More Like this

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 refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT. 

   more » « 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 refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT. 

   more » « less
3. 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. 

   more » « less
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)

   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. 

   more » « less
5. 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. 

   more » « less