Mining Educational Data to Predict Academic Dropouts: a Case Study in Blended Learning Course

Abstract

Learning management systems generate a large amount of data, where knowledge discovery is possible using data mining techniques. We proposed simple prediction scheme using decision tree analysis for purpose of classification to identify dropout prone students in the middle of the semester based on previous year’s course characteristics for that course. The data included 717 students’ online activities in compulsory, sophomore level course with blended learning styles, 79% of the actual dropout students were predicted correctly.