An Ontology-Based Framework for Authoring Assisted by Recommendation

In this paper, we propose the use of Semantic Web technologies to bridge the gap between authoring systems and authors. The core part of our solution is the ontology-based framework that captures the information about the interaction between learning objects (LOs) and four main roles in the educational process (content author, instructional designer, teacher, and learner), and then according to this information proposes the most relevant learning content to the author. The central part of the framework is the RequestRecommendation ontology that formalizes the author’s request along with a set of learning content proposals (recommendation), as a response to that request. Furthermore, we propose the use of a weighting scheme to calculate the weight of the content proposals and thus enable their ranking within the recommendation.

• Type: Conference
• Year: 2007
• Where: ICALT
• Authors: Sasa Nesic, Dragan Gasevic, Mehdi Jazayeri
• Keywords: Content Proposals, ICALT 2007, Machine Learning, Relevant Learning Content, Semantic Web Technologies