Using predictive risk modelling to identify students at high risk of paper non-completion and programme non-retention at university
| aut.embargo | No | en_NZ |
| aut.thirdpc.contains | No | en_NZ |
| aut.thirdpc.permission | No | en_NZ |
| aut.thirdpc.removed | No | en_NZ |
| dc.contributor.advisor | Maloney, Tim | |
| dc.contributor.author | Jia, Pengfei | |
| dc.date.accessioned | 2014-05-16T03:26:57Z | |
| dc.date.available | 2014-05-16T03:26:57Z | |
| dc.date.copyright | 2014 | |
| dc.date.created | 2014 | |
| dc.date.issued | 2014 | |
| dc.date.updated | 2014-05-16T03:10:16Z | |
| dc.description.abstract | Course non-completion is of substantial concern to university and public funding bodies as it could potentially affect attrition rates and eventual educational performance. This paper seeks to empirically estimate the factors that affect paper non-completion and programme non-retention. More importantly, identifying students who are at high risk of course non-completion would provide opportunities for possible early intervention services. This study develops a predictive risk model (PRM) to estimate the likelihood of course non-completion among first-year students at a large public university in New Zealand. The main aim of this research is to explore the potential use of administrative data for targeting prevention and early interventions to university students. Our results suggest that many factors, including part-time study, ethnicity, gender, educational background, and programme study areas, could play a prominent role in predicting a student’s risk of paper non-completion in the first year and non-retention in the second year at university. We assess the “target effectiveness” of our model from a number of perspectives. For example, the area under the ROC curves for paper non-completion and programme non-retention are 0.7553 and 0.7125, respectively. Students with the highest 10% of risk scores by our PRM would account for 29.25% of paper non-completions and 23.33% of programme non-retentions. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/7187 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Predictive risk modelling | en_NZ |
| dc.subject | Paper non-completion | en_NZ |
| dc.subject | Programme non-retention | en_NZ |
| dc.subject | University dropout | en_NZ |
| dc.title | Using predictive risk modelling to identify students at high risk of paper non-completion and programme non-retention at university | en_NZ |
| dc.type | Dissertation | |
| thesis.degree.discipline | ||
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Masters Dissertations | |
| thesis.degree.name | Master of Business | en_NZ |