Active learning modules assessments: An update of results by gender and ethnic groups

Authors

Ashland O. Brown, University of the PacificFollow
Daniel D. Jensen, U.S. Air Force Academy
Richard H. Crawford, University of Texas, Austin
Joseph J. Rencis, Tennessee Technological University
Ella R. Sargent, University of the Pacific
Brock U. Dunlap, University of Texas, Austin
Rachelle K. Hackett, University of the PacificFollow
Kathy Schmidt Jackson, Pennsylvania State University
Kyle A. Watson, University of the PacificFollow
Ismail I. Orabi, University of New Haven
Jiancheng Liu, University of the PacificFollow
John J. Wood, U.S. Air Force Academy
Christopher A. Wejmar, University of the Pacific
Paul H. Schimpf, Eastern Washington University
Chuan-Chiang Chen, California State Polytechnic University

Document Type

Conference Presentation

Conference Title

Annual Meeting of the American Society of Engineering Education

Organization

American Society of Engineering Education

Location

Indianapolis, IN

Date of Presentation

6-15-2014

Abstract

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's-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 by gender and ethnicgroups. In addition, we present statistical comparisons between different personality types(based on Myers-Briggs Type Indicator, MBTI subgroups) and different learning styles (basedon Felder-Solomon ILS subgroups) in regards to the average gains each subgroup of students hasmade on quiz performance. Although exploratory, and generally based on small sample sizes atthis point in our multi-year formative evaluation process, the modules for which subgroupdifferences are found are being carefully reviewed in an attempt to determine whethermodifications should be made to better ensure equitable impact of the module across studentsfrom specific personality and /or learning styles subgroups (e.g. MBTI Intuitive versus Sensing;ILS Sequential versus Global).

Comments

© 2012 American Society for Engineering Education

Recommended Citation

Brown, Ashland O.; Jensen, Daniel D.; Crawford, Richard H.; Rencis, Joseph J.; Sargent, Ella R.; Dunlap, Brock U.; Hackett, Rachelle K.; Jackson, Kathy Schmidt; Watson, Kyle A.; Orabi, Ismail I.; Liu, Jiancheng; Wood, John J.; Wejmar, Christopher A.; Schimpf, Paul H.; and Chen, Chuan-Chiang, "Active learning modules assessments: An update of results by gender and ethnic groups" (2014). Benerd College Faculty Presentations. 213.
https://scholarlycommons.pacific.edu/ed-facpres/213