The data revolution comes to higher education
Identifying students at risk of dropout in Chile
Bibliographic Data
| ID | 4545651 |
|---|---|
| Authors | Paul T Von Hippel (0000-0003-4498-4374, LBJ School of Public Affairs, University of Texas, Austin, Texas, USA), A Hofflinger (0000-0002-1231-9911, Universidad de La Frontera, corresponding author) |
| Year | 2021 |
| Volume | 43 |
| Issue | 1 |
| Pages | 2-23 |
| Publication date | 2021-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Higher Education Policy and Management (JOURNAL) |
| Journal identifiers | ISSN: 1360-080X • E-ISSN: 1469-9508 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/1360080x.2020.1739800 |
| PMID | 38009105 |
| OpenAlex | W3013620967 |
| Language | EN |
| Citations received | 20 |
| References cited | 18 |
Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results show that data available at matriculation are only weakly predictive of persistence, while prediction improves dramatically once data on university grades become available. Some predictors of persistence are under policy control. Financial aid predicts higher persistence, and being denied a first-choice major predicts lower persistence. Student success programmes are ineffective at some universities; they are more effective at others, but when effective they often fail to target the highest risk students. Universities should use data regularly and systematically to identify high-risk students, target them with interventions, and evaluate those interventions' effectiveness
Dropout (neural networks · Economic growth · Economics · Graduation (instrument · Higher education · Latin Americans · Mathematics education · Matriculation · Medical education · Persistence (discontinuity · Political science · Psychological intervention · Computer Science · Educational Outcomes and Influences · Engineering · Higher Education Research Studies · Medicine · Online Learning and Analytics · Psychology
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| Unique citing works | 20 |
|---|---|
| Citations per year | 4 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 20 |