computational thinking (identity, participation, creative expression) and critical computational thinking (political and ethical impacts of computing, justice). This expansion reflects a revaluation of what it means for learners to be computationally-literate in the 21st century. We review the current landscape of K–12 computing education, discuss interactions between different framings of computational thinking, and consider how an encompassing framework of computational literacies clarifies the importance of computing for broader K–12 educational priorities as well as key unresolved issues.

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5

Kuo, Wei-Chen, and Ting-Chia Hsu. "Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game." Asia-Pacific Education Researcher 29, no. 1 (September 20, 2019): 67–83. http://dx.doi.org/10.1007/s40299-019-00479-9.

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6

Park, Young-Shin, and James Green. "Bringing Computational Thinking into Science Education." Journal of the Korean earth science society 40, no. 4 (August 30, 2019): 340–52. http://dx.doi.org/10.5467/jkess.2019.40.4.340.

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7

Rubinstein, Amir, and Benny Chor. "Computational Thinking in Life Science Education." PLoS Computational Biology 10, no. 11 (November 20, 2014): e1003897. http://dx.doi.org/10.1371/journal.pcbi.1003897.

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8

Swaid, Samar I. "Bringing Computational Thinking to STEM Education." Procedia Manufacturing 3 (2015): 3657–62. http://dx.doi.org/10.1016/j.promfg.2015.07.761.

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9

Angeli, Charoula, and Michail Giannakos. "Computational thinking education: Issues and challenges." Computers in Human Behavior 105 (April 2020): 106185. http://dx.doi.org/10.1016/j.chb.2019.106185.

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Li, Yeping, Alan H. Schoenfeld, Andrea A. diSessa, Arthur C. Graesser, Lisa C. Benson, Lyn D. English, and Richard A. Duschl. "On Computational Thinking and STEM Education." Journal for STEM Education Research 3, no. 2 (July 2020): 147–66. http://dx.doi.org/10.1007/s41979-020-00044-w.

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11

Villalba-Condori, Klinge Orlando, and Luis Magdiel Oliva-Córdova. "Teacher Training to Develop Computational Thinking at the Primary Education Level." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (October 31, 2019): 91–98. http://dx.doi.org/10.5373/jardcs/v11i10/20193010.

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12

Son, Young-Su, and Kwang-Jae Lee. "Computational Thinking Teaching Model Design for Activating IT Convergence Education." Journal of the Korea institute of electronic communication sciences 11, no. 5 (May 31, 2016): 511–22. http://dx.doi.org/10.13067/jkiecs.2016.11.5.511.

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13

Grover, Shuchi, and Roy Pea. "Computational Thinking in K–12." Educational Researcher 42, no. 1 (January 2013): 38–43. http://dx.doi.org/10.3102/0013189x12463051.

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Jeannette Wing’s influential article on computational thinking 6 years ago argued for adding this new competency to every child’s analytical ability as a vital ingredient of science, technology, engineering, and mathematics (STEM) learning. What is computational thinking? Why did this article resonate with so many and serve as a rallying cry for educators, education researchers, and policy makers? How have they interpreted Wing’s definition, and what advances have been made since Wing’s article was published? This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.

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14

Park, Sung Hee. "Study of SW Education in University to enhance Computational Thinking." Journal of Digital Convergence 14, no. 4 (April 28, 2016): 1–10. http://dx.doi.org/10.14400/jdc.2016.14.4.1.

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Galoyan, Tamara, Amanda Barany, Jonan Phillip Donaldson, Nahla Ward, and Penny Hammrich. "Connecting Science, Design Thinking, and Computational Thinking through Sports." International Journal of Instruction 15, no. 1 (January 1, 2022): 601–18. http://dx.doi.org/10.29333/iji.2022.15134a.

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16

Jeong, Hyeon-Seon, and Kyeong Hur. "Incorporating Computational Thinking into Media Literacy Education for Critical Thinking." Journal of Korean Culture 48 (February 29, 2020): 105–28. http://dx.doi.org/10.35821/jkc.2020.02.48.105.

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17

Barr, Valerie. "Disciplinary thinking, computational doing." ACM Inroads 7, no. 2 (May 16, 2016): 48–57. http://dx.doi.org/10.1145/2891414.

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18

Heintz, Fredrik, and Linda Mannila. "Computational thinking for all." ACM Inroads 9, no. 2 (April 27, 2018): 65–71. http://dx.doi.org/10.1145/3210553.

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19

Kale, Ugur, Mete Akcaoglu, Theresa Cullen, Debbie Goh, Leah Devine, Nathan Calvert, and Kara Grise. "Computational What? Relating Computational Thinking to Teaching." TechTrends 62, no. 6 (April 18, 2018): 574–84. http://dx.doi.org/10.1007/s11528-018-0290-9.

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20

Sidek, Salman Firdaus, Maizatul Hayati Mohamad Yatim, and Che Soh Said. "Characterizing Computational Thinking for Tertiary Education Learning." Journal of Contemporary Issues and Thought 10 (July 30, 2020): 58–69. http://dx.doi.org/10.37134/jcit.vol10.sp.6.2020.

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21

WEIGEND, Michael, Jiří VANÍČEK, Zsuzsa PLUHÁR, and Igor PESEK. "Computational Thinking Education through Creative Unplugged Activities." Olympiads in Informatics 13 (July 13, 2019): 171–92. http://dx.doi.org/10.15388/ioi.2019.11.

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Unplugged activities are well known in the computer science education. Creativity and computational thinking have been extensively researched and classified in last decade. In this paper we are focusing on creative unplugged activities and their potential in the classroom. We propose a model consisting of four types of creative unplugged activities that are used in CS education and present the results of an international online survey in which 360 educators participated in 2018. The survey found out how far the model is supported by educators, the extent to which creative activities are used in the classroom, what intentions are being pursued and what educational potential is seen in the four types of activities. Based on results of the survey we present ideas and methods on how to include and integrate creative unplugged activities into CS education and some possibilities on how to change such tasks to be more creative.

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22

Gadanidis, George. "Artificial intelligence, computational thinking, and mathematics education." International Journal of Information and Learning Technology 34, no. 2 (March 6, 2017): 133–39. http://dx.doi.org/10.1108/ijilt-09-2016-0048.

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Purpose The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances. Design/methodology/approach The theoretical framework of this paper adopts a sociocultural perspective where knowledge is constructed in interactions with others (Vygotsky, 1978). Others also refers to the multiplicity of technologies that surround us, including both the digital artefacts of our new media world, and the human methods and specialized processes acting in the world. Technology is not simply a tool for human intention. It is an actor in the cognitive ecology of immersive humans-with-technology environments (Levy, 1993, 1998) that supports but also disrupts and reorganizes human thinking (Borba and Villarreal, 2005). Findings There is fruitful overlap between AI, CT and ME that is of value to consider in mathematics education. Originality/value Seeing ME through the lenses of other disciplines and recognizing that there is a significant overlap of key elements reinforces the importance of agency, modeling and abstraction in ME and provides new contexts and tools for incorporating them in classroom practice.

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23

Harangus, Katalin, and Zoltán Kátai. "Computational Thinking in Secondary and Higher Education." Procedia Manufacturing 46 (2020): 615–22. http://dx.doi.org/10.1016/j.promfg.2020.03.088.

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Kang, Younah, and Keeheon Lee. "Designing technology entrepreneurship education using computational thinking." Education and Information Technologies 25, no. 6 (May 25, 2020): 5357–77. http://dx.doi.org/10.1007/s10639-020-10231-2.

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25

Kafai, Yasmin B. "From computational thinking to computational participation in K--12 education." Communications of the ACM 59, no. 8 (July 22, 2016): 26–27. http://dx.doi.org/10.1145/2955114.

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26

Moon, Gyo Sik. "On the Direction of the Application of the Concepts of Computational Thinking for Elementary Education." Journal of the Korea Contents Association 13, no. 6 (June 28, 2013): 518–26. http://dx.doi.org/10.5392/jkca.2013.13.06.518.

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27

Lodi, Michael, and Simone Martini. "Computational Thinking, Between Papert and Wing." Science & Education 30, no. 4 (April 28, 2021): 883–908. http://dx.doi.org/10.1007/s11191-021-00202-5.

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AbstractThe pervasiveness of Computer Science (CS) in today’s digital society and the extensive use of computational methods in other sciences call for its introduction in the school curriculum. Hence, Computer Science Education is becoming more and more relevant. In CS K-12 education, computational thinking (CT) is one of the abused buzzwords: different stakeholders (media, educators, politicians) give it different meanings, some more oriented to CS, others more linked to its interdisciplinary value. The expression was introduced by two leading researchers, Jeannette Wing (in 2006) and Seymour Papert (much early, in 1980), each of them stressing different aspects of a common theme. This paper will use a historical approach to review, discuss, and put in context these first two educational and epistemological approaches to CT. We will relate them to today’s context and evaluate what aspects are still relevant for CS K-12 education. Of the two, particular interest is devoted to “Papert’s CT,” which is the lesser-known and the lesser-studied. We will conclude that “Wing’s CT” and “Papert’s CT,” when correctly understood, are both relevant to today’s computer science education. From Wing, we should retain computer science’s centrality, CT being the (scientific and cultural) substratum of the technical competencies. Under this interpretation, CT is a lens and a set of categories for understanding the algorithmic fabric of today’s world. From Papert, we should retain the constructionist idea that only a social and affective involvement of students into the technical content will make programming an interdisciplinary tool for learning (also) other disciplines. We will also discuss the often quoted (and often unverified) claim that CT automatically “transfers” to other broad 21st century skills. Our analysis will be relevant for educators and scholars to recognize and avoid misconceptions and build on the two core roots of CT.

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28

Christensen, Dana, and Doug Lombardi. "Understanding Biological Evolution Through Computational Thinking." Science & Education 29, no. 4 (July 22, 2020): 1035–77. http://dx.doi.org/10.1007/s11191-020-00141-7.

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Lee, Irene, and Joyce Malyn-Smith. "Computational Thinking Integration Patterns Along the Framework Defining Computational Thinking from a Disciplinary Perspective." Journal of Science Education and Technology 29, no. 1 (November 22, 2019): 9–18. http://dx.doi.org/10.1007/s10956-019-09802-x.

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Kite, Vance, Soonhye Park, and Eric Wiebe. "The Code-Centric Nature of Computational Thinking Education: A Review of Trends and Issues in Computational Thinking Education Research." SAGE Open 11, no. 2 (April 2021): 215824402110164. http://dx.doi.org/10.1177/21582440211016418.

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Computational thinking (CT) is being recognized as a critical component of student success in the digital era. Many contend that integrating CT into core curricula is the surest method for providing all students with access to CT. However, the CT community lacks an agreed-upon conceptualization of CT that would facilitate this integration, and little effort has been made to critically analyze and synthesize research on CT/content integration (CTCI). Conflicting CT conceptualizations and little understanding of evidence-based strategies for CTCI could result in significant barriers to increasing students’ access to CT. To address these concerns, we analyzed 80 studies on CT education, focusing on both the CT conceptualizations guiding current CT education research and evidence-based strategies for CTCI. Our review highlights the code-centric nature of CT education and reveals significant gaps in our understanding of CTCI and CT professional development for teachers. Based on these findings, we propose an approach to operationalizing CT that promotes students’ participation in CT, present promising methods for infusing content with CT, and discuss future directions for CT education research.

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31

Saari, Erni Marlina, and Gail Hopkins. "Computational thinking – Essential and pervasive toolset." Asian Journal Of Assessment In Teaching And Learning 10, no. 1 (April 2, 2020): 23–31. http://dx.doi.org/10.37134/ajatel.vol10.1.3.2020.

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Education 21st century is all about enfolding digital technology. The theme “Higher Education 4.0: Knowledge, Industry and Humanity”, mandated from Malaysia’s former Higher Education Minister Datuk Idris Jusoh. The minister identified that universities have to be trained to adapt and change the curriculum so that graduates are capable to fill in jobs which are yet to arise. This Fourth Industrial Revolution 4.0 as part of the call to revamp the Malaysia higher education system. There are nine Malaysia future-proof skills that has been listed under Ministry of Higher Education module on Framing Malaysian Higher Education 4.0 – future-proof talents. There are creativity and innovation; Holistic Entrepreneurial and Balanced; Resilience; leadership; Compassion and mindfulness; values and ethics; flexibility and adaptability; critical thinking and problem solving and finally communication and language proficiency. The above mentioned points are future-proof skills sets for Malaysian graduates. There are three additional future-proof attributes which are lifelong learners, multiple intelligence and competencies and computational thinking. This qualitative study explored the significant area in the recent digital technology and development hence, it will be one of the crucial knowledge that should be acquired by everyone and anyone not only in Malaysia but in the whole wide world. Technology is moving rapidly and educators have to keep up with this fast pace. This issue attentively allied with the terminology pioneered by Jeanette Wing that is called computational thinking (CT). The interviewed is carried out among eleven Malaysian pre-service teachers and existing teachers under PGCE (Post Graduate Certificate in Education) program in the United Kingdom, to study on their attitudes towards the idea of learning programming. Through the interviews, the researcher was able to record and interpret trainee teachers’ perceptions of the motivation to learn programming. The data were then analysed and categorised before the final codes were determined to get the final output using thematic code analysis technique. The findings show that learning programming is possible for those who had no computing background thus answered one of the aim of this study that CT skills could be adopted in any fields and more creators or designers will be established compared to the passive users. There was a rational consideration of the value of learning programming to their professional skills and the studies have asked whether understanding such will raise the participants’ engagement with learning a programming language and thus assist them to acquire CT skill.

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Lee, Kyunghee, and Jungwon Cho. "Computational Thinking Evaluation Tool Development for Early Childhood Software Education." JOIV : International Journal on Informatics Visualization 5, no. 3 (September 27, 2021): 313. http://dx.doi.org/10.30630/joiv.5.3.672.

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The early childhood software education is being actively conducted, but research on evaluation of computational thinking is in its infancy. The purpose of early childhood software education is to cultivate the computational thinking through activities centered on solving problems in everyday life. Evaluation in software education is very important in that it not only measures computational thinking simply but also improves computational thinking through evaluation. As such, guidelines for evaluating computational thinking that can be used in early childhood software education are needed, but they are very lacking. Therefore, in this study, the researcher developed an evaluation tool that can meet the ultimate purpose of software education, cultivating computational thinking. The developed evaluation tools are a software education effectiveness test tool and a computational thinking test tool. They were developed to the level of development and interaction of the early childhood. The developed evaluation tool has been validated by software experts, early childhood education experts, and early childhood teachers. As a result of the second step validity verification, all content validity was confirmed. Through this, it was confirmed that the evaluation tool developed in this study can be used as a tool for evaluating computational thinking. This study provides implications for evaluation of computational thinking for early childhood software education. In addition, it is meaningful that it has been suggested to be effectively used for proper evaluation in early childhood software education.

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33

Weintrop, David, Shandra Morehouse, and Mega Subramaniam. "Assessing computational thinking in libraries." Computer Science Education 31, no. 2 (January 18, 2021): 290–311. http://dx.doi.org/10.1080/08993408.2021.1874229.

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34

Kamalova, G., and А. Akzholova. "THE CONCEPT AND SKILLS OF COMPUTATIONAL THINKING IN DIGITAL EDUCATION." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (December 15, 2021): 168–73. http://dx.doi.org/10.51889/2021-4.1728-7901.23.

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Nowadays, the role of digital technologies in human life and in the field of education is increasing. The concept of computational thinking is becoming a key concept in the digital age. The use of such thinking in the educational process allows students to solve problems and tasks, creatively and systematically perceive various information. The article deals with the concept of "computational thinking", elements of computational thinking (abstraction, decomposition, generalization, algorithms), approaches to evaluating computational thinking. The article is based mainly on the review and analysis of foreign scientific and pedagogical works, since the practical definition of the concept of computational thinking and its components is not widely reflected in the works of Kazakhstani researchers.

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35

Shin, Seungki. "Designing the Instructional Framework and Cognitive Learning Environment for Artificial Intelligence Education through Computational Thinking." Journal of The Korean Association of Information Education 23, no. 6 (December 31, 2019): 639–53. http://dx.doi.org/10.14352/jkaie.2019.23.6.639.

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Et. al., Woojong Moon,. "Effect Of Execution Time Analysis Epl Program For Computational Thinking Of Elementary School Students ^." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (April 11, 2021): 336–45. http://dx.doi.org/10.17762/turcomat.v12i4.511.

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Software education has emerged as a hot topic around the world, with the goal to raise interests on computational thinking. However, assessments on the computational thinking have not been actively conducted thus far. According to a study by Lee (2019), which analyzed 138 papers on computational thinking published in Korean journals from 2015 to 2018, software education has been introduced and studies on computational thinking are being conducted, but studies on teaching methods that2 improve computational thinking are needed. In this study, we developed and applied a primary educational programming language(EPL) program focused on execution time analysis aimed at improving computational thinking. By using the “Bebras Challenge” as an assessment tool and SPSS as a statistical tool, educational effects were analyzed through the results of pre- and post-computational thinking assessments. The analysis outcomes showed that the EPL education focused on execution time analysis was effective in improving the computational thinking of elementary school students. Putting execution time analysis EPL into primary software education as an educational topic will be effective in improving computational thinking.

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Stoilescu, Dorian. "Exploring the Introduction of Computational Thinking in STEM Education in Australian Schools." Southeast Asian Mathematics Education Journal 9, no. 1 (December 31, 2019): 17–24. http://dx.doi.org/10.46517/seamej.v9i1.70.

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This paper discusses theoretical and curricular aspects of computational thinking in curriculum and detects recent perspectives and challenges noticed in introducing computational thinking in STEM in Australian Schools. It presents the way computational thinking is defined and understood in curriculum documents and a set of relatively new implementations that were designed nationally and in the state of New South Wales. This paper uses qualitative research methods such as content analysis and text analysis. The research analyses some recent trends in introducing computational thinking and explores these reforms that are described in the official documents. It was noticed that although the importance of computational thinking was highly emphasized, the documents cannot describe a consistent implementation of this set of educational policies, as at this time implementing computational thinking is largely underperforming. It is recommended a more systemic way of designing policies and curriculum content for the integration of computational thinking in Australian schools is needed. Future research needs to explore reasons for delaying these reforms of introducing computational thinking.

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Park, Gwangryeol. "Development of Learning Materials for Computational Thinking Education." Journal of Korean Practical Arts Education 26, no. 1 (February 29, 2020): 33–50. http://dx.doi.org/10.29113/skpaer.2020.26.1.003.

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Subramaniam, Shivaraj, Siti Mistima Maat, and Muhammad Sofwan Mahmud. "Computational thinking in mathematics education: A systematic review." Cypriot Journal of Educational Sciences 17, no. 6 (June 30, 2022): 2029–44. http://dx.doi.org/10.18844/cjes.v17i6.7494.

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As a research area, computational thinking (CT) has gotten increased attention in mathematics education in the last decade. A study identifying patterns in CT research would be essential in understanding the technique for developing CT in mathematics and guiding future research attempts. As a result, the goal of this systematic literature review is to look at the learning methods promoting CT in mathematics lessons. The Preferred Reporting Items for Systematic Review and Meta-Analyses standards were utilised to guarantee that this study was done systematically. The result shows that even though there are various types of learning tools that are most commonly used, the coding programming tool and robotic activities tool are the most user-friendly methods for encouraging CT in mathematics education. This literature review is intended to provide educators with a better understanding of learning tools in order to enhance CT, which may help transform education into something more creative and meaningful. Keywords: Computational thinking; education; learning tools; mathematics; systematic review

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BELL, Judith, and Tim BELL. "Integrating Computational Thinking with a Music Education Context." Informatics in Education 17, no. 2 (October 13, 2018): 151–66. http://dx.doi.org/10.15388/infedu.2018.09.

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41

Yadav, Aman, Chris Mayfield, Ninger Zhou, Susanne Hambrusch, and John T. Korb. "Computational Thinking in Elementary and Secondary Teacher Education." ACM Transactions on Computing Education 14, no. 1 (March 2014): 1–16. http://dx.doi.org/10.1145/2576872.

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42

Spangsberg, Thomas Hvid, and Martin Brynskov. "The Nature of Computational Thinking in Computing Education." International Journal of Information and Education Technology 8, no. 10 (2018): 742–47. http://dx.doi.org/10.18178/ijiet.2018.8.10.1132.

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43

So, Hyo-Jeong, Morris Siu-Yung Jong, and Chen-Chung Liu. "Computational Thinking Education in the Asian Pacific Region." Asia-Pacific Education Researcher 29, no. 1 (December 18, 2019): 1–8. http://dx.doi.org/10.1007/s40299-019-00494-w.

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Peters‐Burton, Erin. "The power of computational thinking in STEM education." School Science and Mathematics 120, no. 3 (March 2020): 127–28. http://dx.doi.org/10.1111/ssm.12397.

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45

Czerkawski, Betul C., and Eugene W. Lyman. "Exploring Issues About Computational Thinking in Higher Education." TechTrends 59, no. 2 (January 28, 2015): 57–65. http://dx.doi.org/10.1007/s11528-015-0840-3.

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Kong, Siu‐Cheung, and Ming Lai. "Computational identity and programming empowerment of students in computational thinking development." British Journal of Educational Technology 53, no. 3 (December 20, 2021): 668–86. http://dx.doi.org/10.1111/bjet.13175.

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47

Sanford, John F., and Jaideep T. Naidu. "Computational Thinking Concepts for Grade School." Contemporary Issues in Education Research (CIER) 9, no. 1 (January 15, 2016): 23–32. http://dx.doi.org/10.19030/cier.v9i1.9547.

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Анотація:

Early education has classically introduced reading, writing, and mathematics. Recent literature discusses the importance of adding “computational thinking” as a core ability that every child must learn. The goal is to develop students by making them equally comfortable with computational thinking as they are with other core areas of early education. Computational thinking does not come naturally and requires training and guidance. This paper argues for the inclusion of computational thinking in tandem with mathematics. As an example, the paper demonstrates spreadsheet applications that can be utilized concurrently with early mathematical concepts. It demonstrates that at this time, spreadsheets are the best medium for inculcating computational thinking but recognizes that advances in technology may favor other digital approaches in time.

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Liang, Hui, and Hua Li. "Research of Excel VBA Teaching Based on Computational Thinking Ability Training." Applied Mechanics and Materials 373-375 (August 2013): 2200–2204. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.2200.

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Recently, higher education has seen an increasing emphasis on the prominent role of computational thinking in computer fundamental education. Computational thinking is taken for the fundamental skills for everyone, not just computer scientist, to learn and use. How to develop students capacity for computational thinking during the teaching process is one of the basic goals of computer fundamental education. Using Excel VBA course teaching as example, in this paper, we discuss how to understand the essence of computational thinking and how cultivate the computational thinking ability of students.

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Syafril, S., T. Rahayu, and G. Ganefri. "Prospective Science Teachers’ Self-Confidence in Computational Thinking Skills." Jurnal Pendidikan IPA Indonesia 11, no. 1 (March 31, 2022): 119–28. http://dx.doi.org/10.15294/jpii.v11i1.33125.

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This study aims to analyze prospective science teachers’ self-confidence in computational thinking skills on three main points: (i) prospective science teachers’ self-confidence in computational thinking skills, (ii) differences in prospective science teachers’ self-confidence in computational thinking skills as per gender, and (iii) differences in prospective science teachers’ self- confidence in computational thinking skills as per expertise (Biology and Physics). A quantitative cross-sectional survey methodology was used as the research design. A total of 1023 prospective science teachers (biology and physics) were randomly selected as the research sample from the 1959 total population. Data were collected using a self-confidence questionnaire on computational thinking skills. The adaptation results were assessed first by five experts before being tested on 74 prospective science teachers from different universities. The results show that prospective science teachers’ self-confidence in computational thinking skills was generally high (Mean = 78.57). The Mann-Whitney U test found no difference in prospective science teachers’ self-confidence in computational thinking skills as per gender (Mean= 78.05, SD= 9.03 for male, Mean= 78.73, SD= 6.86 for female, with a value of F= 6.028, Z= -0.891, Sig= 0.373 0.05). The Independent Sample t-test also showed no difference in prospective science teachers’ self-confidence in computational thinking skills as per expertise. This study concludes that prospective science teachers have high self-confidence in computational thinking skills as crucial skills in the science teaching profession.

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Connolly, Cornelia, Raquel Hijón-Neira, and Seán Ó. Grádaigh. "Mobile Learning to Support Computational Thinking in Initial Teacher Education." International Journal of Mobile and Blended Learning 13, no. 1 (January 2021): 49–62. http://dx.doi.org/10.4018/ijmbl.2021010104.

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Research on the role of mobile learning in computational thinking is limited, and even more so in its use in initial teacher education. Aligned to this there is a need to consider how to introduce and expose pre-service teachers to computational thinking constructs within the context of the subject area they will teach in their future classrooms. This paper outlines a quasi-experimental study to examine the role of mobile learning in facilitating computational thinking development amongst pre-service teachers in initial teacher education. The study enquires if there are significant differences in grades achieved in computational thinking and programming learning when mobile learning is introduced. Findings showed and reaffirmed the positive influence of the mobile applications on the development of computational thinking amongst the pre-service teachers who participated.

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