Issue No. 01 - January-March (2008 vol. 1)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TLT.2008.1
Xavier Ochoa , Escuela Superior Politécnica del Litoral, Guayaquil
Erik Duval , Katholieke Universiteit Leuven, Leuven
This paper develops the concept of relevance in the context of learning object search. It proposes a set of metrics to estimate the topical, personal and situational relevance dimensions. These metrics are derived mainly from usage and contextual information. 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, the combination of the metrics through the RankNet learning sorts the result list 50% better than the base-line ranking. The paper also presents openquestions in the field of learning object relevance ranking that deserve further attention.
Digital Libraries, Systems issues, Search process, Metadata, Information filtering
X. Ochoa and E. Duval, "Relevance Ranking Metrics for Learning Objects," in IEEE Transactions on Learning Technologies, vol. 1, no. , pp. 34-48, 2008.