Initializing student models in Web-based ITSs: a generic approach

The issue of initializing the model of a new student is of great importance for educational applications that aim at offering individualized support to students. We introduce a general framework for the initialization of the student model in Web-based educational applications. According to the framework, the student modeler makes initial estimations of the new student's knowledge level and error proneness based on the models of other similar students. This is done by applying a machine learning technique. The similarity between students is calculated taking into account different student characteristics for different teaching domains. We have implemented the proposed methodology in two different tutoring domains, namely language learning and mathematics. An evaluation study conducted in the case of the Web-based language learning system, showed that the use of the framework can initialize student models in a sufficiently accurate way, given the little information about individuals available.