Microeconometric Approaches in Exploring the Relationships Between Early Alert Systems and Student Retention: A Case Study of a Regionally Based University in Australia
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.
Measuring financial implications of an early alert system
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.
Linking early alert systems and student retention: a survival analysis approach
Higher education institutions are increasingly seeking technological solutions to not only enhance the learning environment but also support students. In this study, we explored the case of an early alert system (EAS) at a regional university engaged in both on-campus and online teaching. Using a total of 16,142 observations captured between 2011 and 2013, we examined the relationship between EAS and the student retention rate. The results indicate that when controlling for demographic, institution, student performance and workload variables, the EAS is able to identify students who have a significantly higher risk of discontinuing from their studies. This implies that early intervention strategies are effective in addressing student retention, and thus an EAS is able to provide actionable information to the student support team.
Microeconometric Analysis of the Relationships Between Early Alert Sytems and Student Retention
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.