Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/31929
Title: Measuring financial implications of an early alert system
Contributor(s): Harrison, Scott  (author); Villano, Renato  (author)orcid ; Lynch, Grace  (author); Chen, George  (author)orcid 
Publication Date: 2016-04-25
DOI: 10.1145/2883851.2883923
Handle Link: https://hdl.handle.net/1959.11/31929
Abstract: The prevalence of early alert systems (EAS) at tertiary institutions is increasing. These systems are designed to assist with targeted student support in order to improve student retention. They also require considerable human and capital resources to implement, with significant costs involved. It is therefore an imperative that the systems can demonstrate quantifiable financial benefits to the institution. The purpose of this paper is to report on the financial implications of implementing an EAS at an Australian university as a case study. The case study institution implemented an EAS in 2011 using data generated from a data warehouse. The data set is comprised of 16,124 students enrolled between 2011 and 2013. Using a treatment effects approach, the study found that the cost of a student discontinuing was on average $4,687. Students identified by the EAS remained enrolled for longer, with the institution benefiting with approximately an additional $4,004 in revenue per student over the length of enrolment. All schools had a significant positive effect associated with the EAS and the EAS showed significant value to the institution regardless of the timing when the student was identified. The results indicate that EAS had significant financial benefits to this institution and that the benefits extended to the entire institution beyond the first year of enrolment.
Publication Type: Conference Publication
Conference Details: LAK 2016: 6th International Conference on Learning Analytics and Knowledge, Edinburgh, United Kingdom, 25th - 29th April, 2016
Source of Publication: LAK '16 Conference Proceedings: The Sixth International Learning Analytics & Knowledge Conference, p. 241-248
Publisher: Association for Computing Machinery (ACM)
Place of Publication: New York, United States of America
Fields of Research (FoR) 2020: 380104 Economics of education
Socio-Economic Objective (SEO) 2020: 160304 Teaching and instruction technologies
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Humanities, Arts and Social Sciences
UNE Business School

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