Abstract: “Educational Data Mining (EDM) techniques play an important role in understanding hidden students’ data patterns to improve the quality of teaching-learning professions. In machine learning, feature selection usually emerges as a preprocessing step to extract necessary and sufficiently small subset of features for predictive / decision-making type of learning tasks. In this study, authors decided to work only upon external (changeable) attributes of students by assigning weights that reflect their academic efforts put in for those attributes. The attribute precedence levels extracted student-wise by current FE model due to academic efforts put up by students in their ongoing course were compared with equivalently generated precedence relations from RELIEF method and it’s variant. The favorable model accuracies of these precedence relations when compared with RELIEF have given a new meaning to EDM objectives in the direction of individual student counseling encouraging them to appraise t”

Keywords: EDM, Students’ Academic Performance, Attribute Precedence Relations, Near-Hit, Near-Miss, RELIEF weights.