June 23, 2013
June 23, 2013
June 26, 2013
Educational Research and Methods
23.1187.1 - 23.1187.14
A Hazards Model Study of Pathway Analysis in EngineeringFactors that indicate, explain, or predict if a student will persist or exit an engineering degreehave been a subject of a lot of research in engineering education. Findings from these studiesidentify factors that lead to success or barriers that lead premature exit from an engineeringdegree; however, they often focus on students who matriculate into engineering or analyzestudents once they have matriculated into engineering. We propose studying an alternatepathway, students who switch into engineering from other majors. Examining alternate pathwaysmay yield a fuller picture of the ways into and through engineering degrees and may beleveraged through different institutional policies and programs for attracting engineering studentsfrom other fields.Survival analysis is a longitudinal statistical method used to model the hazard or risk of an eventoccurring for some population. Our study implemented discrete survival analysis and a subset ofa database comprising more than 1,000,000 unique students. For our current research, we use asample population of first-time in college (FTIC) students initially matriculating into non-engineering disciplines in two years with population of ~55,000 at nine institutions. The event ofinterest is switching into engineering, and time is measured by terms. To better understand thedynamics of “attraction” into engineering we also run similar analyses with Science, Technologyand Math (as a similar comparator) and Social Science (as a dissimilar comparator). Survivalanalysis results allow us to graph the term by term hazard or risk of attraction into engineering(and the comparators) as well as the “survival” rate in the pool of individuals who have notexperienced the event, providing us insights into the relative attraction rates of engineeringcontrasted with other disciplines.Our preliminary results show that the attraction (hazard) rates for engineering are lower thanboth STM and social science attraction rates; furthermore, the pool of students who abstain fromswitching is greatest for engineering, and significantly less for STM and social science. Thusengineering has the lowest attraction rates and the highest abstention (which would be viewed asretention from their current department) rates. Interestingly, the hazard rate displays a similarpattern for all three groups, peaking at semester four and dropping markedly after semester six.In the full study, we also plan to examine if attraction and abstention rates differ by gender andethnicity across engineering and the comparators. These findings agree with other studies usingthe same database, which gives confidence in the model. The unique contribution of this workwill be findings regarding the switching population that yield insight into those students andrelated insights regarding the students who matriculate in engineering.
Schimpf, C. T., & Ricco, G. D., & Ohland, M. W. (2013, June), The Dynamics of Attracting Switchers: A Cross-Disciplinary Comparison Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/22572
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