Journal Article: Insights of Instructors and Advisors into an Early Prediction Model for Non-Thriving Students

Citation:

Hershkovitz, Arnon, and Alex Ambrose. 2022. “Insights of Instructors and Advisors into an Early Prediction Model for Non-Thriving Students.” Journal of Learning Analytics, 1-16. https://doi.org/10.18608/jla.2022.7509.

Abstract:

In this qualitative study (N=6), we explored insights of first-year students’ instructors and advisors into an early identification system aimed at detecting non-thriving students in the context of an all-campus first-year orientation course for undergraduates. Following the development of that prediction model in a bottom-up manner, using a plethora of available data, we focus on how its end-users could help us understand the underlying mechanisms that drive the identification of non-thriving students. As findings suggest, participants were appreciative overall of the prediction and its timing and came up with various behaviours that could explain non-thriving, mostly motivation and engagement. They suggested additional data that could predict non-thriving, including background information, academic engagement, and learning habits.

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