Abstract

Adaptive systems provide personalised services in accordance with their knowledge or assumptions about each interacting user. Such systems adapt their own behaviour to find the optimal response to a specific user need or goal. In adaptive e‐learning systems, personalisation is based upon the premise that distinct learners follow distinct strategies during learning and show distinct preferences in the consumption of learning materials. Indeed, adaptive e‐learning systems have proven to be more efficient than non‐adaptive approaches in some application domains and for some types of learners. An efficient adaptive system is capable of deciding autonomously what, how, when and why to personalise. Though, the last two aspects (when and why) are mostly triggered through its environment. Therefore, accurate internal models of the system’s real world are of major importance, i.e. the better the system knows the targets in its environment, the higher its capability of adapting successfully. First of all,

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