Online Graduate Teacher Education: Establishing an EKG for Student Success Intervention

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 clustering 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. The potential exists for instructors to recognize simple graphic patterns that identify and formatively address these issues with their students. Next phases of research will apply further validation of both the models attempted and additional predictor variables

Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

“…The longer a student has been in school, the more likely he or she is to complete a degree (Hart, 2012;Levy, 2007;Moore & Kearsley, 2005;Wang & Newlin, 2002). Several studies found grade point average (GPA) to be positively correlated with success in individual courses (Aragon & Johnson, 2008;Campbell, DeBlois, & Oblinger, 2007;Dupin-Bryant, 2004;Harrell & Bower, 2011;Hart, 2012;Jayaprakash, Moody, Laura, Regan, & Baron, 2014;Menager-Beeley, 2001;Morris, Wu, & Finnegan, 2005;Muse, 2003;Osborn, 2001;Shelton, Hung, & Baughman, 2015;Valasek, 2001). Some of these studies also found that both the verbal and mathematic scores on the SAT are strong predictors of academic success Cortes, 2013;Morris et al, 2005 Reason, 2003;Swail (2004) Blacks, Hispanics, Native Americans less likely to persist success on a more general level finding that academic performance in higher education mirrors that of previous academic experiences.…”

“…How the data is used, and what actions are taken, depends on the needs of the group, and their placement in the hierarchy of the educational process (Jayaprakash et al, 2014;Shelton et al, 2015). Learning analytics data are used in three areas: descriptive, predictive, and prescriptive analyses (Affendey, Paris, Mustapha, Sulaiman, & Muda, 2010;Brown, 2011).…”

“…Learning analytics study results can be useful to instructional designers when creating online courses (Lockyer et al, 2013). Department level staff can use data to inform personnel decisions including teaching assignments and training needs (Berger & Lyon, 2005;Dziuban et al, 2012;Shelton et al, 2015). University staff that provides supplemental student resources benefits from learning analytics results to refine the timing and location of various services (Becker, 2013;.…”

“…Learning analytics can encourage faculty members to take part in a self-reflection of their online teaching (Dringus, 2011). A self-reflection may encourage professional growth for faculty in the differences between face-to-face and online teaching and learning pedagogy (Shelton et al, 2015). Faculty members have the power to use learning analytics to guide students to success, affect practice, and contribute to the scholarship of teaching and learning .…”