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

Lockwood, James; Mooney, Aidan

doi:10.21585/ijcses.v2i1.26

Cited by 111 publications

(104 citation statements)

References 128 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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, ; Kalelioğlu, ; Lockwood & Mooney, ), 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: Discussionmentioning

confidence: 76%

“…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%

See 1 more Smart Citation

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

2019

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 76%

Section: Literature Reviewmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

“…by conducting a thorough review of the literature body. They showed testing possibilities, the importance for institutional adoption, and the interdisciplinary implementation [34].…”

Section: Teaching the Teachersmentioning

confidence: 99%

The Missing Link to Computational Thinking

Pollak

Ebner

2019

Future Internet

View full text Add to dashboard Cite

After a lengthy debate within the scientific community about what constitutes the problem solving approach of computational thinking (CT), the focus shifted to enable the integration of CT within compulsory education. This publication strives to focus the discussion and enable future research in an educational setting with a strong focus on the Austrian circumstances and the goal to allow wide international adoption later on. Methodically, a literature review was conducted to gain knowledge about the current strands of research and a meta study to show the diversity of proposed and materialized studies. Three main questions were answered, establishing that CT as an idea is rooted in scientific literature dating back to the 1980s and grew in popularity after Wing introduced the concept to a broader audience. A number of authors contributed to the current state of the field, with the most cited review coming from Grover and Pea. The challenge to integrate CT in curricula around the world was met by many experiments and case studies but without a conclusive framework as of yet. Ultimately, this paper determines that expert integration is a blank spot in the literature and aims to create a strong, inclusive path to CT education by inviting practitioners.

show abstract

“…Computational Thinking (CT) has been described as an essential skill which everyone should learn and can include in skill set [3]. Computational thinking is an important skill for everyone and it should be considered as an important component of students' analytical ability along with reading, writing, and arithmetic [4].…”

Section: Introductionmentioning

confidence: 99%

The Contribution of Educational Robotics and Constructivist Approach to Computational Thinking in the 21st Century

Istikomah¹,

Budiyanto²

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

View full text Add to dashboard Cite

Recent years later, there have been researches exploring the importance of computational thinking as an important skill needed by the students to manage activities in all disciplines. The development of computational thinking requires media or tool to help the students develop skill through their experience. Robotics can be used as a media to give students opportunities with the learning by their experience. The correct approach is needed to help the students develop computational thinking through their experience with robotics. A constructivist approach is a prominent approach to teaching with the learning experience. The constructivist approach may potentially be considered of the development of computational thinking skills. This paper reviews the contribution of robotics and constructivist approach to computational thinking. The insight from the reviewed papers, subsequently, was analysed the effect of the use of the robotics and constructivist approach to computational thinking.

show abstract

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

Cited by 111 publications

References 128 publications

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

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

The Missing Link to Computational Thinking

The Contribution of Educational Robotics and Constructivist Approach to Computational Thinking in the 21st Century

Contact Info

Product

Resources

About