Relevance Ranking Metrics for Learning Objects

The main objective of this paper is to improve the current status of learning object search. First, the current situation is analyzed and a theoretical solution, based on relevance ranking, is proposed. To implement this solution, this 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 percent better than the baseline ranking.