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Direct linkKemper, Lorenz; Vorhoff, Gerrit; Wigger, Berthold U. – European Journal of Higher Education, 2020
We perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without the need of specific collection. Therefore, we propose a methodical approach…
Descriptors: Foreign Countries, Predictor Variables, Potential Dropouts, School Holding Power
Troob, Charles – 1985
A longitudinal analysis of students who entered New York City high schools in 1979 supports the perception that most future dropouts can be identified at the beginning of their high school careers. This study examined the records of more than a quarter of the 1979 entering class at New York City high schools. Analyses were performed on attendance,…
Descriptors: Academic Achievement, Attendance, Dropout Characteristics, Dropout Rate
