Early identification of dropout risks
Project outline
In the research project, an early warning system for identifying impending study dropouts will be developed and the impact of an intervention after early detection will be tested. Subsequently, the effectiveness of the existing landscape of measures against dropouts will be evaluated with the help of the early warning system. Of particular interest is the comparison of the different study programs - especially the STEM subjects - as well as the comparison between students with and without migration background. The early warning system is to be designed in such a way that it can also be implemented at other universities.
Additional information
completed
2017 until 2020
Dropout risks from university, progress of studies, dropout, monitoring, interventions
Kerstin Schneider (project lead)
Simon Görtz (project lead)
Johannes Berens (contact person)
Simon Oster
- Schneider, K., Berens, J., Görtz, S. (2021): Automatisierte Früherkennung abbruchgefährdeter Studierender: Was können die Systeme leisten und was sind die Herausforderungen? Handbuch Qualität in Studium, Lehre und Forschung, Berlin, DUZ Verlags- und Medienhaus.
- Schneider, K., Berens, J., Görtz, S. (2021): Maschinelle Früherkennung abbruchgefärdeter Studierender und Wirksamkeit niedrigschwelliger Intervention. In Neugebauer, M., Daniel, H.D., Wolter, A. (Hrsg.): Studienerfolg und Studienabbruch. Wiesbaden, Springer VS.
- Berens, J., Schneider, K., Görtz, S., Oster, S., Burghoff, J. (2019): Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. In: Journal of Educational Data Mining, 11(3), 1-41. https://doi.org/10.5281/zenodo.3594771
- Schneider, K., Berens, J., Burghoff, J. (2019): Drohende Studienabbrüche durch Frühwarnsysteme erkennen: Welche Informationen sind relevant?. In: Zeitschrift für Erziehungswissenschaften, 22, 1121-1146. DOI: 10.1007/s11618-019-00912-1
- Berens, J., Schneider, K. (2019): Drohender Studienabbruch: Wie gut sind Frühwarnsysteme? In: Qualität in der Wissenschaft (QiW), 3+4/19, 102-108. ISSN 1860-3041
- Berens, J., Schneider, K., Görtz, S., Oster, S., Burghoff, J. (2018): Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods. CESifo Working Papers, 7259. [download from RePEc]
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