Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/54024
Title: Microeconometric Analysis of the Relationships Between Early Alert Sytems and Student Retention
Contributor(s): Harrison, Scott  (author); Villano, Renato  (supervisor)orcid ; Lynch, Grace  (supervisor); Chen, George  (supervisor)orcid 
Conferred Date: 2016-10-21
Copyright Date: 2015-11
Thesis Restriction Date until: 2017-10-22
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/54024
Abstract: 

The main objective of this study is to evaluate the relationship between Early Alert Systems (EAS) and student retention. Specifically, the study aims to: (i) examine the effects of demographic, institutional and learning environment variables on student retention, (ii) examine the effects of EAS on student retention, and (iii) assess the financial implications of the interaction between EAS and student retention. Selected microeconometric models were estimated using data for 16,124 undergraduate students extracted from a case study university. The data was captured over three years between 2011 and the beginning of 2014.

Key findings of this study show that demographic, institution, student performance and workload variables all exhibit statistically significant relationships with retention measures at the case study institution. Furthermore, the EAS had a positive effect on increasing students’ length of enrolment. Females are more likely to discontinue, but are also more likely to complete their course. Aboriginal and Torres Strait Islander (ATSI) students are more likely to be retained than non-ATSI students. Institutional factors such as the type of course, the school a student enrols in, or mode of enrolment all affect student’s retention rate. Variables capturing student performance and workload further affect retention. Periods of inactivity during students’ enrolment was one of the strongest factors affecting measures of student retention. The study also finds that demographic, institution, learning environment and EAS variables are subject to significant temporal effects. Using weekly observations, temporal effects were captured up to 156 weeks (3 years) of student enrolment, yielding a total of 1,119,170 observations. Using survival modelling, the study provides an unprecedented degree of accuracy in estimating the relationship between explanatory variables and the hazard of discontinuing over time.

Finally, the financial implications of the EAS was evaluated using treatment effects modelling. On average, students identified by the EAS for targeted support remained enrolled for an extra 14 weeks than students not identified by the EAS. The additional revenue in tuition fees caused by EAS identification is estimated to be $4,004 per student. It is concluded that early alert systems have significant financial benefits, initiating support services that positively impact on student outcomes.

Publication Type: Thesis Doctoral
Fields of Research (FoR) 2008: 140208 Health Economics
140301 Cross-Sectional Analysis
160510 Public Policy
Fields of Research (FoR) 2020: 380108 Health economics
380201 Cross-sectional analysis
440709 Public policy
Socio-Economic Objective (SEO) 2008: 910205 Industry Policy
920204 Evaluation of Health Outcomes
910405 Public Sector Productivity
Socio-Economic Objective (SEO) 2020: 150505 Industry policy
200202 Evaluation of health outcomes
150305 Public sector productivity
HERDC Category Description: T2 Thesis - Doctorate by Research
Appears in Collections:Thesis Doctoral
UNE Business School

Files in This Item:
6 files
File Description SizeFormat 
openpublished/HarrisonScottPhD2016Thesis.pdfPublished version10.07 MBAdobe PDF
Download Adobe
View/Open
Show full item record

Page view(s)

956
checked on May 19, 2024

Download(s)

92
checked on May 19, 2024
Google Media

Google ScholarTM

Check


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.