Use of Big Data to Model Performance and Change

Collaborative scholarship in assessment, evaluation, and knowledge mobilization

Use of Big Data to Model Performance and Change

Working with colleagues in admissions, undergraduate medicine, post-graduate medicine, and dentistry we have developed a comprehensive data system that tracks students from time of application to graduation, and for some, to post-graduate training. Using this database, we have conducted a series of studies that have examined cohort effects in licensing exams, established performance risk indicators, and attempted to identify students at risk early on to enhance the learning experience.

Lead Investigator: Dr. Saad Chahine

Select Publications

Chahine, S., Plouffe, R., Goldberg, H., Sadler, K., Drosdowech, N., Bohay, R., Garcia, B., & Hammond R. (2019). Do factors from admissions and dental school predict performance on national board exams? Journal of Dental Education, Online First,

Chahine, S., Kulasegaram, K., Wright, S., Monterio, S., Grierson, L.E.M., Barber, C., Sebok-Syer, S.S., McConnell, M., Yen, W., De Champlian, A., & Touchie, C. (2018). A Call to Investigate the Relationship Between Education and Health Outcomes Using Big Data. Academic Medicine, 93(6), 829-832.

Plouffe, R.A., Hammond`, R., Goldberg, H.A., & Chahine, S. (2018). What matters from admissions? Identifying success and risk among Canadian dentistry students. Journal of Dental Education, 82(5), 515-523.

Barber, C., Hammond, R., Gula, L., Tithecott, G., & Chahine, S. (2018). In search of black swans: Identifying student risk of failing licensing examinations.  Academic Medicine, 93(3), 478-485.

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