Temporal and Between-Group Variability in College Dropout Prediction
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909071132590080 |
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| author | Glandorf, Dominik Lee, Hye Rin Orona, Gabe Avakian Pumptow, Marina Yu, Renzhe Fischer, Christian |
| author_facet | Glandorf, Dominik Lee, Hye Rin Orona, Gabe Avakian Pumptow, Marina Yu, Renzhe Fischer, Christian |
| contents | Large-scale administrative data is a common input in early warning systems for college dropout in higher education. Still, the terminology and methodology vary significantly across existing studies, and the implications of different modeling decisions are not fully understood. This study provides a systematic evaluation of contributing factors and predictive performance of machine learning models over time and across different student groups. Drawing on twelve years of administrative data at a large public university in the US, we find that dropout prediction at the end of the second year has a 20% higher AUC than at the time of enrollment in a Random Forest model. Also, most predictive factors at the time of enrollment, including demographics and high school performance, are quickly superseded in predictive importance by college performance and in later stages by enrollment behavior. Regarding variability across student groups, college GPA has more predictive value for students from traditionally disadvantaged backgrounds than their peers. These results can help researchers and administrators understand the comparative value of different data sources when building early warning systems and optimizing decisions under specific policy goals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06498 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Temporal and Between-Group Variability in College Dropout Prediction Glandorf, Dominik Lee, Hye Rin Orona, Gabe Avakian Pumptow, Marina Yu, Renzhe Fischer, Christian Computers and Society Machine Learning Large-scale administrative data is a common input in early warning systems for college dropout in higher education. Still, the terminology and methodology vary significantly across existing studies, and the implications of different modeling decisions are not fully understood. This study provides a systematic evaluation of contributing factors and predictive performance of machine learning models over time and across different student groups. Drawing on twelve years of administrative data at a large public university in the US, we find that dropout prediction at the end of the second year has a 20% higher AUC than at the time of enrollment in a Random Forest model. Also, most predictive factors at the time of enrollment, including demographics and high school performance, are quickly superseded in predictive importance by college performance and in later stages by enrollment behavior. Regarding variability across student groups, college GPA has more predictive value for students from traditionally disadvantaged backgrounds than their peers. These results can help researchers and administrators understand the comparative value of different data sources when building early warning systems and optimizing decisions under specific policy goals. |
| title | Temporal and Between-Group Variability in College Dropout Prediction |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2401.06498 |