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dc.contributor.advisorBarger, Rita
dc.contributor.authorMorgan, William Parker
dc.date.issued2020
dc.date.submitted2020 Fall
dc.descriptionTitle from PDF of title page viewed March 2, 2021
dc.descriptionDissertation advisor: Rita Barger
dc.descriptionVita
dc.descriptionIncludes bibliographical references (page 173-186)
dc.descriptionThesis (Ph.D.)--School of Education and Department of Mathematics and Statistics. University of Missouri--Kansas City, 2020
dc.description.abstractAccurate placement into an initial college mathematics course is a key step toward the successful completion of college mathematics and, eventually, a college degree. Conversely, misplacement in mathematics may lead to a reduced likelihood of course completion and degree attainment. This study investigated the ability of two placement models to predict course success in mathematics: a hierarchical placement model utilizing cut point values determined by CHAID decision trees and an algorithmic placement model utilizing the regression equations from binary logistic regression analysis. Both models used the placement measures of high school grade point average (HSGPA) and ACT-Mathematics (ACTM) scores to predict the placement of students from a large, Midwest urban/suburban community college. The accuracy of the two placement models was compared to determine which, if either, placement model more accurately placed students in their initial college mathematics course. The ability of each placement model to discern between a successful student and an unsuccessful student was also compared. The analysis showed that the two placement models perform equally well in predicting course success and in discerning between a successful and unsuccessful student. The algorithmic model tended to underplace students by placing substantially more students in the lowest mathematics course level than the hierarchical placement model with only minimal improvement in predicting success. These results, along with the likelihood that the placement rules of the hierarchical model would be more easily understood by students and advisors, make the hierarchical model more useful for college mathematics placement.
dc.description.tableofcontentsIntroduction -- Literature review -- Methodology -- Results and Analysis -- Conclusion -- Appendix A. The North Carolina Community College System’s (NCCCS) Math Benchmark Courses Eligible for Multiple Measures Placement (as of 2016) -- Appendix B. Histograms and Normal Q-Q Plots for HSGPA and ACTM Data -- Appendix C. CHAID Decision Tree Model Outputs from SPSS
dc.identifier.urihttps://hdl.handle.net/10355/80787
dc.subject.lcshEducational tests and measurements -- Mathematical models
dc.subject.lcshEducational tests and measurements ǂx Mathematics
dc.subject.lcshPrediction of scholastic success -- Mathematics
dc.subject.lcshMathematics -- Study and teaching
dc.subject.lcshCollege entrance achievement tests -- Mathematics
dc.subject.otherDissertation -- University of Missouri--Kansas City -- Education
dc.subject.otherDissertation -- University of Missouri--Kansas City -- Mathematics
dc.titleComparing Multiple Measure Placement Models in Mathematics
thesis.degree.disciplineCurriculum and Instruction (UMKC)
thesis.degree.disciplineMathematics (UMKC)
thesis.degree.grantorUniversity of Missouri--Kansas City
thesis.degree.levelDoctoral
thesis.degree.namePh.D. (Doctor of Philosophy)


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