A new paradigm of ranking & searching in learning object repository
Proceedings of the second ACM international workshop on Multimedia technologies for distance leaning
Sharing e-learning experiences: a personalised approach
HCITOCH'10 Proceedings of the First international conference on Human-computer interaction, tourism and cultural heritage
Statistical profiles of highly-rated learning objects
Computers & Education
Learnometrics: metrics for learning objects
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
LONET: An interactive search network for intelligent lecture path generation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Considering formal assessment in learning analytics within a PLE: the HOU2LEARN case
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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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.