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

• Published in: Journal of educational data mining Vol. 11; no. 3; pp. 1 - 41
• Main Authors: Berens, Johannes, Schneider, Kerstin, Gortz, Simon, Oster, Simon, Burghoff, Julian
• Format: Journal Article
• Language: English
• Published: International Educational Data Mining 01.12.2019
• Subjects: Academic Persistence; At Risk Students; College Students; Data Analysis; Data Collection; Dropout Prevention; Foreign Countries; Immigrants; Risk Management; School Holding Power; Student Attrition; Student Characteristics; Germany

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 stude...