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1. Analyzing Students’ Synergistic Learning Processes in Physics and CT by Collaborative Discourse Analysis

   Snyder, C.; Biswas, G.; Emara, M.; Grover, S.; & Conlin, L. (June 2019, Computer-supported collaborative learning)

   The introduction of computational modeling into science curricula has been shown to benefit students’ learning, however the synergistic learning processes that contribute to these benefits are not fully understood. We study students’ synergistic learning of physics and computational thinking (CT) through their actions and collaborative discourse as they develop computational models in a visual block-structured environment. We adopt a case study approach to analyze students synergistic learning processes related to stopping conditions, initialization, and debugging episodes. Our findings show a pattern of evolving sophistication in synergistic reasoning for model-building activities. 

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2. Assessing Students’ Knowledge Co-construction Behaviors in a Collaborative Computational Modeling Environment.

   https://doi.org/10.1007/978-3-031-11647-6_105  

   Snyder, C. (July 2022, AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham.)

   Rodrigo, M.M. (Ed.)

   Successful knowledge co-construction during collaborative learning requires students to develop a shared conceptual understanding of the domain through effective social interactions. Developing and applying shared understanding of concepts and practices is directly impacted by the prior knowledge that students bring to their interactions. We present a systematic approach to analyze students’ knowledge co-construction processes as they work through a physics curriculum that includes inquiry activities, instructional tasks, and computational model-building activities. Utilizing a combination of students’ activity logs and discourse analysis, we assess how students’ knowledge impacts their knowledge co-construction processes. We hope a better understanding of how students’ co-construction processes develop and the difficulties they face will lead to better adaptive scaffolding of students’ learning and better support for collaborative learning. 

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3. Analyzing debugging processes during collaborative, computational modeling in science

   Hutchins, N.M. (June 2021, Computersupported collaborative learning)

   Hmelo-Silver, C. E. (Ed.)

   This paper develops a systematic approach to identifying and analyzing high school students’ debugging strategies when they work together to construct computational models of scientific processes in a block-based programming environment. We combine Markov models derived from students’ activity logs with epistemic network analysis of their collaborative discourse to interpret and analyze their model building and debugging processes. We present a contrasting case study that illustrates the differences in debugging strategies between two groups of students and its impact on their model-building effectiveness. 

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4. Analyzing debugging processes during collaborative, computational modeling in science

   Hutchins, N.M.; Snyder, C.; Emara, M.; Grover, S.; Biswas, G. (June 2021, Computersupported collaborative learning)

   Hmelo-Silver, C. E. (Ed.)

   This paper develops a systematic approach to identifying and analyzing high school students’ debugging strategies when they work together to construct computational models of scientific processes in a block-based programming environment. We combine Markov models derived from students’ activity logs with epistemic network analysis of their collaborative discourse to interpret and analyze their model building and debugging processes. We present a contrasting case study that illustrates the differences in debugging strategies between two groups of students and its impact on their model-building effectiveness. 

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5. A DSML for a Robotics Environment to Support Synergistic Learning of CT and Geometry. Kong, S. C., Sheldon, J., & Li, K. Y.. (Eds.). Conference

   Hutchins, N. (July 2018, Proceedings of International Conference on Computational Thinking Education 2018.)

   Synergistic learning of computational thinking (CT) and STEM has proven to be an effective method for enhancing CT education as well as advancing learning in many STEM domains. Domain Specific Modeling Languages (DSML) facilitate the building of computational modeling frameworks that are directly linked to STEM content, thus making it easier for students to focus on concepts and practices. At the same time, teachers can more easily relate curricular content to the model building tasks. This paper discusses the design, development, and implementation of a robotics DSML to support a middle school geometry curriculum. 

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