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1. Promoting Computational Thinking through Project-Based Learning

   https://doi.org/doi.org/10.1186/s43031-021-00033-y  

   Shin, N.; Bowers, J.; Krajcik, J.; Damelin, D. (August 2021, Disciplinary and Interdisciplinary Science Education Research)

   null (Ed.)

   This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT. 

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2. Promoting computational thinking through project-based learning

   https://doi.org/10.1186/s43031-021-00033-y  

   Shin, Namsoo; Bowers, Jonathan; Krajcik, Joseph; Damelin, Daniel (August 2021, Disciplinary and Interdisciplinary Science Education Research)

   Abstract This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT. 

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3. Applying a mathematical sense-making framework to student work and its potential for curriculum design

   https://doi.org/17010138  

   Gifford, J.D. and (May 2021, Physical review)

   null (Ed.)

   This paper extends prior work establishing an operationalized framework of mathematical sense making (MSM) in physics. The framework differentiates between the object being understood (either physical or mathematical) and various tools (physical or mathematical) used to mediate the sense-making process. This results in four modes of MSM that can be coordinated and linked in various ways. Here, the framework is applied to novel modalities of student written work (both short answer and multiple choice). In detailed studies of student reasoning about the photoelectric effect, we associate these MSM modes with particular multiple choice answers, and substantiate this association by linking both the MSM modes and multiple choice answers with finer-grained reasoning elements that students use in solving a specific problem. Through the multiple associations between MSM mode, distributions of reasoning elements, and multiple- choice answers, we confirm the applicability of this framework to analyzing these sparser modalities of student work and its utility for analyzing larger-scale (N > 100) datasets. The association between individual reasoning elements and both MSM modes and MC answers suggest that it is possible to cue particular modes of student reasoning and answer selection. Such findings suggest potential for this framework to be applicable to the analysis and design of curriculum. 

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4. Using Students’ Conceptual Models to Represent Understanding of Crosscutting Concepts in an NGSS-Aligned Curriculum Unit About Urban Water Runoff

   https://doi.org/10.1007/s10956-021-09911-6  

   Fick, Sarah J.; McAlister, Anne M.; Chiu, Jennifer L.; McElhaney, Kevin W. (March 2021, Journal of Science Education and Technology)

   null (Ed.)

   Recent science education reforms, as described in the Framework for K-12 Science Education (NRC, 2012), call for three-dimensional learning that engages students in scientific practices and the use of scientific lenses to learn science content. However, relatively little research at any grade level has focused on how students develop this kind of three-dimensional knowledge that includes crosscutting concepts. This paper aims to contribute to a growing knowledge base that describes how to engage students in three-dimensional learning by exploring to what extent elementary students represent the crosscutting concept systems and system models when engaged in the practice developing and using models as part of an NGSS-aligned curriculum unit. This paper answers the questions: How do students represent elements of crosscutting concepts in conceptual models of water systems? How do students’ representations of crosscutting concepts change related to different task-based scaffolds? To analyze students’ models, we developed and applied a descriptive coding scheme to describe how the students illustrated the flow of water. The results show important differences in how students represented system elements across models. Findings provide insight for the kinds of support that students might need in order to move towards the development of three-dimensional understandings of science content. 

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5. What dimensions do students notice through computational modeling and data analysis?: An investigation using MoDa

   Wagh, A.; Eloy, A.; Fuhrmann, T.; Rosenbaum, L.; Blikstein, P.; Wilkerson, M. (January 2023, Annual International Conference of the National Association for Research in Science Teaching (NARST))

   This paper draws on a larger project in which we design for students to iteratively engage in scientific practices of computational modeling and data analysis. Here, we report on two sixth-grade science classes’ work in a unit about how ink diffuses through hot and cold water. Using interaction analysis, we analyzed what dimensions students attended to as they analyzed data, constructed computational models, and compared the two to validate their models. Our analysis led to three findings: 1. Visual cues from video data were salient to students who heavily drew on them to iterate on their models.; 2. Programming computational models raised questions about the behavior of the individual particles in the phenomenon.; and 3. The visual data made salient the contrasting conditions being modeled. However, instead of developing a single model that explained diffusion in both hot and cold water, students programmed distinct behaviors for each condition. The findings illustrate how visual data and modeling together can help students generate explanations to account for scientific phenomena and show evidence that students need explicit supports for thinking about models as providing an explanation for a range of related conditions in the system. 

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