Integrating Computational Thinking in STEM Education: A Literature Review

Wang, Changzhao; Shen, Ji; Chao, Jie

doi:10.1007/s10763-021-10227-5

Cited by 94 publications

(70 citation statements)

References 63 publications

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“…3) in various ways as an essential characteristic of CT for understanding and representing problems to readily solve, as shown in Table 2. Based on the literature, this aspect is operationalizable in three sub-aspects: (CT1a) describing a clear goal or question that can be answered, as well as an approach to answering the question using computational tools (Irgens et al, 2020;Shute et al, 2017;Wang et al, 2021), (CT1b) identifying the essential elements of a phenomenon or problem (Anderson, 2016;Türker & Pala, 2020), and (CT1c) describing elements in such a way that they are calculable for use in a computational representation of the phenomenon or problem (Brennan & Resnick, 2012;Chen et al, 2017;Hutchins et al, 2020;Lee & Malyn-Smith, 2020).…”

Section: Aspects and Sub-aspects Of Computational Thinkingmentioning

confidence: 99%

A framework for supporting systems thinking and computational thinking through constructing models

et al. 2022

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We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.

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Section: Aspects and Sub-aspects Of Computational Thinkingmentioning

confidence: 99%

A framework for supporting systems thinking and computational thinking through constructing models

et al. 2022

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“…The use of computation in undergraduate physics education is on the rise for various reasons. Students can use computation to simulate physical systems, conduct advanced analysis of experimental data, create insightful visualizations, explore analytically intractable problems, and bridge the gap between mathematical problems and experimental activities [1][2][3][4][5][6][7][8][9][10]. Upon graduating, students find that computation is prevalent in all fields of physics research and STEM industry [10][11][12][13], with faculty mentors and employers expecting well-developed computational skills in their research mentees and new hires [8,[14][15][16][17].…”

mentioning

confidence: 99%

“…Integrating computation into the undergraduate experience provides students with research-and industry-relevant skills [13,17,18] while helping them develop an appropriate view of building models and implementing them with a computer [19]. Computation enables students to pursue creative solutions [20,21], more directly engage in sense-making [5,6,22,23], and test a variety of model-based predictions [9,21,24,25]. Such activities equalize student mathematical backgrounds [26][27][28], promote deeper learning [13,21,29,30], and support underrepresented groups [9,[31][32][33].…”

mentioning

confidence: 99%

“…Computation enables students to pursue creative solutions [20,21], more directly engage in sense-making [5,6,22,23], and test a variety of model-based predictions [9,21,24,25]. Such activities equalize student mathematical backgrounds [26][27][28], promote deeper learning [13,21,29,30], and support underrepresented groups [9,[31][32][33]. In physics and beyond, computational thinking has become its own suite of learning objectives, including data practices, modeling and simulation practices, computational problem solving practices, and systems thinking [9,10,13,29,30,[34][35][36].…”

mentioning

confidence: 99%

“…Pedagogical strategies such as pair programming [42,43] promote a unique form of collaboration [36] as students use code to study models together. Assessing computationally integrated learning can take many forms, such as computationally integrated homework problems and term projects [9,12,21], computational essays [44,45], integrated code-notebook systems [46][47][48], or integrated lab reports with experiment-based work [2,3]. Observational studies are being conducted to identify what new challenges computation introduces to students [49] and how students tend to move through computational activities [36] so that the benefits of computation can be realized without additional interference.…”

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confidence: 99%

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Student Representations of Computation in the Physics Community

Lane¹,

Headley²

2021

Preprint

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Undergraduate physics education has greatly benefited from the introduction of computational activities. However, despite the benefits computation has delivered, we still lack a complete understanding of the computationally integrated learning experience from the student perspective. In particular, we are interested in investigating how students develop expert-like perceptions of computation as a practice within the professional physics community. To investigate this aspect of student development, we employ the Communities of Practice framework, which describes how students navigate through a professional community by appropriating the community's practices and goals as personally important. We introduce the construct of a COP-Model as a student's internal representation of a professional community that they develop as they navigate that community. We used this construct to formulate a set of research questions and semistructured interview protocols to explore how five physics students represent the use of computation in their mental models of the global physics community. We foreground these representations in the local academic community established by their instructors in three concurrent computationally integrated physics courses. We find that these students saw computation as a normal and valuable part of physics practice, identified benefits of using computation in alignment with the physics community, struggle with confidence with regards to computation, and demonstrate some expectations for computational proficiency that are misaligned with the physics community. We establish these themes with interview excerpts and discuss implications for instruction and future research.

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First experiences of integrating computational thinking into a blended learning in‐service training program for STEM teachers

Knie

Standl

Schwarzer

2022

Comp Applic In Engineering

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Over the last two decades, computational thinking (CT) has gained importance in discussions about competencies that students require to deal with complex problems in a world shaped by digitalization. Therefore, schools and teachers should integrate CT into their instructional practices. Particularly, the STEM (Science, Technology, Engineering, and Mathematics) subjects are seen as a meaningful context for embedding CT. Yet, there are only a few professional development programs that address STEM teachers and how to integrate CT. Thus, in our study, we redesigned an in‐service teacher training program. The redesigned program was offered in a blended‐learning format, consisting of alternating digital and face‐to‐face phases. As part of the digital phases, an interactive online self‐learning module on CT was introduced to foster transdisciplinary competencies in the field of STEM to solve complex problems. To identify the perceptions and development of the participants of the training program and the online module on CT a within‐subject design was used with a questionnaire survey, conducted with a pre‐post‐follow‐up design. The first results of the ongoing survey indicate high satisfaction with both the program itself and the online CT module. At the beginning of the program, about two‐thirds of the participating teachers were completely unfamiliar with the term CT, nevertheless, interest in the new topic was high. It was shown the program was able to create initial awareness of the meaning and importance of CT. In addition, teachers who have engaged with CT during the intervention seem to use CT‐related practices more often in the classroom afterward.

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Integrating Computational Thinking in STEM Education: A Literature Review

Cited by 94 publications

References 63 publications

A framework for supporting systems thinking and computational thinking through constructing models

A framework for supporting systems thinking and computational thinking through constructing models

Student Representations of Computation in the Physics Community

First experiences of integrating computational thinking into a blended learning in‐service training program for STEM teachers

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