Karampiperis P. and Sampson D. (2004). Adaptive Learning Object Selection in Intelligent Learning Systems. Journal of Interactive Learning Research. Special Issue on Computational Intelligence in Web-based Education, vol. 15(4), November 2004. Adaptive Learning Object Selection in Intelligent Learning Systems PYTHAGORAS KARAMPIPERIS AND DEMETRIOS SAMPSON Department of Technology Education and Digital Systems, University of Piraeus 150, Androutsou Street, Piraeus, GR-18534 Greece and Informatics and Telematics Institute, Centre for Research and Technology Hellas, 42,Arkadias Street,Athens,GR-15234,Greece e-mail:{pythk,sampson}@iti.gr Adaptive learning object selection and sequencing is recognized as among the most interesting research questions in intelligent web-based education. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection is based on a set of teaching rules according to the cognitive style or learning preferences of the learners. In spite of the fact that most of these rules are generic (i.e. domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make “instructional sense”. Moreover, in order to design highly adaptive learning systems a huge set of rules is required, since dependencies between educational characteristics of learning objects and learners are rather complex. In this paper, we address the learning object selection problem in intelligent learning systems proposing a methodology that instead of forcing an instructional designer to manually define the set of selection rules, it produces a decision model that mimics the way the designer decides, based on the observation of the designer’s reaction over a small-scale learning object selection case. INTRODUCTION The high rate of evolution of e-learning platforms implies that on the one hand, increasingly complex and dynamic web-based learning infrastructures need to be managed more efficiently, and on the other hand, new type of learning services and mechanisms need to be developed and provided. To meet the current needs, such services should satisfy a diverse range of requirements, as for example, personalization and adaptation (Dolog, Henze, Nejdl, Sintek, 2004; Vasilakos, Devedzic, Kinshuk, Pedrycz, 2004). The field of computational intelligence in web-based education can contribute towards providing web-based technologies, methods and techniques for supporting teaching and learning in an intelligent way.