Application of Support Vector Machine to Cognitive Diagnosis

Abstract

Support Vector Machine (SVM) is applied to the modern educational measurement’s diagnostic classification of 0,1 scoring test, and then comparisons of the classification results with some of the typical cognitive diagnostic classification are made. The results show that using SVM to cognitive diagnostic classification, which only needs a small sample for training, can ensure a high correct classification rate, while required short time to run. This advantage suggests that SVM could be employed to identify attributes behind in items to reduce the labor strength. Experiments show high precision and certain feasibility.