REL Midwest Ask A REL Response

Research Tools

July 2021

Aksit, O., & Wiebe, E. N. (2019). Exploring force and motion concepts in middle grades using computational modeling: A classroom intervention study. Journal of Science Education and Technology, 29(1), 65–82. https://eric.ed.gov/?id=EJ1246438

From the ERIC abstract: “Computational thinking (CT) and modeling are authentic practices that scientists and engineers use frequently in their daily work. Advances in computing technologies have further emphasized the centrality of modeling in science by making computationally enabled model use and construction more accessible to scientists. As such, it is important for all students to get exposed to these practices in K-12 science classrooms. This study investigated how a week-long intervention in a regular middle school science classroom that introduced CT and simulation-based model building through block-based programming influenced students’ learning of CT and force and motion concepts. Eighty-two seventh-grade students from a public middle school participated in the study. Quantitative data sources included pre- and post-assessments of students’ understanding of force and motion concepts and CT abilities. Qualitative data sources included classroom observation notes, student interviews, and students’ reflection statements. During the intervention, students were introduced to CT using block-based programming and engaged in constructing simulation-based computational models of physical phenomena. The findings of the study indicated that engaging in building computational models resulted in significant conceptual learning gains for the sample of this study. The affordances of the dynamic nature of computational models let students both ‘observe’ and ‘interact’ with the target phenomenon in real time while the generative dimension of model construction promoted a rich classroom discourse facilitating conceptual learning. This study contributes to the nascent literature on integrating CT into Kc12 science curricula by emphasizing the affordances and generative dimension of model construction through block-based programming.”

Basu, S., Kinnebrew, J. S., & Biswas, G. (2014). Assessing student performance in a computational-thinking based science learning environment. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Intelligent Tutoring Systems (pp. 476–481). Springer. https://link.springer.com/chapter/10.1007/978-3-319-07221-0_59

From the abstract: “Computational Thinking (CT) can effectively promote science learning, but K-12 curricula lack efforts to integrate CT with science. In this paper, we present a generic CT assessment scheme and propose metrics for evaluating correctness of computational and domain-specific constructs in computational models that students construct in CTSiM—a learning environment that combines CT with middle school science. We report a teacher-led, multi-domain classroom study using CTSiM and use our metrics to study how students’ model evolution relates to their pre-post learning gains. Our results lay the framework for online evaluation and scaffolding of students in CTSiM.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://www.sciencedirect.com/science/article/abs/pii/S0360131517300490?casa_token=UXS8Jpu6qJ4AAAAA:qzMp8LKz7AeJNdcTbrNizB17kU5ZHfrcCvaXlDN6zPIIF7VU-ffRrFvOvXWVHbm5zOsVN8xeiw

From the abstract: “Based on a framework of computational thinking (CT) adapted from Computer Science Teacher Association’s standards, an instrument was developed to assess fifth grade students’ CT. The items were contextualized in two types of CT application (coding in robotics and reasoning of everyday events). The instrument was administered as a pre and post measure in an elementary school where a new humanoid robotics curriculum was adopted by their fifth grade. Results show that the instrument has good psychometric properties and has the potential to reveal student learning challenges and growth in terms of CT.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://eric.ed.gov/?id=EJ995867

From the ERIC abstract: “Jeannette Wing’s influential article on computational thinking 6 years ago argued for adding this new competency to every child’s analytical ability as a vital ingredient of science, technology, engineering, and mathematics (STEM) learning. What is computational thinking? Why did this article resonate with so many and serve as a rallying cry for educators, education researchers, and policy makers? How have they interpreted Wing’s definition, and what advances have been made since Wing’s article was published? This article frames the current state of discourse on computational thinking in K-12 education by examining mostly recently published academic literature that uses Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). Computational thinking is more about thinking than computing. Journal for STEM Education Research, 3, 1–18. https://link.springer.com/article/10.1007%2Fs41979-020-00030-2

From the ERIC abstract: “Computational thinking is widely recognized as important, not only to those interested in computer science and mathematics but also to every student in the twenty-first century. However, the concept of computational thinking is arguably complex; the term itself can easily lead to direct connection with ‘computing’ or ‘computer’ in a restricted sense. In this editorial, we build on existing research about computational thinking to discuss it as a multi-faceted theoretical nature. We further present computational thinking, as a model of thinking, that is important not only in computer science and mathematics, but also in other disciplines of STEM and integrated STEM education broadly.”

Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3, 147–166. https://link.springer.com/article/10.1007/s41979-020-00044-w

From the ERIC abstract: “The recognized importance of computational thinking has helped to propel the rapid development of related educational efforts and programs over the past decade. Given the multi-faceted nature of computational thinking, which goes beyond programming and computer science, however, approaches and practices for developing students’ computational thinking are not always self-explanatory in terms of their foci and feasibility in diverse educational contexts. In this editorial, we first examine relevant publications in computational thinking to identify a trend of integrating computational thinking into disciplinary education. We subsequently build on recent discussions about the concept of computational thinking to (1) frame a review of educational efforts in developing students’ computational thinking, (2) discuss opportunities and challenges to further such educational efforts through not only programming and computer science but also other disciplines, and (3) articulate needed research and scholarship to support educational practices.”

Orban, C. M., & Teeling-Smith, R. M. (2020). Computational thinking in introductory physics. Physics Teacher, 58(4), 247–251. https://eric.ed.gov/?id=EJ1252125

From the ERIC abstract: “‘Computational thinking’ (CT) is still a relatively new term in the lexicon of learning objectives and science standards. The term was popularized in an essay by Wing, who said, ‘To reading, writing and arithmetic, we should add computational thinking to every child’s analytical ability.’ Agreeing with this premise, in 2013 the authors of the Next Generation Science Standards (NGSS) included ‘mathematical and computational thinking’ as one of eight essential science and engineering practices that K-12 teachers should strive to develop in their students. There is not yet widespread agreement on the precise definition or implementation of CT, and efforts to assess CT are still maturing, even as more states adopt K-12 computer science standards. In this article we will try to summarize what CT means for a typical introductory (i.e., high school or early college) physics class. This will include a discussion of the ways that instructors may already be incorporating elements of CT in their classes without knowing it.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Palts, T., & Pedaste, M. (2020). A model for developing computational thinking skills. Informatics in Education, 19(1), 113–128. https://eric.ed.gov/?id=EJ1248111

From the ERIC abstract: “Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation.”

Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K–12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. https://link.springer.com/article/10.1007/s10639-012-9240-x

From the abstract: “Computational thinking (CT) draws on concepts and practices that are fundamental to computing and computer science. It includes epistemic and representational practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction. However, these practices are also central to the development of expertise in scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating CT and programming into the K-12 STEM curricula. In this paper, we first present a theoretical investigation of key issues that need to be considered for integrating CT into K-12 science topics by identifying the synergies between CT and scientific expertise using a particular genre of computation: agent-based computation. We then present a critical review of the literature in educational computing, and propose a set of guidelines for designing learning environments on science topics that can jointly foster the development of computational thinking with scientific expertise. This is followed by the description of a learning environment that supports CT through modeling and simulation to help middle school students learn physics and biology. We demonstrate the effectiveness of our system by discussing the results of a small study conducted in a middle school science classroom. Finally, we discuss the implications of our work for future research on developing CT-based science learning environments.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://www.sciencedirect.com/science/article/abs/pii/S1747938X17300350?casa_token=Lni4EYgvVDUAAAAA:spbfx5RmA7DjFT8UXOsPjfXHIveAAyDjew-asmrWoI9xPO-o6qef8ArGn-uy8iPS9mVMOezG7Q

From the abstract: “This paper examines the growing field of computational thinking (CT) in education. A review of the relevant literature shows a diversity in definitions, interventions, assessments, and models. After synthesizing various approaches used to develop the construct in K-16 settings, we have created the following working definition of CT: The conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts. This definition highlights that CT is primarily a way of thinking and acting, which can be exhibited through the use particular skills, which then can become the basis for performance-based assessments of CT skills. Based on the literature, we categorized CT into six main facets: decomposition, abstraction, algorithm design, debugging, iteration, and generalization. This paper shows examples of CT definitions, interventions, assessments, and models across a variety of disciplines, with a call for more extensive research in this area.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Swanson, H., Anton, G., Bain, C., Horn, M., & Wilensky, U. (2017, July). Computational thinking in the science classroom. International Conference on Computational Thinking Education, Hong Kong. https://par.nsf.gov/biblio/10026244

From the abstract: “The importance of Computational Thinking (CT) as a goal of science education is increasingly acknowledged. This study investigates the effect of computationally-enriched science curriculum on students’ development of CT practices. Over the course of one school year, biology lessons featuring the exploration of NetLogo models were implemented in the classrooms of three 9th grade biology teachers at an urban public secondary school in the United States. One-hundred thirty-three biology students took both pre- and post-tests that were administered at the beginning and end of the school year. The students’ responses to relevant assessment items were coded and scored using rubrics designed to evaluate their mastery of two learning objectives relating to modeling and simulation practices. The first learning objective was to explore the relationship between a system’s parameters and its behavior . The second learning objective was to identify the simplifications made by a model. Each item’s pre- and post-test scores were compared using a Wilcoxon signed-rank test. Results indicate a statistically significant improvement with respect to the second of the two learning objectives, suggesting that the computationally-enriched biology curriculum enhanced students’ ability to identify the simplifications made by a model.”

Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://www.sciencedirect.com/science/article/abs/pii/S0360131519303483

From the ERIC abstract: “With the increasing attention to Computational Thinking (CT) in education, there has been a concomitant rise of needs and interest in investigating how to assess CT skills. This study systematically reviewed how CT has been assessed in the literature. We reviewed 96 journal articles to analyze specific CT assessments from four perspectives: educational context, assessment construct, assessment type, and reliability and validity evidence. Our review results indicate that (a) more CT assessments are needed for high school, college students, and teacher professional development programs, (b) most CT assessments focus on students’ programming or computing skills, (c) traditional tests and performance assessments are often used to assess CT skills, and surveys are used to measure students’ CT dispositions, and (d) more reliability and validity evidence needs to be collected and reported in future studies. This review identifies current research gaps and future directions to conceptualize and assess CT skills, and the findings are expected to be beneficial for researchers, curriculum designers, and instructors.”

Note: REL Midwest was unable to locate a link to the full-text version of this resource. Although REL Midwest tries to provide publicly available resources whenever possible, it was determined that this resource may be of interest to you. It may be found through university or public library systems.

Taslibeyaz, E., Kursun, E., & Karaman, S. (2020). How to develop computational thinking: A systematic review of empirical studies. Informatics in Education, 19(4), 701–719. https://eric.ed.gov/?id=EJ1279434

From the ERIC abstract: “The primary purpose of this study is to investigate CT skills development process in learning environments. It is also aimed to determine the conceptual understanding and measurement approaches in the studies. To achieve these aims, a systematic research review methodology was implemented as the research design. Empirical studies on computational thinking indexed in the Web of Science and ERIC databases were selected without constraint on the publication dates. The studies found were examined and a pre-analysis was conducted by the researchers. Following the pre-analysis, 29 articles were selected to be included in the study. Content analysis was applied in order to determine and evaluate the common codes and themes related to the findings. In conclusion, instead of relying on attractiveness, functionality, market share of educational tools (robotic sets, software packets etc.), availability of qualified learning activities focused on problem solving is the main point practitioners should consider.”

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://eric.ed.gov/?id=EJ1087675

From the ERIC abstract: “Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include ‘computational thinking’ as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.”

Wing, J. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7–14. https://www.learntechlib.org/p/183466/

From the abstract: “Computer science has produced, at an astonishing and breathtaking pace, amazing technology that has transformed our lives with profound economic and societal impact. In the course of the past ten years, we have come to realize that computer science offers not just useful software and hardware artifacts, but also an intellectual framework for thinking, what I call ‘computational thinking’. Everyone can benefit from thinking computationally. My grand vision is that computational thinking will be a fundamental skill—just like reading, writing, and arithmetic—used by everyone by the middle of the 21st Century.”