Discovering Learning Patterns of Male and Female Students by Contrast Targeted Rule Mining

Abstract

In recent years, data mining techniques has attracted the attention from educational researchers and applied in educational research pervasively. As a famous data mining method, traditional association rules mining tend to ignore the infrequent data item and can only analyze a single dataset. To address these issues, a contrast targeted rule mining model is introduced in this paper. A complete analysis for the patterns and differences in the academic situation of male and female students is then conducted by the contrast targeted rule mining. Some useful association rules extracted by CTR are presented to demonstrate the difference of male and female students' learning patterns.