| dc.identifier.uri | http://hdl.handle.net/11401/75996 | |
| 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 | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.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. | |
| dcterms.available | 2017-09-18T23:49:45Z | |
| dcterms.contributor | Finch, Stephen J. | en_US |
| dcterms.contributor | Zhu, Wei | en_US |
| dcterms.contributor | Wu, Song | en_US |
| dcterms.contributor | Nehm, Ross. | en_US |
| dcterms.creator | Lee, Un Jung | |
| dcterms.dateAccepted | 2017-09-18T23:49:45Z | |
| dcterms.dateSubmitted | 2017-09-18T23:49:45Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 90 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/75996 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-18T23:49:45Z (GMT). No. of bitstreams: 1
Lee_grad.sunysb_0771E_12407.pdf: 1451900 bytes, checksum: cc5e03b032922affd3fb6aee71775af2 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Statistics | |
| dcterms.subject | Longitudinal data, Student response system, Trajectory analysis | |
| dcterms.title | The application of trajectory analysis for an early warning system in STEM courses | |
| dcterms.type | Dissertation | |
