An application of data mining to the problem of the university students’ dropout using Markov chains

Abstract

A new application of data mining to the problem of University dropout is presented. A new modeling technique, based on Markov chains, has been developed to mine information from data about the University students’ behavior. The information extracted by means of the proposed technique has been used to deeply understand the dropout problem, to find out the high-risk population and to drive the design of suitable politics to reduce it. To represent the behavior of the students the available data have been modeled as a Markov chain and the associated transition probabilities have been used as a base to extract the aforesaid behavioral patterns. The developed technique is general and can be successfully used to study a large range of decisional problems dealing with data in the form of events or time series. The results of the application of the proposed technique to the students’ data will be presented.