Mining the Change of Fuzzy Quantitative Association Rules for Summative Assessment

A learning management system (LSM) prevails and it accumulates an amount of data about the progress of student learning, demographic and students' background over different sessions. Educators are very concerned about the shifts of unknown relationships among a thousand variables about students in a LMS for adjusting their teaching strategies and pedagogics. However, educators are not satisfied with the traditional reports, which are explored with limited relative variables on the issue of summative assessment and learning achievement in static sessions. The information about the changes of unknown relationships among many variables cannot be produced with statistical methods in traditional reports. Our study proposes a mining change of fuzzy quantitative association rules model to reveal the information. This model can discover the six types of changes rules from unknown relationships among many variables with nominal or numerical attributes. Experiments are carried out to evaluate the proposed model. We empirical demonstrate how the model helps educators understand the changing characteristics of students and to modify their teaching practices.