Cognitive Diagnosis of Students' Test Performance Based on Probability Inference

Abstract

Cognitive diagnosis is aimed at inferring the degree of cognitive state from observations. This paper considers cognitive diagnosis as an instance of model-based diagnosis, which has been studied in artificial intelligence for many years. The model-based cognitive diagnosis we present runs on a model of students' courses in terms of knowledge items that they may learn, tests them and helps them to understand their faults in cognition, and thus improves their learning performance in an E-learning environment. To do so, courses are formally defined as set of knowledge items with requirement constraints, and associated with a set of exam questions. Moreover, the authors introduce Bayesian net to build a model of cognitive diagnosis, using probabilistic inference on it to help a student understand what knowledge item he/she does not master, and the recommendations like what should be done next. Experimental results show that the group of students with such understanding can improve their testing performance greatly in an E-learning environment. Although the demo system has been integrated with a specific computerized adaptive testing system, the general technique could be applied to a broad class of intelligent tutoring systems.