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Online Graduate Teacher Education: Establishing an EKG for Student Success Intervention
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, , Boise State University, United States

Society for Information Technology & Teacher Education International Conference, in Las Vegas, NV, United States ISBN 978-1-939797-13-1 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA

Abstract

Predicting which students enrolled in graduate online education are at-risk for failure is an arduous yet important task for teachers and administrators alike. This research reports on a statistical analysis technique using both static and dynamic variables to determine which students are at-risk and when an intervention could be most helpful during a semester. Time-series analysis of online teacher education classes revealed that prediction is possible after the 10th week capturing over 78% of at-risk students. Visual analysis of dynamic student activities shares a number of striking commonalities consistent with EKG charting. Next phases of research will apply further validation of both the models attempted and additional predictor variables.

Citation

Shelton, B. & Hung, J.l. (2015). Online Graduate Teacher Education: Establishing an EKG for Student Success Intervention. In D. Rutledge & D. Slykhuis (Eds.), Proceedings of SITE 2015--Society for Information Technology & Teacher Education International Conference (pp. 1045-1050). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved June 18, 2019 from .

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