The effect of computer science on physics learning in a computational science environment

Taub, Rivka; Armoni, Michal; Bagno, Esther; Ben‐Ari, Mordechai

doi:10.1016/j.compedu.2015.03.013

Cited by 32 publications

(24 citation statements)

References 26 publications

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“…In addition to understanding how our curriculum can support students' development of CT-STEM practices, our research aims to understand how engagement in these practices can support students' science content learning. Research already points to the productivity of computational tools for science learning (Guzdial, 1994;National Research Council, 2011;Redish & Wilson, 1993;Repenning, Webb, & Ioannidou, 2010;Sengupta, Kinnebrew, Basu, Biswas, & Clark, 2013;Sherin, 2001;Taub, Armoni, Bagno, & Ben-Ari, 2015;Wilensky & Reisman, 2006). As described by restructuration theory, the representational form of knowledge influences how it can be understood.…”

Section: Discussionmentioning

confidence: 99%

Introducing and Assessing Computational Thinking in the Secondary Science Classroom

Swanson

Anton

Bain

et al. 2019

Computational Thinking Education

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The importance of computational thinking (CT) as a goal of science education is increasingly acknowledged. The representational affordances of computational tools are changing the way knowledge can be constructed, expressed, and understood across disciplines. Our group has worked to explicitly characterize CT practices used by computational STEM researchers (CT-STEM practices) and to develop computational science curricula that teach both CT-STEM practices and science content. We have previously characterized four strands of CT-STEM practices: data practices, modeling and simulation practices, computational problemsolving practices, and systems thinking practices. In this chapter, we show that a group of 9th grade students developed competencies for modeling and simulation practices as a result of their engagement in our computational biology curriculum. As evidence, we present findings from a quantitative analysis of students' written responses to assessments given before and after their participation in three computational biology units. Results suggest that the computational biology curriculum helped students develop a number of important competencies for the strand on modeling and simulation practices. Our work contributes to the field's understanding of how science curricula can be designed to foster students' development of CT-STEM practices and how this development can be assessed.

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Section: Discussionmentioning

confidence: 99%

Introducing and Assessing Computational Thinking in the Secondary Science Classroom

Swanson

Anton

Bain

et al. 2019

Computational Thinking Education

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“…implementation in the investigation phase), while Group 1 could not conduct the planned activities (deep-level use) into practice (surface-level use). This challenge may be explained by numerical analysis methods, which require skills from physics, mathematics, and computer science (Taub, Armoni, Bagno, & Ben-Ari, 2015). To solve the problem, students had to (i) understand the concepts relating to the phenomenon Figure 5.…”

Section: Discussionmentioning

confidence: 99%

Visualising the temporal aspects of collaborative inquiry-based learning processes in technology-enhanced physics learning

Lämsä

Hämäläinen

Koskinen

et al. 2018

International Journal of Science Education

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This study presents new ways of visualising technology-enhanced collaborative inquiry-based learning (CIBL) processes in an undergraduate physics course. The data included screen-capture videos from a technology-enhanced learning environment and audio recordings of discussions between students. We performed a thematic analysis based on the phases of inquiry-based learning (IBL). The thematic analysis was complemented by a content analysis, in which we analysed whether the utilisation of technological tools was on a deep-level, surface-level, or nonexistent basis. Student participation was measured in terms of frequency of contributions as well as in terms of impact. We visualised the sequence of the face-to-face interactions of two groups of five students by focussing on the temporal aspects of IBL, technology enhancement and collaborative learning. First, instead of the amount of time the groups spent on a specific IBL phase, the between-group differences in the most frequent transitions between the IBL phases determined their differential progress in the CIBL process. Second, we found that the transitions were triggered by the groups' ways of utilising technological tools either at the deep level or at the surface level. Finally, we found that the level of participation inequity remained stable throughout the CIBL process. As a result, only some of the members of the groups played a role in the most frequent transitions. Furthermore, this study reveals the need for scaffolds focussing on inquiry, technological and collaborative skills at the beginning of the learning process.

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“…2. Uso interactivo del conocimiento y la información 22 (36.1%) Reconocer y determinar lo que no se sabe; identificar, ubicar y acceder a fuentes apropiadas de información; evaluar la calidad, propiedad y el valor de dicha información, así como sus fuentes y organizar el conocimiento y la información (5,6,10,18,20,23,25,27,34,35,39,41,42,43,45,49,53,55,57,59,60,61).…”

Section: Influencia Del Lenguaje De Programación En Las Competencias unclassified

Lenguajes de programación y desarrollo de competencias clave. Revisión sistemática

Tejera-Martínez

Aguilera

Vílchez-González

2020

REDIE

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El presente estudio trata de dar respuesta a una serie de interrogantes relacionados con los lenguajes de programación en el marco de la educación formal; en particular, en qué etapas educativas se usan los lenguajes de programación y cuáles son los más utilizados. A su vez, se pretende conocer la repercusión de estos lenguajes en el aprendizaje del alumnado, para lo cual se ha estudiado su influencia en el desarrollo de las competencias clave. Se llevó a cabo una revisión sistemática, según los fundamentos de la declaración prisma, de los trabajos indexados en las bases de datos Web of Science y Scopus entre 2007 y 2018. Los resultados muestran un incipiente interés de la comunidad científica por los lenguajes de programación en entornos educativos formales. Asimismo, se ha constatado la eficacia del uso de los lenguajes de programación en el desarrollo competencial del alumnado.

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The effect of computer science on physics learning in a computational science environment

Cited by 32 publications

References 26 publications

Introducing and Assessing Computational Thinking in the Secondary Science Classroom

Introducing and Assessing Computational Thinking in the Secondary Science Classroom

Visualising the temporal aspects of collaborative inquiry-based learning processes in technology-enhanced physics learning

Lenguajes de programación y desarrollo de competencias clave. Revisión sistemática

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