Programming and its affordances for physics education: A social semiotic and variation theory approach to learning physics

Svensson, Kim; Eriksson, Urban; Pendrill, Ann-Marie

doi:10.1103/physrevphyseducres.16.010127

Cited by 18 publications

(11 citation statements)

References 27 publications

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“…Most of the studies focusing on science subjects examined the CT transfer effects in biology ( n = 6) (Arık & Topçu, 2022; Irgens et al, 2020; Nguyen & Santagata, 2021; Peel et al, 2019; Peel & Friedrichsen, 2018; Rachmatullah & Wiebe, 2022), physics ( n = 5) (Aksit & Wiebe, 2020; Hutchins et al, 2020; Orban et al, 2018; Svensson et al, 2020; Vieira et al, 2018) and integrated science contexts ( n = 5) (Basu et al, 2017; Guven et al, 2020; Lin et al, 2021; Sáez‐López et al, 2019; Sengupta et al, 2013). In addition, two studies assessed the effects in a chemistry context ( n = 2) (Bortz et al, 2020; Chongo et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

Lai

Wong

2022

Computer Assisted Learning

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Background: Computational thinking (CT) is regarded as an essential 21st-century skill, and attempts have been made to integrate it into other subjects. Instructional approaches to CT development and assessment in the field of computer science have attracted global attention, but the influence of CT skills on other subject areas is under-researched.Objective: Our goal is to investigate the transfer effects of CT in different subject areas and examine the educational characteristics of CT intervention approaches that promote the transfer of learning.Method: We carefully selected and reviewed 55 empirical studies from leading bibliographic databases and examined the transfer of CT using a meta-analysis and a qualitative synthesis. Results and Conclusions:We identified and summarized these effects in the fields of mathematics, science, engineering and the humanities. A meta-analysis of these studies identified a generally significant effect of the transfer of CT skills to other subject areas. We also explored the characteristics of CT interventions that aid the transfer of learning by qualitatively assessing the identified studies. The results of the review offer a holistic view of the trends in CT transfer research that can be used as a reference for both researchers and instructors. K E Y W O R D S computational thinking, meta-analysis, qualitative synthesis, transfer of learning 1 | INTRODUCTION Computational thinking (CT) has become a key motivator for bringing computer science back into K-12 schools (Tikva & Tambouris, 2021).CT is initially defined as a set of cognitive problem-solving skills originating from computer science (Wing, 2006), and gradually the notion of CT has been developed into a new cross-disciplinary literacy, which can be a vehicle for personal expression and can connect with other literacy practices (Kafai & Proctor, 2021). Grover and Pea (2013)

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Section: Resultsmentioning

confidence: 99%

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

Lai

Wong

2022

Computer Assisted Learning

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“…We saw a similar case with Blaine, who gave up after encountering numerous errors and no longer proceeded with the activity. From another perspective, Svensson et al [48] viewed computation as a social semiotic, or a way of communicating about and exploring phenomena. They saw challenges emerge when students had limited skill with using the semiotic resources, even when students did see the benefit of communicating and exploring through computation.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies highlighted the challenges and benefits to students of integrating computation into a physics setting. Svensson et al [48] viewed computation as a type of social semiotic, meaning it can be used to describe many different phenomena and it can produce many different answers to many different questions. In their view, becoming skilled at computation is like learning to communicate with a new language.…”

Section: Introductionmentioning

confidence: 99%

Students' perspectives on computational challenges in physics class

Hamerski¹,

McPadden²,

Caballero³

et al. 2022

Preprint

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High school science classrooms across the United States are answering calls to make computation a part of science learning. The problem is that there is little known about the barriers to learning that computation might bring to a science classroom or about how to help students overcome these challenges. This case study explores these challenges from the perspectives of students in a high school physics classroom with a newly revamped, computation-integrated curriculum. Focusing mainly on interviews to center the perspectives of students, we found that computation is a doubleedged sword: It can make science learning more authentic for students who are familiar with it, but it can also generate frustration and an aversion towards physics for students who are not.

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“…The study conducted by Noftiana, Nasir, and Islami (2019) showed that using scratch during teaching physics helps students to understand electric dynamics and students are interested in scratch animation. It was found that scratch helps the students to test themselves physics concepts and enable them to understand well physics (Svensson, Eriksson, & Pendrill, 2020). (Weber & Wilhelm, 2020) showed that the use of programming in physics education has a positive impact on students' conceptual understanding, critical thinking, and increase interest in physics.…”

Section: Introductionmentioning

confidence: 99%

Mathematics and Science Teacher’s Conception and Reflection on Computer Programming with Scratch: Technological and Pedagogical Standpoint

Iyamuremye

Nsabayezu

2022

IJETL

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The current study investigates teachers' conception and reflection of computer programming from scratch in terms of technological and pedagogical standpoint. Mixed research approaches typically achievement tests and interviews were used to collect quantitative and qualitative data respectively. A total of twenty-one Mathematics and Science teachers were randomly selected to participate in the study. A paired t-test was used to analyze quantitative data from pre and post-test, while descriptive and interpretive analyses were used to analyze qualitative data from the interview. The results of the study showed that Mathematic and Sciences teachers have a great conception of scratch programming after attending scratch programming. There was a significant difference in mean between pre and post-test (p ˂ 0.05). It was also found that scratch is an effective pedagogical tool for teaching and learning Mathematics and Sciences. In addition, teachers expressed positive views of using scratch in teaching and learning Mathematics and Sciences as it helps them to visualize abstract content, motivate students, increase students' interest, critical thinking, and problem-solving skills, act as an assessment tool and increase student's academic performance.

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Programming and its affordances for physics education: A social semiotic and variation theory approach to learning physics

Cited by 18 publications

References 27 publications

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

Students' perspectives on computational challenges in physics class

Mathematics and Science Teacher’s Conception and Reflection on Computer Programming with Scratch: Technological and Pedagogical Standpoint

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