Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout. (February 2018) - Burgos, Concepción; Campanario, María L.; Peña, David de la; Lara, Juan A.; Lizcano, David; Martínez, María A.
Highlights: We have used logistic regression to identify students that are likely to drop out. The input data are the historical grades of students for different activities. Our dropout detection method outperforms other analysed proposals. We have designed a tutoring action plan to prevent student dropout. Our dropout prevention strategy can reduce the dropout rate by 14%. Abstract: E-learning systems generate huge amounts of data, whose analysis may become a daunting task which makes it necessary to use computational analytical techniques and tools. We propose the use of knowledge discovery techniques to analyse historical student course grade data in order to predict whether or not a student will drop out of a course. Logistic regression models are used for the purpose of classification. Experiments conducted with data on over 100 students for several distance learning courses confirm the predictive power of our proposal. Using the resulting predictive models we have designed a tutoring action plan. Applying this plan, we managed to reduce the dropout rate by 14% with respect to previous academic years in which no dropout prevention mechanism was applied. Our main contribution is a tool and a tutoring plan that can be used by our educational institution (and others) to reduce dropout rate in e-learning courses. Graphical abstract: