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Conference Proceeding

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To meet national workforce need, we integrated computational modeling training into undergraduate materials science and engineering (MSE) courses, including Thermodynamics, and Structure of Materials. We also flipped the courses, requiring students to self-study topics outside the class. In the class, the instructors focused on demonstrating real-world materials problems and guiding the students to solve the problems using different computational modeling techniques. Learning the computational modeling concepts within a short period of time was challenging to the students. Another challenge was that the students had various STEM backgrounds, such as MSE, mechanical engineering, and physics. In order to foster student learning, engage student interest and seamlessly couple computational modeling modules with the courses, real-world problems, examples and homework were all developed based on student background and interest. For each lecture, 90% of the time was arranged for quiz, problem solving, and hands-on training. The students could improve their understanding of computational modeling concepts through practice. This paper presents the above teaching strategies and demonstrates one computational module used for teaching the students how to estimate point defect formation energy with a computational modeling tool. The student feedback suggests that integrating computational modeling training into undergraduate curriculum is feasible. However, it posts some challenges, such as teaching hours and teaching balance between computational modeling theory and practice, for the students and instructor. The paper also discusses the improvement plan.

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© (2016), American Society for Engineering Education, Proceedings of ASEE Annual Conference (New Orleans, LA).