Open AccessMicroeconometric Approaches in Exploring the Relationships Between Early Alert Systems and Student Retention: A Case Study of a Regionally Based University in Australia
Author(s) -
Scott H. Harrison,
Renato Villano,
Grace Lynch,
George Chen
Publication year - 2021
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2021.7087
Subject(s) - retention rate , knowledge retention , confounding , learning analytics , hazard , psychology , mathematics education , computer science , medical education , data science , medicine , statistics , mathematics , chemistry , computer security , organic chemistry
Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for numerous confounding variables, significant relationships between the EAS and student retention were identified. Capturing dynamic relationships between the explanatory variables and the hazard of discontinuing provides new insight into understanding student retention factors. We concluded that survival models are the best methods of understanding student retention when temporal data is available.