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Is Modeling of Freshman Engineering Success Different from Modeling of Non‐Engineering Success?

dc.contributor.authorVeenstra, Cindy P.en_US
dc.contributor.authorDey, Eric L.en_US
dc.contributor.authorHerrin, Gary D.en_US
dc.date.accessioned2013-01-03T19:44:50Z
dc.date.available2013-01-03T19:44:50Z
dc.date.issued2008-10en_US
dc.identifier.citationVeenstra, Cindy P.; Dey, Eric L.; Herrin, Gary D. (2008). "Is Modeling of Freshman Engineering Success Different from Modeling of Non‐Engineering Success?." Journal of Engineering Education 97(4). <http://hdl.handle.net/2027.42/95487>en_US
dc.identifier.issn1069-4730en_US
dc.identifier.issn2168-9830en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/95487
dc.description.abstractThe engineering community has recognized the need for a higher retention rate in freshman engineering. If we are to increase the freshman retention rate, we need to better understand the characteristics of academic success for engineering students. One approach is to compare academic performance of engineering students to that of non‐engineering students. This study explores the differences in predicting academic success (defined as the first year GPA) for freshman engineering students compared to three non‐engineering student sectors (Pre‐Med, STEM, and non‐STEM disciplines) within a university. Academic success is predicted with pre‐college variables from the UCLA/CIRP survey using factor analysis and regression analysis. Except for the factor related to the high school GPA and rank, the predictors for each student sector were discipline specific. Predictors unique to the engineering sector included the factors related to quantitative skills (ACT Math and Science test scores and placement test scores) and confidence in quantitative skills.en_US
dc.publisherWiley Periodicals, Inc.en_US
dc.publisherBlackwell Publishing Ltden_US
dc.subject.otherCIRP Surveyen_US
dc.subject.otherPre‐College Characteristicsen_US
dc.subject.otherFreshman Engineering Successen_US
dc.titleIs Modeling of Freshman Engineering Success Different from Modeling of Non‐Engineering Success?en_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelEngineering Educationen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Industrial and Operations Engineering University of Michiganen_US
dc.contributor.affiliationumDepartment of Industrial and Operations Engineering University of Michiganen_US
dc.contributor.affiliationumSchool of Education University of Michiganen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/95487/1/j.2168-9830.2008.tb00993.x.pdf
dc.identifier.doi10.1002/j.2168-9830.2008.tb00993.xen_US
dc.identifier.sourceJournal of Engineering Educationen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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