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Abstract BackgroundThis study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on three two-week-long team-based computational modeling and simulation projects and reflected upon their experience. ResultsResults characterized different levels of engagement with computational model-based learning in the form of problem formulation and model planning, implementation and use of the computational model, evaluation, and interpretation of the outputs of the model, as well as reflection on the process. Results report on students’ levels of representational competence as related to the computational model, meaning-making of the underlying code of the computational model, graphical representations generated by the model, and explanations and interpretations of the output representations. Results also described regulatory skills as challenges and strategies related to programming skills, challenges and strategies related to meaning-making skills for understanding and connecting the science to the code and the results, and challenges and strategies related to process management mainly focused on project management skills. ConclusionCharacterizing dimensions of computational model-based reasoning provides insights that showcase students’ learning, benefits, and challenges when engaging in team-based computational modeling and simulation projects. This study also contributes to evidence-based scaffolding strategies that can support undergraduate students' engagement in the context of computational modeling and simulation. 

Small-scale robots have great potential in medicine, micro-assembly and many other areas. For example, robots containing iron can be steered using the magnetic gradient generated by MRI scanners. Since the gradient is approximately the same everywhere inside the scanner, each robot receives the same input and therefore they all are subjected to the same force. A similar technique can be used with rotating magnetic fields. Each robot receives the same inputs, making motion planning challenging. This paper uses a Rapidly Exploring Random Tree (RRT) to plan paths that deliver multiple robots to goal positions by using obstacles to break the actuation symmetry.