Relevance Ranking Metrics for Learning Objects

— The main objetive of this paper is to improve the current status of learning object search. First, the current situation is analyzed and a theretical solution, based on relevance ranking is pruposed. To implement this solution, the paper develops the concept of relevance in the context of learning object search. Based on this concept, it proposes a set of metrics to estimate the topical, personal and situational relevance dimensions. These metrics are calculated mainly from usage and contextual information and do not require any explicit information from users. An exploratory evaluation of the metrics shows that even the simplest ones provide statistically significant improvement in the ranking order over the most common algorithmic relevance metric. Moreover, combining the metrics through learning algorithms sorts the result list 50% better than the base-line ranking.

• Type: Conference
• Year: 2007
• Where: ECTEL
• Authors: Xavier Ochoa, Erik Duval
• Keywords: ECTEL 2007, Learning Object, Object Search, Relevance Ranking