Quality Metrics for Learning Object Metadata

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Xavier Ochoa

Presentation at EDMedia 2006 about how to measure quality of lerning object metadata

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Quality Metrics for Learning Object Metadata Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium

Standardization and Interoperability of Learning Objects Repositories has solved the “chicken and egg” problem… …but has created new ones.

The production, management and consumption of Learning Object Metadata is vastly surpassing the human capacity to review or process these metadata.

Why Measuring Quality? ,[object Object],[object Object]

How to Measure Metadata Quality? ,[object Object],[object Object]

But we cannot measure the quality manually anymore…

… but is a good idea to follow the same quality characteristics.

Our Proposal: Use Metrics ,[object Object],[object Object]

Quality Characteristics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Quality Metric Readability Textual Information Content Nominal Information Content Weighted Completeness Simple Completeness Metric Formula Metric Name

Evaluation of the Metrics ,[object Object],[object Object],[object Object],[object Object],[object Object]

Applications ,[object Object],[object Object],[object Object],[object Object]

Further Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Thank you, Gracias Comments, Suggestions, Critics… are Welcome! More Information: http://ariadne.cti.espol.edu.ec/M4M

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Quality Metrics for Learning Object Metadata

1. Quality Metrics for Learning Object Metadata Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium

2. Standardization and Interoperability of Learning Objects Repositories has solved the “chicken and egg” problem… …but has created new ones.

3. The production, management and consumption of Learning Object Metadata is vastly surpassing the human capacity to review or process these metadata.

6. But we cannot measure the quality manually anymore…

7. … but is a good idea to follow the same quality characteristics.

10.

11. Quality Metric Readability Textual Information Content Nominal Information Content Weighted Completeness Simple Completeness Metric Formula Metric Name

12.

13. Evaluation Results

14.

15. Repository Evaluation

16. Quality Visualization

17. Automated Evaluation of Quality

18.

19. Thank you, Gracias Comments, Suggestions, Critics… are Welcome! More Information: http://ariadne.cti.espol.edu.ec/M4M