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

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.

Early Warning Model and Its Application Research on the Alienation of Online Consumption of Higher Vocational Students

“…With limited financial resources and a heightened desire for social acceptance, they are particularly susceptible to consumption alienation (Mindel et al, 2022). [8]The allure of easy credit and online loans further exacerbates this vulnerability, as highlighted by Salmela-Aro et al, (2017), who noted a significant correlation between online loan availability and consumption alienation among vocational students. [9]…”

Analysing correlation between sequential event in student’s learning path

“…Furthermore, in some learning analyticsrelated research, teaching techniques such as constructivist learning, were proposed to aid university students in the acquisition of generic skills [3], while others analyzed factors that help students navigate digital learning activities using LMS (Learning Management System) tools [4] or CMLS [5]. There are other computational approaches related to learning analytics as noted in [6][7], they may be curriculum-related [8][9], centered on competency analysis [10] [11], involve the creation of an adaptive tutoring system [12], or may require sorting out the data of students having difficulties in their studies [13]. Another area specifically searches for causal relationships between temporal occurrences using time series as data streams [14][15] [16].…”