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Identifying false positives when targeting students at risk of dropping out
Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision...
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| Published in: | Education economics 2023-05, Vol.31 (3), p.313-325 |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c465t-54e7dbfbb2d8c749023831d2fda6011516a58560ff3b7c5d9049e9c0991edc033 |
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| cites | cdi_FETCH-LOGICAL-c465t-54e7dbfbb2d8c749023831d2fda6011516a58560ff3b7c5d9049e9c0991edc033 |
| container_end_page | 325 |
| container_issue | 3 |
| container_start_page | 313 |
| container_title | Education economics |
| container_volume | 31 |
| creator | Eegdeman, Irene Cornelisz, Ilja Meeter, Martijn van Klaveren, Chris |
| description | Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for dropout prevention explicit. Data of a Dutch vocational education institute is used to show how out-of-sample machine learning predictions can be used to formulate invitation rules in a way that targets students at risk more effectively, thereby facilitating early detection for effective dropout prevention. |
| doi_str_mv | 10.1080/09645292.2022.2067131 |
| format | article |
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| fulltext | fulltext |
| identifier | ISSN: 0964-5292 |
| ispartof | Education economics, 2023-05, Vol.31 (3), p.313-325 |
| issn | 0964-5292 1469-5782 |
| language | eng |
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| source | EBSCOhost Business Source Ultimate; International Bibliography of the Social Sciences (IBSS); EBSCOhost Econlit with Full Text; PAIS Index; ERIC; Taylor and Francis Social Sciences and Humanities Collection |
| subjects | Accuracy Algorithms Artificial Intelligence At Risk Students Dropout Characteristics Dropout Prevention Dropping out Foreign Countries Identification Intervention Learning machine learning Methods Prediction Prevention Study success Vocational education Vocational Schools |
| title | Identifying false positives when targeting students at risk of dropping out |
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