publication . Other literature type . Article . 2019

Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods

Berens, Johannes; Schneider, Kerstin; Gortz, Simon; Oster, Simon; Burghoff, Julian;
Open Access English
  • Published: 23 Dec 2019
  • Publisher: Zenodo
To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and private university to predict student dropout as a basis for a targeted intervention. To create an EDS that can be used in any German university, we use the AdaBoost Algorithm to combine regression analysis, neural networks, and decision trees - instead of relying on only one specific method. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private un...
ACM Computing Classification System: ComputingMilieux_COMPUTERSANDEDUCATION
free text keywords: student dropout, early detection, administrative data, higher education, AdaBoost
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Other literature type . 2019
Provider: Datacite
Other literature type . 2019
Provider: Datacite
Article . 2019
Provider: ZENODO
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