Learning Objects: Adaptive Retrieval through Learning Styles

Abstract

Nowadays, the amount of information grows in an exponential way, mainly because of technological advances in media. This scenario claims for the development of different skills in order to increase learning abilities, making them personal and customizable. Such factor is significant in a changing society, which implies in a range of mechanisms which would allow to identify, in a non-intrusive way, which learning style some specific student would prefer to perform in order to build knowledge from some learning object under a learning context. This requires defining some strategies in order to recognize adult learner's learning styles for some specific learning context. This work is based on theoretical references of Felder, Kolb and Gardner, proposing the implementation of a metadata annotation to identificate prime learning styles that are present in specific learning objects. This classification constitutes a starting point to recover learning objects from a repository according to apprentice's profile and experiences. As a result, the effectiveness of the use of learning objects will be improved.

E-Portfolios for Integrated Reflection

“…There have been metadata standards for learning objects, such as those proposed by Dublin Core (DC, 2009), IEEE LTSC (IEEE LTSC, 2009), and IMS Guide (IMS, 2006, which is similar to library catalogue systems. However, to effectively use learning objects to support teaching and learning for a specific field, domain knowledge must be applied to manage the learning objects (Harman & Koohang, 2005;Koohang, 2004;Mustaro & Silveira, 2006). This has lead to approaches to Semantic Web applications that model the relationships between learning objects using formal ontologies (Sicilia & Lytras, 2005).…”

Learning Pod: A New Paradigm for Reusability of Learning Objects

“…Several methods for capturing learning styles and using that information to drive learning object selection have been proposed (García, Amandi, Schiaffino, & Campo, 2007;García-Valdez et al, 2007;Kaur et al, 2005;Mustaro & Silviera, 2006;Santally & Senteni, 2005;Wolf, 2002). Whether the learning style is discerned from analyzing responses to a questionnaire, like the 118-question survey proposed by Wolf (2002) in the iWeaver project, or if it is detected in real-time during the course of instruction by providing alternative content models to be rated by the learner and corroborated by learner responses, the relevant information from which the Styler will base its placement decisions comes from the Learner module.…”

Project Based Learning and Learning Environments

“…It might also be able to provide alternate content depending the competence profile it has established, or indeed different media formats of LOs depending of student preferences. These notions are closely linked to research of adaptive LMSs based on learning styles (Mustaro, 2006).…”