Authors:
Alessandro da Silveira Dias
and
Leandro Krug Wives
Affiliation:
Universidade Federal do Rio Grande do Sul, Brazil
Keyword(s):
Metadata, Learning Object, Information Overload, End User, IEEE LOM, Learner-driven Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
Abstract:
E-learning systems created new learning spaces and enabled users to participate more actively in the construction of their own knowledge. In these, users can learn in a self-directed way, make choices regarding their learning depending on the possibilities provided by the system. One of the most important choices is "how to learn", which in this work corresponds to which learning object the user will choose. For this, the user, considering of a list of relevant learning objects, uses their metadata to make a decision. The problem is that current metadata standards have many types of information, so, the user suffers from the metadata information overload. For relieving the user, this work assesses the most relevant metadata from a set of learning objects and ranks them based on this assessment. A case study was conducted to show the application of this ranking on the AdaptWeb® e-learning system and indicated that the vast majority of subjects did not suffer from the metadata overload.