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The application of trajectory analysis for an early warning system in STEM courses

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dc.contributor.advisor Finch, Stephen J. en_US
dc.contributor.advisor Wu, Song en_US
dc.contributor.author Lee, Un Jung en_US
dc.contributor.other Department of Applied Mathematics and Statistics. en_US
dc.date.accessioned 2017-09-20T16:42:20Z
dc.date.available 2017-09-20T16:42:20Z
dc.date.issued 2015-12-01
dc.identifier.uri http://hdl.handle.net/11401/76095 en_US
dc.description 90 pg. en_US
dc.description.abstract The retention of STEM (science, technology, engineering, and mathematics) majors has become a national concern. “Early warning systems†(EWS) are being developed to identify students who perform poorly early in the semester so that interventions can be implemented. The research reported here utilizes clicker scores and review quiz scores collected in every class session for the longitudinal analysis, as well as pre-course concept inventory scores and self-reported student characteristics. Pre course concept inventory scores were significantly predictive of final course grade. Student demographic characteristics had a smaller fraction of final course grade explained. The cumulative average student clicker score was highly predictive of final course grade. The cumulative average student review quiz score was also highly predictive of final course grade in spring 2014 semester, but was less predictive and less correlated with final course grade in the fall 2014 semester. The trajectories of transformed clicker and review quiz scores identified student longitudinal patterns of scores. Students with scores that were high at the beginning of the semester had consistently higher scores through the semester. In addition, the Bayesian Posterior Probabilities (BPPs) of clicker score trajectory were significant predictors of final course grade. In a trajectory analysis of ACF and PACF, the number of zero clicker scores was associated with final course grade. In conclusion, pre-course concept inventory scores and clicker scores were effective predictive variables for an EWS. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Statistics en_US
dc.subject.other Longitudinal data, Student response system, Trajectory analysis en_US
dc.title The application of trajectory analysis for an early warning system in STEM courses en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Zhu, Wei en_US
dc.contributor.committeemember Nehm, Ross. en_US

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