Mining categories of learners by a competitive neural network

This paper addresses the problem of user modeling, which is a crucial step in the development of Adaptive Hypermedia systems. In particular, we focus on adaptive educational hypermedia systems, where the users are learners. Learners are modeled in the form of categories that are extracted from empirical data, represented by responses to questionnaires, via a competitive neural network. The key feature of the proposed network is that it is able to adapt its structure during learning so that the appropriate number of categories is automatically revealed. The effectiveness of the proposed approach is shown on two questionnaires of different type.