Literaturnachweis - Detailanzeige
| Autor/inn/en | Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria |
|---|---|
| Titel | Early-Predicting Dropout of University Students: An Application of Innovative Multilevel Machine Learning and Statistical Techniques |
| Quelle | In: Studies in Higher Education, 47 (2022) 9, S.1935-1956 (22 Seiten)Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Ieva, Francesca) ORCID (Agasisti, Tommaso) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0307-5079 |
| DOI | 10.1080/03075079.2021.2018415 |
| Schlagwörter | Dropouts; Potential Dropouts; Dropout Prevention; Dropout Characteristics; Identification; At Risk Students; College Students; Artificial Intelligence; Data Collection; Data Analysis; Pattern Recognition; Predictor Variables; Engineering Education; Foreign Countries; Student Characteristics; Predictive Measurement; Italy |
| Abstract | This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university. (As Provided). |
| Anmerkungen | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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