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  1. 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.
  2. Introducing computational modeling into STEM classrooms can provide opportunities for the simultaneous learning of computational thinking (CT) and STEM. This paper describes the C2STEM modeling environment for learning physics, and the processes students can apply to their learning and modeling tasks. We use an unsupervised learning method to characterize student learning behaviors and how these behaviors relate to learning gains in STEM and CT.
  3. The devastating impact of climate change on coral reefs has reinforced our need to better understand their causes, especially the ones related to humans. Simultaneously, we need to raise awareness about the significance of reefs, both as an ecological host to twenty-five percent of marine life and as a key economic resource for millions of people. Opportunities afforded through coral reef research coupled with advances in computational modeling platforms may provide a unique opportunity to introduce the study of corals into K-12 STEM curricula by combining computational thinking (CT) constructs to build computational models that allow students to explore andmore »systematically study the effects of climate change on the reefs. We outline such a computational modeling curriculum in this paper.« less
  4. 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 ofmore »our approach and lay the foundation for guiding future automated approaches to detecting the synergistic learning of science and CT.« less
  5. 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.
  6. 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 resultsmore »of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices.« less
  7. Gresalfi, M. ; Horn, I. (Ed.)
    The design of most learning environments focuses on supporting students in making, constructing, and putting together projects on and off the screen, with much less attention paid to the many issues—problems, bugs, or traps—that students invariably encounter along the way. In this symposium, we present different theoretical and disciplinary perspectives on understanding how learners engage in debugging applications on and off screen, examine learners’ mindsets about debugging from middle school to college students and teachers, and present pedagogical approaches that promote strategies for debugging problems, even having learners themselves design problems for others. We contend that learning to identify andmore »fix problems—debug, troubleshoot, or get unstuck—in completing projects provides a productive space in which to explore multiple theoretical perspectives that can contribute to our understanding of learning and teaching critical strategies for dealing with challenges in learning activities and environments.« less