Assessment of active learning modules: An update of research findings

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 inengineering programs; however, industry needs bachelor\u27s-level engineering graduates to haveskills in applying this essential analysis and design technique. We have used the Kolb LearningCycle 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.Originally developed using MSC Nastran, followed by development efforts in SolidWorksSimulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team ofresearchers, with National Science Foundation support, have created over twenty-eight activelearning modules. We will discuss the implementation of these learning modules which havebeen incorporated into undergraduate courses that cover topics such as machine design,mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis,structural fatigue analysis, computational fluid dynamics, rocket design, chip formation duringmanufacturing, and large scale deformation in machining.This update on research findings includes statistical results for each module which compareperformance on pre- and post-learning module quizzes to gauge change in student knowledgerelated to the difficult engineering concepts that each module addresses. Statistically significantstudent performance gains provide evidence of module effectiveness. In addition, we presentstatistical comparisons between different personality types (based on Myers-Briggs TypeIndicator, MBTI subgroups) and different learning styles (based on Felder-Solomon ILSsubgroups) in regards to the average gains each subgroup of students has made on quizperformance. Although exploratory, and generally based on small sample sizes at this point inour multi-year formative evaluation process, the modules for which subgroup differences arefound are being carefully reviewed in an attempt to determine whether modifications should bemade to better ensure equitable impact of the module across students from specific personalityand /or learning styles subgroups (e.g. MBTI Intuitive versus Sensing; ILS Sequential versusGlobal)