2013 ASEE Annual Conference, Atlanta, Georgia.

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A Hazards Model Study of Pathway Analysis in Engineering Factors that indicate, explain, or predict if a student will persist or exit an engineering degree have been a subject of a lot of research in engineering education. Findings from these studies identify factors that lead to success or barriers that lead premature exit from an engineering degree; however, they often focus on students who matriculate into engineering or analyze students once they have matriculated into engineering. We propose studying an alternate pathway, students who switch into engineering from other majors. Examining alternate pathways may yield a fuller picture of the ways into and through engineering degrees and may be leveraged through different institutional policies and programs for attracting engineering students from other fields.Survival analysis is a longitudinal statistical method used to model the hazard or risk of an event occurring for some population. Our study implemented discrete survival analysis and a subset of a database comprising more than 1,000,000 unique students. For our current research, we use a sample 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 of interest is switching into engineering, and time is measured by terms. To better understand the dynamics of “attraction” into engineering we also run similar analyses with Science, Technology and Math (as a similar comparator) and Social Science (as a dissimilar comparator). Survival analysis 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 not experienced the event, providing us insights into the relative attraction rates of engineering contrasted with other disciplines.Our preliminary results show that the attraction (hazard) rates for engineering are lower than both STM and social science attraction rates; furthermore, the pool of students who abstain from switching is greatest for engineering, and significantly less for STM and social science. Thus engineering has the lowest attraction rates and the highest abstention (which would be viewed as retention from their current department) rates. Interestingly, the hazard rate displays a similar pattern 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 and ethnicity across engineering and the comparators. These findings agree with other studies using the same database, which gives confidence in the model. The unique contribution of this work will be findings regarding the switching population that yield insight into those students and related insights regarding the students who matriculate in engineering.


2013, ASEE, cross-disciplinary

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