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The landscape of contemporary engineering education is ever changing, adapting and evolving. Finite element theory and application has often been the focus of graduate-level courses in engineering programs; however, industry needs more bachelor's-level engineering graduates to have skills in applying this essential analysis and design technique. Today’s globally competitive world requires fast redesigns of products/ processes that are well-suited to using finite element analysis to reduce the design cycle. We have used the Kolb Learning Cycle as a conceptual framework to improve student learning of difficult engineering concepts, and to gain essential knowledge of finite element analysis (FEA) and design content knowledge. A team of researchers, with a National Science Foundation grant for the past five years, have created and made improvements to seventeen active learning FEA modules which were originally developed using MSC Nastran, following the development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages. We summarize the incremental improvements of these learning modules during the past five years as we implemented them into undergraduate courses that covered topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, and chip formation during manufacturing, and large scale deformation in machining. This paper summarizes five years of incremental improvements to the modules comparing the student performance on preand post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts addressed in each module. The researchers made significant changes to their finite element learning modules annually to improve student understanding of these difficult engineering concepts in their classes. Statistically, significant student performance gains provide evidence of module effectiveness by gender and ethnic groups was found to be minimal. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each subgroup of students has made on quiz performance. Although exploratory, and generally based on small sample sizes in our five-year formative evaluation process, the modules for which subgroup differences were carefully reviewed and some instances re-administered in a different settings in an attempt to improve student performance across specific personality and /or learning styles subgroups (e.g. MBTI Intuitive versus Sensing; ILS Sequential versus Global).

Active Engineering Education Modules: Summary Paper of Five Years of Incremental Improvements to the Modules