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1. Coherence across conceptual and computational representations of students’ scientific models.

   Hutchins, N.M. ; Basu, S. ; McElhaney, K. ; Chiu, J. ; Fick, S. ; Zhang, N. ; Biswas, G. ( June 2021 , Computersupported collaborative learning)

   de Vries, E. (Ed.)

   We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinementsmore »to our curriculum and scaffolding of students to further support the integrated learning of science and CT.« less
2. Coherence across conceptual and computational representations of students’ scientific models

   Hutchins, N.M. ; Basu, S. ; McElhaney, K. ; Chiu, J. ; Fick, S. ; Zhang, N. ; Biswas, G. ( June 2021 , Computersupported collaborative learning)

   de Vries, E. (Ed.)

   We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinementsmore »to our curriculum and scaffolding of students to further support the integrated learning of science and CT.« less
3. 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)

   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 authenticmore »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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4. 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)

   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 sciencemore »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.« less
5. "Making" Science Relevant for the 21st Century: Early Lessons from a Research-Practice Partnership

   https://doi.org/10.1145/3311890.3311910  

   Fancsali, Cheri ; Mirakhur, Zitsi ; Klevan, Sarah ; Rivera-Cash, Edgar ( March 2019 , FabLearn 2019 proceedings)

   The Maker Partnership Program (MPP) is an NSF-supported project that addresses the critical need for models of professional development (PD) and support that help elementary-level science teachers integrate computer science and computational thinking (CS and CT) into their classroom practices. The MPP aims to foster integration of these disciplines through maker pedagogy and curriculum. The MPP was designed as a research-practice partnership that allows researchers and practitioners to collaborate and iteratively design, implement and test the PD and curriculum. This paper describes the key elements of the MPP and early findings from surveys of teachers and students participating in themore »program. Our research focuses on learning how to develop teachers’ capacity to integrate CS and CT into elementary-level science instruction; understanding whether and how this integrated instruction promotes deeper student learning of science, CS and CT, as well as interest and engagement in these subjects; and exploring how the model may need to be adapted to fit local contexts. Participating teachers reported gaining knowledge and confidence for implementing the maker curriculum through the PDs. They anticipated that the greatest implementation challenges would be lack of preparation time, inaccessible computer hardware, lack of administrative support, and a lack of CS knowledge. Student survey results show that most participants were interested in CS and science at the beginning of the program. Student responses to questions about their disposition toward collaboration and persistence suggest some room for growth. Student responses to questions about who does CS are consistent with prevalent gender stereotypes (e.g., boys are naturally better than girls at computer programming), particularly among boys.« less