The document discusses relevance ranking metrics for learning objects. It introduces the problem of abundance of learning objects making it difficult to find the most relevant ones. It then discusses using relevance ranking metrics as a solution, in a similar way search engines like Google rank results. Several potential metrics are introduced, including topical, personal and situational relevance. An exploratory study is described that tested the metrics on MIT OpenCourseware learning objects, finding that even basic metrics improved rankings over the baseline and that linear combinations of metrics should be further researched. The presenters conclude that meaningful and scalable relevance ranking is possible and could be used as a baseline for further research.

1. Relevance Ranking Metrics for Learning Objects Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium

3. Economy of Abundance

4. Economy of Abundance Put your LMS here!

9. What is Relevance?

10. Relevance Ranking Metrics Relevance Ranking Metric B Metric A Metric C Context

11. Relevance Ranking Metrics Relevance Ranking Meaningful Metric B Metric A Metric C Context

12. Relevance Ranking Metrics Relevance Ranking Scalable Metric B Metric A Metric C Context

13. Topical Relevance

14. Personal Relevance

15. Situational Relevance

21. Results

22. Basic Topical Relevance Metric Trees

23. Basic Personal Relevance Metric Net.

24. Basic Situational Relevance Metric XML

25. Linear Combination

26. Linear Combination