Assessment of active learning modules: An update of research findings

Authors

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

Document Type

Conference Presentation

Conference Title

Annual Meeting of the American Society of Engineering Education

Organization

American Society of Engineering Education

Location

Atlanta, GA

Date of Presentation

6-23-2013

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. In addition, we presentstatistical comparisons between different personality types (based on Myers-Briggs TypeIndicator, MBTI, subgroups) and different learning styles (based on the 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).

Comments

© 2012 American Society for Engineering Education

Recommended Citation

Brown, Ashland O.; Crawford, Richard H.; Jensen, David D.; Rencis, Joseph J.; Liu, Jiancheng; Watson, Kyle A.; Jackson, Kathy Schmidt; Hackett, Rachelle K.; Schimpf, Paul H.; Chen, Chuan-Chiang; Orabi, Ismail I.; Akasheh, Firas; Wood, John J.; Dunlap, Brock U.; and Sargent, Ella R., "Assessment of active learning modules: An update of research findings" (2013). Benerd College Faculty Presentations. 205.
https://scholarlycommons.pacific.edu/ed-facpres/205