Characteristics of Studies Conducted on Computational Thinking: A Content Analysis

Kalelıoğlu, Fılız

doi:10.1007/978-3-319-93566-9_2

Cited by 20 publications

(22 citation statements)

References 62 publications

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“…Additionally, studies integrating CT and science thus far have not provided evidence that CT can be used in an unplugged capacity to teach science content. To date, most CT implementations rely on computers and are conducted in programming contexts (Hsu et al, 2018;Kalelio glu, 2018;Lockwood & Mooney, 2017), which have been promising, but this study provides evidence that unplugged CT can support student learning through the creation of hand-written algorithmic explanations.…”

Section: Ct Update Helped Students Learn Science Contentmentioning

confidence: 78%

“…CT is relatively new to the field of science education, and while integrations of CT and science have been implemented in classrooms, the literature base is limited with few empirical studies focusing on CT learning and science learning (Kalelioglu et al, ). Moreover, a vast majority of CT implementations rely on computers and are conducted in programming contexts (Hsu, Chang, & Hung, ; Kalelioğlu, ; Lockwood & Mooney, ). Many integrations use computational models and simulations of scientific content, or data collection and analysis with programming to facilitate the integration of CT and science (reviewed in Peel et al, n.d.).…”

Section: Literature Reviewmentioning

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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Section: Ct Update Helped Students Learn Science Contentmentioning

confidence: 78%

Section: Literature Reviewmentioning

confidence: 99%

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

2019

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“…The development of computational thinking skills has recently become the focus of the attention of researchers and policymakers in Turkey, as in all countries (Gulbahar and Kalelioğlu, 2018;Tang Chou and Tsai, 2020). In this context, new course contents are prepared for the development of computational thinking skills, and the teaching programs are renewed.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, new activities continue to be prepared for different disciplines (Kaan, Çalışkan, & Yetişir, 2017). In addition, it is known that there has been a significant increase in articles, master's theses, and doctoral dissertations on computational thinking skills (İliç, Haseski, & Tuğtekin, 2018;Kalelioğlu, 2018). In the study conducted by Tang, Chou, and Tsai (2020), considering the studies on computational thinking skills between 2006 and 2018, it was observed that the number of studies on computational thinking skills increased significantly after 2013.…”

Section: Introductionmentioning

confidence: 99%

“…Researches show a remarkable increase in the number of studies in recent years regarding computational thinking skills in Turkey (Ilic, Haseski and Tuğtekin, 2018;Kalelioğlu, 2018). In this context, there is a need for studies that will provide a general framework in determining the tendencies, gaps, and clutter for studies on computational thinking skills.…”

Section: Introductionmentioning

confidence: 99%

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Türkiye’de Bilgi İşlemsel Düşünme Becerisi ile İlgili Yapılmış Lisansüstü Tezlerin Tematik ve Yöntemsel Eğilimleri

Elçiçek

2020

Sakarya University Journal of Education

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The purpose of this research is to analyse the thematic and methodological trends of computational thinking skills-related graduate theses in Turkey. Within the scope of this purpose, a total of 38 works, including 30 Master's and 8 doctoral studies, published in the National Thesis Centre database of YÖK (Council of Higher Education), were analysed using a document analysis model, one of the qualitative research methods. A descriptive content analysis technique was used to examine graduate theses. According to the results of the research, it has been concluded that the effects of programming teaching, robotics, and block-based visual programming on computational thinking skills are examined in graduate theses. It has been determined that a very limited number of design and development studies have been carried out to develop computational thinking skills. It has been observed that the majority of graduate theses were made in the field of Computer Education and Instructional Technology and most theses were published in 2019. It has been concluded that graduate theses are mostly designed by using the quantitative research method and quasi-experimental design, and secondary school sample profile is mostly preferred in the studies. However, it has been seen that the data collected by using a scale, interview, and observation data collection tools are analysed in accordance with predictive analysis techniques. As a result of the research, it has been recommended that design studies should be carried out in which qualitative or mixed research methods are used and especially pre-school sample profiles are preferred.

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Introducing Computer Science and Arts for All (CSA4ALL): Developing an Inclusive Curriculum and Portal for K5 Children

Pandey

Jamshidi

Marghitu

2023

Lecture Notes in Computer Science

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Characteristics of Studies Conducted on Computational Thinking: A Content Analysis

Cited by 20 publications

References 62 publications

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

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

Türkiye’de Bilgi İşlemsel Düşünme Becerisi ile İlgili Yapılmış Lisansüstü Tezlerin Tematik ve Yöntemsel Eğilimleri

Introducing Computer Science and Arts for All (CSA4ALL): Developing an Inclusive Curriculum and Portal for K5 Children

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