Defining and Assessing Students’ Computational Thinking in a Learning by Modeling Environment

Zhang, Ningyu; Biswas, Gautam

doi:10.1007/978-981-13-6528-7_12

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…They argue that CT is primarily related to thinking rather than programming [74]. It is a common belief that physics, mathematics, informatics, chemistry, and biology constitute the appropriate framework for students to promote the acquisition of essential practices of CT [75][76][77][78][79]. Moreover, this is the case whether CT is linked to programming environments, which is significantly confirmed for computer science or not necessarily, as in physics, mathematics, chemistry, and biology.…”

Section: Research Objectivesmentioning

confidence: 97%

Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses

Bounou,

Lavidas,

Komis

et al. 2023

Education Sciences

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According to numerous researchers, a clear and direct correlation exists between Computational Thinking (CT) and courses falling under the purview of Science, Technology, Engineering, and Mathematics (STEM), thereby advocating for the integration of CT into the curricula of STEM courses. Nonetheless, it is noteworthy that only a few studies have scrutinized this correlation in-depth. Most such studies connect the correlation tacitly and predominantly concentrate on the empirical assessment of CT within the curriculum of one STEM discipline. This research seeks to evaluate the Computational Thinking abilities of 80 high school students in Greece and discern the extent of correlation with their academic performance in STEM and Greek language courses. A longitudinal survey was executed to accomplish this objective, commencing with administering a test designed to gauge the fundamental components of Computational Thinking. It is worth noting that this test draws its inspiration from internationally recognized computer competitions and serves as a credible assessment tool. Subsequently, an assessment was carried out to ascertain the degree of correlation between students’ Computational Thinking aptitude and their written performance in the subjects encompassed by the STEM category and the Greek language courses. The outcomes of this investigation revealed the presence of a statistically significant correlation between students’ Computational Thinking proficiency and their performance in these academic subjects, further extending to the academic direction of study chosen by the students. Based on the findings of this research, implications and pedagogical recommendations are delineated while concurrently acknowledging the limitations encountered during this study.

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Section: Research Objectivesmentioning

confidence: 97%

Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses

Bounou,

Lavidas,

Komis

et al. 2023

Education Sciences

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“…Studies have shown that applying CT in STEM domains through modeling enhances students’ learning (Basu et al, 2016 ; García-Peñalvo, Reimann, Tuul, Rees, & Jormanainen, 2016 ; Gadanidis et al, 2016 ; Jaipal-Jamani & Angeli, 2017 ; Pei et al, 2018 ; Zhang & Biswas, 2019 ). These studies show that programming for computational modeling can serve as an effective vehicle for learning challenging science and math concepts.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several studies in educational STEM emphasize the fact that CT can be practiced across content-domains in STEM. These studies address the impact of integrating CT into a learning process that focuses on enhancing both CT and conceptual understanding through computational modeling of complex systems (Basu et al, 2014;Blikstein & Wilensky, 2009;diSessa, 2000;Guzdial, 1995;Hambrusch et al, 2009;Kaput, 1994;Wilensky & Resnick, 1999;Zhang & Biswas, 2019).…”

Section: Introductionmentioning

confidence: 99%

Promoting learning transfer in science through a complexity approach and computational modeling

2023

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This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic phenomena using the Much.Matter.in.Motion (MMM) platform. A distinct innovation of this work is the complexity-based visual epistemic structure underpinning the Much.Matter.in.Motion (MMM) platform, which guided students' modeling of complex systems. This epistemic structure suggests that a complex system can be described and modeled by defining entities and assigning them (1) properties, (2) actions, and (3) interactions with each other and with their environment. In this study, we investigated students’ conceptual understanding of science, systems understanding, and CT. We also explored whether the complexity-based structure is transferable across different domains. The study employs a quasi-experimental, pretest-intervention-posttest-control comparison-group design, with 26 seventh-grade students in an experimental group, and 24 in a comparison group. Findings reveal that students who constructed computational models significantly improved their science conceptual knowledge, systems understanding, and CT. They also showed relatively high degrees of transfer—both near and far—with a medium effect size for the far transfer of learning. For the far-transfer items, their explanations included entities’ properties and interactions at the micro level. Finally, we found that learning CT and learning how to think complexly contribute independently to learning transfer, and that conceptual understanding in science impacts transfer only through the micro-level behaviors of entities in the system. A central theoretical contribution of this work is to offer a method for promoting far transfer. This method suggests using visual epistemic scaffolds of the general thinking processes we would like to support, as shown in the complexity-based structure on the MMM interface, and incorporating these visual structures into the core problem-solving activities. Supplementary Information The online version contains supplementary material available at 10.1007/s11251-023-09624-w.

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“…Moreover, CT integrated STEM learning enables students to see and test their hypotheses and visualize how systems work by using computer modeling and simulation (Lee & Malyn-Smith, 2020; Lee & Martin, 2016; Weintrop et al, 2014, 2016). Studies have suggested that the integration of CT into STEM learning can improve learning performance in specific STEM disciplines, such as science (e.g., Aksit & Wiebe, 2020; Irgens et al, 2020; Sengupta et al, 2013; Waterman et al, 2020), mathematics (e.g., Rodríguez-Martínez et al, 2020), physics (e.g., Aksit & Wiebe, 2020; Ferrarelli & Iocchi, 2021; Hutchins et al, 2020; Zhang & Biswas, 2019; Zhang et al, 2019), chemistry (e.g., Aslan et al, 2020), and biology (e.g., Dickes & Sengupta, 2013; Peel et al, 2019). Furthermore, CT integration improved STEM learning performance when it was integrated with multiple STEM disciplines, including science and engineering (Zhang, 2020; Zhang et al, 2020), science and mathematics (Sáez-López et al, 2019), physics and engineering (Yin et al, 2020), and physics and biology (Basu, Biswas, Sengupta et al, 2016).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

The Effects of Computational Thinking Integration in STEM on Students’ Learning Performance in K-12 Education: A Meta-analysis

Cheng

Ritzhaupt

2022

Journal of Educational Computing Research

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Computational thinking is believed to be beneficial for Science, Technology, Engineering, and Mathematics (STEM) learning as it is closely related to many other skills required by STEM disciplines. There has been an increasing interest in integrating computational thinking into STEM and many studies have been conducted to examine the effects of this intervention. This meta-analysis examined the effects of computational thinking integration in STEM on students’ STEM learning performance in the K-12 education context. Following systematic procedures, we identified 20 publications with 21 studies meeting the inclusion and exclusion criteria from a range of academic databases. We extracted effect sizes on student learning outcomes in one-group pretest-posttest designs. We also examined a range of moderating variables in the models, including student levels, STEM disciplines, intervention durations, alignment with content standards (e.g., CSTA/NGSS), types of intervention (e.g., simulation), and the use of unplugged/plugged activities. Overall, we found a statistically significant large effect size ( g = 0. 85 [95% CI of 0.57–1.14]; p < .001), indicating a large overall effect of computational thinking integration on STEM learning outcomes. The effect sizes were significantly moderated by intervention durations. We provide a discussion of the findings and present implications for future research and practice.

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Defining and Assessing Students’ Computational Thinking in a Learning by Modeling Environment

Cited by 14 publications

References 18 publications

Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses

Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses

Promoting learning transfer in science through a complexity approach and computational modeling

The Effects of Computational Thinking Integration in STEM on Students’ Learning Performance in K-12 Education: A Meta-analysis

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