Finite element active learning module assessment of student improvement, learning styles, gender differences and ethnic differences

The landscape of contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has often been included in graduate-level courses in engineering programs; however, current industry needs bachelor\u27s-level engineering graduates with skills in applying this essential analysis and design technique. Engineering education is also changing to include more active learning. In response to the need to introduce undergrads to the finite element method as well as the need for engineering curricula to include more active learning, we have developed, implemented and assessed a suite of Active Learning Module (ALMs).The ALMs are designed to improve student learning of difficult engineering concepts while students gain essential knowledge of finite element analysis. As such, the ALMs guide the students through a process using commercial finite element software to solve a problem (or set of problems) in a particular area of engineering. Some overview of finite element theory is included. The active learning occurs when the students interact with the physics of the problem by developing and perturbing the model of the problem. Additional active learning is sometimes included through collaborative exercises using the ALM. We have used the Kolb Learning Cycle as a conceptual framework to guide our design of the ALMs. Originally developed using MSC Nastran, followed by development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team of researchers, with National Science Foundation support, have created over twenty-eight active learning modules. We will discuss the implementation of these learning modules which have been 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 during manufacturing, and large scale deformation in machining. This update on research findings includes statistical results for each module which compare performance on pre- and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts that each module addresses. Statistically significant student performance gains provide evidence of module effectiveness. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI, subgroups), different learning styles (based on Felder-Solomon ILS subgroups), and gender and ethnicity in regards to the average gains each group of students have made on quiz performance. Although exploratory, and generally based on small sample sizes at this point in our multi-year effort, the modules for which subgroup differences are found are being carefully reviewed in an attempt to determine whether modifications should be made to better ensure equitable impact of the modules across students from specific personality and/or learning styles subgroups (e.g., MBTI Intuitive versus Sensing; ILS Sequential versus Global). © American Society for Engineering Education, 2014