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A computational tool for engineer dropout prediction

Dropout rates for students in high education are remarkably high, and the phenomenon has been investigated in several studies. Student dropout represents a loss of human capital and a waste of resources. This paper presents an analytic learning framework we have been developing at university to identify potential dropout situations in engineering bachelor students. We discuss the underlying model and show how it has been deployed in an analytics pipeline that alerts schools by predicting possible dropout situations. Our tool is also prescriptive in that it provides insight that might suggest strategies to reduce the dropout rates.