Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics.

ABSTRACT: Student attrition poses a major challenge to academic institutions, funding bodies and students. With the rise of Big Data and predictive analytics, a growing body of work in higher education research has demonstrated the feasibility of predicting student dropout from readily available macro-level (e.g., socio-demographics or early performance metrics) and micro-level data (e.g., logins to learning management systems). Yet, the existing work has largely overlooked a critical meso-level element of student success known to drive retention: students' experience at university and their social embeddedness within their cohort. In partnership with a mobile application that facilitates communication between students and universities, we collected both (1) institutional macro-level data and (2) be

SUBMITTER: Matz SC 

PROVIDER: S-EPMC10082180 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature