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1. 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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2. Making Sense of Models: Connecting Science and Math Through Decoding and Modifying Computational Models

   Lee, I; Sagartz, M; Meyer, P; Anderson, E (April 2025, Science scope)

   This practitioner paper describes the Making Sense of Models (MSM) curriculum that bridges math and science learning through agent based modeling and rich computational thinking investigations that do not require teaching computer programming in middle school classrooms. The MSM curriculum supports students in the NGSS skill of reasoning about how and why a phenomenon happens. After developing decoding skills, students are able to assess the validity of a model based on comparing mechanisms in the model to what they learned about the phenomenon being modeled. The paper also describes how the MSM curriculum supports students’ ability to reason about scientific models and the real world. 

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3. Using computational thinking and modeling to build water and watershed literacy

   Caplan, B.; Covitt, B.; Love, G.; Berkowitz, A.R.; Gunckel, K.L.; McClure, C.; Moore, J.C. (January 2021, Connected science learning)

   null (Ed.)

   Engaging students in science learning that integrates disciplinary knowledge and practices such as computational thinking (CT) is a challenge that may represent unfamiliar territory for many teachers. CompHydro Baltimore is a collaborative partnership aimed at enacting Next Generation Science Standards (NGSS)–aligned instruction to support students in developing knowledge and practice reflective of the goals laid out in A Framework for K–12 Science Education (National Research Council 2012) “... that by the end of 12th grade, all students possess sufficient knowledge of science and engineering to engage in public discussion on related issues … and are careful consumers of scientific and technological information related to their everyday lives.” This article presents the results of a partnership that generated a new high school level curriculum and teacher professional development program that tackled the challenge of integrating hydrologic learning with computational thinking as applied to a real-world issue of flooding. CompHydro Baltimore produced Baltimore Floods, a six-lesson high school unit that builds students’ water literacy by engaging them in computational thinking (CT) and modeling practices as they learn about water system processes involved in urban flooding (See Computational Thinking and Associated Science Practices). CompHydro demonstrates that broad partnerships can address these challenges, bringing together the diverse expertise necessary to develop innovative CT-infused science curriculum materials and the teacher supports needed for successful implementation. 

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4. Studying the Interactions Between Science, Engineering, and Computational Thinking in a Learning-by-Modeling Environment.

   https://doi.org/10.1007/978-3-030-52237-7_48  

   Zhang, N.; Biswas, G.; McElhaney, K.; Basu, S.; McBride, E.; Chiu, J. (June 2020, In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science.)

   Computational Thinking (CT) can play a central role in fostering students' integrated learning of science and engineering. We adopt this framework to design and develop the Water Runoff Challenge (WRC) curriculum for lower middle school students in the USA. This paper presents (1) the WRC curriculum implemented in an integrated computational modeling and engineering design environment and (2) formative and summative assessments used to evaluate learner’s science, engineering, and CT skills as they progress through the curriculum. We derived a series of performance measures associated with student learning from system log data and the assessments. By applying Path Analysis we found significant relations between measures of science, engineering, and CT learning, indicating that they are mutually supportive of learning across these disciplines. 

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5. How Teachers Support Student Computational Thinking Practices

   https://doi.org/10.22318/icls2020.2343  

   Bowers, J.; Shin, N.; Krajcik, J. (June 2020, How Teachers Support Student Computational Thinking Practices)

   Gresalfi, M.; Horn, I. S. (Ed.)

   Computational Thinking (CT) is increasingly being targeted as a pedagogical goal for science education. As such, researchers and teachers should collaborate to scaffold student engagement with CT alongside new technology and curricula. We interviewed two high school teachers who implemented a unit using dynamic modeling software to examine how they supported student engagement with CT through modeling practices. Based on their interviews, they believed that they supported student engagement in CT and modeling through preliminary activities, conducting classroom demonstrations of the phenomenon, and engaging students in model revisions through dialogue. 

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