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

Arnon Hershkovitz*, G. Alex Ambrose

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)202-217
Number of pages16
JournalJournal of Learning Analytics
Issue number2
StatePublished - 31 Aug 2022


FundersFunder number
Schlindwein Family Tel Aviv University


    • Early warning system
    • advisor perceptions
    • data-driven decision-making
    • early identification system
    • instructor perceptions
    • non-thriving
    • prediction model


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