Hoy traemos, a este espacio para la lectura, este artículo publicado en el Journal of Educational Technology & Society,Volume 8 Issue 4, Pages 128-147 Adaptive Learning Resources Sequencing in Educational Hypermedia Systems
Advanced e-Services for the Knowledge Society Research Unit, Informatics and Telematics Institute, Centre for Research and Technology Hellas, 42, Arkadias Street, Athens, GR-15234 Greece, Department of Technology Education and Digital Systems, University of Piraeus, 150, Androutsou Street, Piraeus, GR-18534, Greece, email@example.com
Advanced e-Services for the Knowledge Society Research Unit, Informatics and Telematics Institute, Centre for Research and Technology Hellas, 42, Arkadias Street, Athens, GR-15234 Greece, Department of Technology Education and Digital Systems, University of Piraeus, 150, Androutsou Street, Piraeus, GR-18534, Greece, firstname.lastname@example.org
Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers direct guidance or semi-automatic mechanisms for making the design process less demanding, still it requires significant effort to overcome the problems of inconsistency, confluence and insufficiency, introduced by the use of rules. Due to the problems of inconsistency and insufficiency of the defined rule sets in the Adaptation Model, conceptual “holes” can be generated in the produced learning resource sequences (or learning paths). In this paper, we address the design problem of the Adaptation Model in AEHS proposing an alternative sequencing method that, instead of generating the learning path by populating a concept sequence with available learning resources based on pre-defined adaptation rules, it first generates all possible learning paths that match the learning goal in hand, and then, adaptively selects the desired one, based on the use of a decision model that estimates the suitability of learning resources for a targeted learner. In our simulations we compare the learning paths generated by the proposed methodology with ideal ones produced by a simulated perfect rule-based AEHS. The simulation results provide evidence that the proposed methodology can generate almost accurate learning paths avoiding the need for defining complex rule sets in the Adaptation Model of AEHS.
Adaptive Educational Hypermedia, LO Sequencing, Personalization, Learning objects