Abbruchgründe nicht-traditioneller Studierender – Identifikation von Clustern mittels Data Mining

Herrmann L (2022)


Publication Language: German

Publication Type: Other publication type

Publication year: 2022

Series: Zeitschrift für Hochschulentwicklung

Book Volume: 17

Pages Range: 133-154

Journal Issue: 4

DOI: 10.3217/zfhe-17-04/07

Abstract

Non-traditional students are at increased risk of dropping out, despite good academic performance. This paper therefore aims to contribute to a better understanding of the reasons why these students drop out. In general, dropping out is a complex process that is difficult to grasp. In order to better understand this issue, a cluster analysis was conducted to identify patterns in the reasons for dropping out. The analysis yielded six groups whose members have similar reasons for dropping out. For example, family, performance and finances played a role in one cluster each. In other clusters, however, no clear reason can be identified.

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How to cite

APA:

Herrmann, L. (2022). Abbruchgründe nicht-traditioneller Studierender – Identifikation von Clustern mittels Data Mining.

MLA:

Herrmann, Lisa. Abbruchgründe nicht-traditioneller Studierender – Identifikation von Clustern mittels Data Mining. 2022.

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