June 23, 2013
June 23, 2013
June 26, 2013
Educational Research and Methods
23.107.1 - 23.107.14
A structural equation model correlating success in engineering with academic variables for community college transfer students AbstractBackgroundRecognizing the importance of increasing graduates in STEM fields, the National ScienceFoundation (NSF) has funded the Science Technology Engineering and Math (STEM) TalentExpansion Program (STEP). One initiative of the STEP program is the Student Enrollment andEngagement through Connections (SEEC) project. SEEC is a collaborative, connection-basedalliance between a large Midwestern University and in-state community colleges (CCs). Thepurpose is to increase success of community college transfers to engineering. In this study, thedata provides continuous academic information from both the community college and theuniversity.PurposeThis is an exploratory study investigating the influences on completion of a BS degree inengineering for community college transfer students. The objective of the study is to create astructural equation model (SEM) using academic variables that shows the covariance structurebased on hypothesized relationships between the academic variables and graduation.Design/MethodThe structural equation model is created with Analysis of Moments Structures (AMOS) softwareusing academic variables from both the sending and the receiving institutions. The academicvariables consist of a student’s combined transcript-level data for core course requirements inengineering. The model provides a simultaneous analysis of relationships among the academicvariables and provides strength of relationship indicators. The data set includes 472 in-statecommunity college transfer students who were admitted to the College of Engineering betweenthe years 2002 and 2005. Data is imputed using multiple imputations. Model worthiness isdetermined by root mean square error (RMSE), comparative fit index and the ratio of the chisquared statistic to the degrees of freedom.ResultsThe model, estimated by maximum likelihood, demonstrates a reasonably good fit with the data(χ2=74.254, df=30, p<0.0001) and very good index metrics (RMSE = 0.056, Comparative FitIndex=0.984, chi squared ratio= 2.475).The following academic variables have significant correlations with graduation in engineering: First spring grade point average (GPA) at the receiving institution Number of first spring credit hours at the receiving institution Number of transfer credits toward core engineering courses from the sending institution Number of first fall credit hours after transfer to the receiving institution First fall GPA at the receiving institution University core engineering courses GPAIt is estimated that the predictors of graduation in engineering explain 34.8 percent of itsvariance.ConclusionsThe results of this study emphasis the importance of the core courses on success in engineering,whether the core courses are taken at the community college or the university. This researchmay help increase the success of community college transfers to engineering throughunderstanding of core course relationships and graduating in engineering.
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