Student models construction by using information criteria
Maomi Ueno · 2002
Proposes a method of constructing student models for intelligent tutoring systems (ITSs) by using information criteria. This proposal provides a method to automatically construct the optimum student model from data. The main problem when traditional information criteria are employed to construct a model is that a large amount of data, which is difficult to obtain in actual school situations, needs to be obtained. This paper proposes a new criterion for using a smaller amount of data by utilizing a teacher's expert knowledge. Concretely, (1) the general predictive distribution is derived, and (2) a method of determining the hyper-parameters by using a teacher's expert knowledge is proposed. Finally, some Monte Carlo experiments comparing some information criteria [BIC (Bayesian information criterion), ABIC (Akaike's extension of BIC), MDL (minimum description length), and the exact predictive distribution] are performed. The results show that the proposed method provides the best performance.