DISSECT: Analysis of pedagogical techniques to integrate computational thinking into K-12 curricula

Burgett, T.; Folk, R.; Fulton, James C.; Peel, Amanda; Pontelli, Enrico; Szczepanski, V.

doi:10.1109/fie.2015.7344241

Cited by 15 publications

(21 citation statements)

References 3 publications

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“…programming), but developing interest, critical thinking and CT  From consumer to producer -students begin as users of an artefact by using/studying it, then modify it and potentially create from scratch (use-modify-create)  Worked examples -problem statement and a procedure for solving the problem The DISSECT project is discussed in several sections of this paper (see Section 8 and Section 9.2). In their paper on the project, Burgett et al [59] give an analysis of the pedagogical techniques used to integrate CT. DISSECT pairs graduate "fellows" with teachers in the aims of developing lesson plans that incorporate CT and these modules are designed to show how CT already exists in students' lives. These modules are then varied and made appropriate for the different contexts schools and teachers find themselves in and creativity is encouraged.…”

Section: Secondary Schoolmentioning

confidence: 99%

Computational Thinking in Secondary Education: Where does it fit? A systematic literary review

Lockwood

Mooney

2018

IJCSES

113

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Computational Thinking (CT) has been described as an essential skill which everyone should learn and can therefore include in their skill set. Seymour Papert [1] is credited as concretising Computational Thinking in 1980 but since Wing [2] popularised the term in 2006 and brought it to the international community's attention, more and more research has been conducted on CT in education. The aim of this systematic literary review is to give educators and education researchers an overview of what work has been carried out in the domain, as well as potential gaps and opportunities that still exist.

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Section: Secondary Schoolmentioning

confidence: 99%

Computational Thinking in Secondary Education: Where does it fit? A systematic literary review

Lockwood

Mooney

2018

IJCSES

113

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“…Although CT has ties to computer science, its logic can be applied across disciplines such as science, mathematics, English, and language arts (Barr & Stephenson, ; Guzdial, ; Heintz, Mannila, & Färnqvist, ; Sanford & Naidu, ; Weintrop et al, ; Wing, ). Moreover, the integration of CT and science has the potential to help students learn science content and science practices (Burgett et al, ; Peel et al, n.d.; Peel, Fulton, & Pontelli, ; Wing, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…There is a clear need for empirical studies supporting the integration of CT and science, especially pertaining to science learning outcomes. Within the empirical literature on CT and science integrations, some studies only measured CT learning but not science learning (Burgett et al, ; Peel et al, ). Another study used programming to teach science content, but did not teach CT skills or concepts within their intervention (Sengupta, Kinnebrew, Basu, Biswas, & Clark, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…If students understand the underlying logic of computer science, they may be able to make the shift from technological consumers to technological creators (Swanson, Anton, Bain, Horn, & Wilensky, 2017). With the increase in computing in STEM jobs, CT integration in K-12 classrooms prepares students for STEM careers (Burgett et al, 2015;Grover & Pea, 2013;Wing, 2006). Incorporating CT into K-12 education provides students with sense-making and problem-solving opportunities, which may help foster students' interdisciplinary problem-solving skills (Barr & Stephenson, 2011;Grover & Pea, 2013;Wing, 2006).…”

Section: Computational Thinkingmentioning

confidence: 99%

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Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

2019

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Computational thinking (CT) is a way of making sense of the natural world and problem solving with computer science concepts and skills. Although CT and science integrations have been called for in the literature, empirical investigations of such integrations are lacking. Prior work in natural selection education indicates students struggle to explain natural selection in different contexts and natural selection misconceptions are common. In this mixed methods study, secondary honors biology students learn natural selection through CT by engaging in the design of unplugged algorithmic explanations. Students learned CT principles and practices and applied them to learn and explain the natural selection process. Algorithmic explanations were used to scaffold transfer of natural selection knowledge across contexts through investigation of three organisms and the creation of generalized natural selection algorithms. Students' pre‐ and post‐unit algorithmic explanations of natural selection were analyzed to answer the following research questions: (a) How do students' conceptions of natural selection change over the course of a CT focused unit? (b) What is the relationship between CT and natural selection in students' algorithmic explanations? (c) What are students' perspectives of learning natural selection with CT? Results indicate students' conceptions of natural selection increased and natural selection misconceptions decreased over the course of the unit. Within their post‐unit algorithmic explanations, students used specific CT principles in conjunction with natural selection concepts to explain natural selection, which helped them to learn the details of the natural selection process and correct their natural selection misconceptions. Students indicated the use of CT in unplugged algorithmic explanations in different contexts helped them learn natural selection. This study shows unplugged CT can be used to teach students science content, and it provides an example for further CT and science integrations. Implications for the field are discussed.

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“…As shown in Table 2, to teach CT, 15 studies employed coding (programming), 12 studies interested in developing teaching module, three studies employed unplugged activities, game-based, and framework development. Programming is widely used to teaching CT, including Scratch [14]- [21], followed by Phyton [14], [17], [18], [20], App Inventor [15], [21] Alice [22], Microsoft Kodu [22], Lego Mindstorms [22], Logo [17], C/C++ and BYOB [15]. Android programming was used in robot development [23], [24].…”

Section: Ct Educational Approachesmentioning

confidence: 99%

Reflecting on Computational Thinking Studies for High School Education

Sondakh

2019

CogITo Smart Journal

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Berpikir komputasional telah diakui sebagai suatu kebutuhan dalam menyelesaikan masalah yang kompleks. Beberapa penelitian telah dilakukan untuk memperkenalkan keterampilan ini ke semua tingkat pendidikan. Penelitian ini bertujuan untuk meninjau penelitian tentang berpikir komputasi pada tingkat sekolah menengah. Khususnya, penelitian ini mengkaji domain penelitian, mengidentifikasi metode-metode untuk memperkenalkan berpikir komputasional, serta konsep-konsep berpikir komputasional yang diajarkan kepada pelajar. Tinjauan literatur sistematik dilakukan untuk mencapai tujuan tersebut. Hasil penelitian menunjukkan: penelitian berpikir komputasional mencakup kajian teori, pengembangan kurikulum, pengukuran, dan pengembangan alat. Kajian teori ditujukan untuk memformulasikan konsep. Selain keterampilan teknis, soft-skills telah dinyatakan sebagai elemen berpikir komputasional. Namun, perhatian untuk melibatkan soft-skills dalam penelitian masih kurang. Sebagian besar penelitian difokuskan pada integrasi berpikir komptasional ke dalam kurikulum. Coding menjadi metode yang paling banyak digunakan untuk mengajarkan berpikir komputasional. Sehingga, algorithmic thinking dan abstraction muncul sebagai keterampilan yang paling sering diajarkan atau diukur. Akhirnya, penelitian ini menggarisbawahi adanya kesenjangan untuk dikaji lebih lanjut yaitu berkaitan dengan pengukuran keterampilan berpikir komputasional dan untuk menyertakan soft-skills pada penelitian berpikir komputasional. Kata Kunci—Berpikir komputasional, Sekolah menengah, Penyelesaian masalah

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DISSECT: Analysis of pedagogical techniques to integrate computational thinking into K-12 curricula

Cited by 15 publications

References 3 publications

Computational Thinking in Secondary Education: Where does it fit? A systematic literary review

Computational Thinking in Secondary Education: Where does it fit? A systematic literary review

Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

Reflecting on Computational Thinking Studies for High School Education

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