In this paper, a terminological and pragmatic paradigm shift is proposed and undertaken from the field of Personalisation Systems towards the field of Adaptive Systems. A new conceptual framework for both topics is developed in order to enable a deeper insight into the challenges and benefits of merging the fields. The aim of this paper is to define a generic and component-based Personalisation Model (PM), which is derived from an analytical perspective on systems that are pertinent to adaptation. Furthermore, validity and applicability of the PM are demonstrated for the field of adaptive e-learning. Thus, practical experiences within the AdeLE (Adaptive e-Learning with Eye-Tracking) research project are discussed.
In this paper we introduce AdELE, a framework for adaptive e-learning utilising both eye tracking and content tracking technology. The framework is based upon the combination of fine-grained real-time eye tracking with synchronous content tracking, a user profiler, an adaptive multimedia learning environment, and a dynamic background library. The framework ensures not only adaptivity to the users’ preferences, knowledge level and the realtime tracking of their behaviour, but also ensures the relevance, accuracy and reliability of the knowledge provided.
The recent evolution of e-commerce and the astonishing growth of the Internet have increased the amount of information that scrupulous customers want to process before selecting items that meet their needs. Personalization has become an important strategy in Business to Consumer e-commerce, where knowledge about customers can be exploited in order to improve access to relevant products. This paper presents a machine learning-based approach to turn raw data about customers into knowledge about their interests. This knowledge is stored in personal profiles and is used to provide an intelligent search support.
In all areas of the e-era, personalization plays an important role. Particularly in elearning a main issue is student modelling, that is analysis of student behaviour and prediction of his/her future behaviour and learning performance. In this paper, we have focused our attention on a system based on Machine Learning techniques, which discovers the preferences, needs and interests of users accessing the e-learning system (the Profile Extractor). The automatic generation and discovery of the user profile, to obtain a simple student model, based on learning performance and communication preferences, helps create a personalized education environment.