DIP: Extending CTSiM: An Adaptive Computational Thinking Environment for Learning Science through Modeling and Simulation in Middle School Classrooms

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The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology, and PI teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. An important issue in education is helping learners understand scientific phenomena, especially those that are too small or large, fast or slow, dangerous or inconvenient to experience and manipulate first hand. A way of helping learners experience such phenomena is through modeling -- building a model of the phenomenon or process and then manipulating it to see what happens in different circumstances. Computer tools are available for creating such models, but model building, though very useful for learning, is a complex and difficult task for many learners. In this team's Cyberlearning Exploration (EXP) Project, they developed what looks like a promising way to help middle school students learn to build computational models of scientific phenomena. They designed a visual language for expressing models and showed how progressing from less to more sophisticated models across several phenomena could help middle schoolers not only learn targeted science content but also learn how to design and build models and how to interpret and learn from models. In this follow-on project, they build on that approach, aiming to extend the technology to cover more sciences, automate some of the help teachers provide to students as they engage in model building and interpretation, systematically study the challenges learners face in learning through model building and interpretation, and identify pedagogical approaches that will foster successful learning in these circumstances. The goal of this proposal is to improve middle schoolers' computational thinking and scientific modeling capabilities in parallel with each other and in a way that prepares young learners for the computational sciences of the future. The PIs' earlier Cyberlearning EXP project explored the potential of using what is known about learning to be a computational thinker to guide sequencing of activities for fostering model-building and interpretation capabilities in science. The sequencing they proposed has middle schoolers building models of phenomena that they gradually make more sophisticated over the course of a science unit, then in the next unit, repeating that sequencing again, but with different content requiring more sophisticated or different modeling practices. They designed a language for model specification that they hoped would foster computational thinking and model-building capabilities. CTSIM, the software that supports the approach, is a visual programming platform for modeling scientific modeling that includes discipline-specific constructs, provides a scalable architecture that seamlessly weaves together model construction, simulation, testing, experimentation, and verification, is usable across science domains, and connects to NetLogo, which runs the simulations. In this project, the team will extend the technology to cover more sciences, add adaptive scaffolding based on what they saw teachers providing to help students build and learn from models, identify pedagogical approaches for promoting learning from model building, and systematically study the challenges students face and how to address those challenges. Their research questions focus on students' abilities to simultaneously learn science content and computational thinking skills involved in modeling, the ins and outs of using linked representations, the kinds of feedback needed for scaffolding the approach, and scaling issues for teachers.