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1. Instructor facilitation of STEM+CT discourse: engaging, prompting and guiding students’ computational modeling in physics

   Snyder, C. ( June 2022 , Computersupported collaborative learning)

   Chinn, C. (Ed.)

   The integration of computational modeling into instruction in science classrooms is complex in that it requires the synergistic application of students’ developing science and computational thinking knowledge. This is not only difficult for students, but teachers often find it hard to parse the science content from the computational constructs to guide students when they have difficulties. Leveraging past literature that highlights the beneficial impact instructors can have when they immerse themselves in group problem-solving discussions, this paper examines the instructors’ role in facilitating students’ construction of and problem-solving with computational models. We utilize a case study approach to analyze instructor-facilitated, synchronous group discussions during applications of synergistic learning processes to understand how instructors may elicit students’ knowledge, misunderstanding, and difficulties to help guide, prompt, and engage groups in this complex task for more productive integration in K-12 science classrooms. We hope that this will lead to better scaffolding of students' learning, and better support for teachers when they use such curricula in classrooms. 

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2. Understanding Students’ Model Building Strategies Through Discourse Analysis

   Snyder, C. ; Hutchins, N. ; Biswas, G. ; & Grover, S. ( June 2019 , International Conference on Artificial Intelligence in Education (AIED) 2019.)

   The benefits of computational model building in STEM domains are well documented yet the synergistic learning processes that lead to the effective learning gains are not fully understood. In this paper, we analyze the discussions between students working collaboratively to build computational models to solve physics problems. From this collaborative discourse, we identify strategies that impact their model building and learning processes. 

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3. Studying Synergistic Learning of Physics and Computational Thinking in a Learning by Modeling Environment

   Hutchins, N. ; Biswas, G. ; Conlin, L. ; Emara, M. ; Grover, S. ; Basu, S. ( November 2018 , Proceedings of the 26th International Conference on Computers in Education. Philippines: Asia-Pacific Society for Computers in Education)

   Synergistic learning of computational thinking (CT) and STEM has proven to effective in helping students develop better understanding of STEM topics, while simultaneously acquiring CT concepts and practices. With the ubiquity of computational devices and tools, advances in technology,and the globalization of product development, it is important for our students to not only develop multi-disciplinary skills acquired through such synergistic learning opportunities, but to also acquire key collaborative learning and problem-solving skills. In this paper, we describe the design and implementation of a collaborative learning-by-modeling environment developed for high school physics classrooms. We develop systematic rubrics and discuss the results of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices. 

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4. Using Collaborative Interactivity Metrics to analyze students’ Problem-Solving Behaviors during STEM+C Computational Modeling Tasks

   Snyder Caitlin ; Hutchins, Nicole ; Cohn, Clayton ; Fonteles, Joyce ; Biswas, Gautam ( September 2023 , Learning and individual differences)

   Grieff, S. (Ed.)

   Recently there has been increased development of curriculum and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) learning environments. These environments serve as a catalyst for authentic collaborative problem-solving (CPS) and help students synergistically learn STEM+C content. In this work, we analyzed students’ collaborative problem-solving behaviors as they worked in pairs to construct computational models in kinematics. We leveraged social measures, such as equity and turn-taking, along with a domain-specific measure that quantifies the synergistic interleaving of science and computing concepts in the students’ dialogue to gain a deeper understanding of the relationship between students’ collaborative behaviors and their ability to complete a STEM+C computational modeling task. Our results extend past findings identifying the importance of synergistic dialogue and suggest that while equitable discourse is important for overall task success, fluctuations in equity and turn-taking at the segment level may not have an impact on segment-level task performance. To better understand students’ segment-level behaviors, we identified and characterized groups’ planning, enacting, and reflection behaviors along with monitoring processes they employed to check their progress as they constructed their models. Leveraging Markov Chain (MC) analysis, we identified differences in high- and low-performing groups’ transitions between these phases of students’ activities. We then compared the synergistic, turn-taking, and equity measures for these groups for each one of the MC model states to gain a deeper understanding of how these collaboration behaviors relate to their computational modeling performance. We believe that characterizing differences in collaborative problem-solving behaviors allows us to gain a better understanding of the difficulties students face as they work on their computational modeling tasks. 

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5. Examining Synergistic Learning of Physics and Computational Thinking through Collaborative Problem Solving in Computational Modeling

   Grover, S. ; Biswas, G. ; Hutchins, N. ; Snyder, C. ; & Emara, M. ( April 2019 , Annual Meeting of the American Education Research Association)

   Computational modeling has been shown to benefit integrated learning of science and computational thinking (CT), however the mechanics of this synergistic learning are not well understood. In this research, we examine discourse during collaborative computational model building through the lens of a collaborative problem solving framework to gain insights into collaboration and synergistic learning of high school physics and CT. We pilot our novel approach in the context of C2STEM, a designed modeling environment, and examine collaboration and synergistic learning episodes in a video capture of a dyad modeling 2D motion with constant velocities. Our findings exhibit the promise of our approach and lay the foundation for guiding future automated approaches to detecting the synergistic learning of science and CT. 

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