Publication: Coverage Metrics for Learning-Event Datasets on Client-Side Monitoring
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2012
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IEEE
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Abstract
The collection of learner events within a server-client architecture occurs either at server, client or both complementarily. Such collection may be incomplete due to various factors, particularly for client-based monitoring, where learners can disable, delete or even modify their event logs due to privacy policies. The quality and accuracy of any analysis based on such data collections depends critically on the quality of the subjacent dataset. We propose three initial metrics to evaluate the completeness of a learning dataset: client-to-server ratio, event-to-activity ratio and subjective ratio. These metrics provide a glimpse on the coverage rate of the monitoring and can be applied to distinguish subsets of data with a minimum level of reliability to be used in a learning analytics stud
Description
Proceedings of: 12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012).
Workshop "Bootstrapping Learning Analytics". Chair: Fridolin Wild & Felix Mödritscher
Keywords
Learning analytics, Metric, Coverage, Completeness
Bibliographic citation
Proceedings of the: 12th IEEE International Conference on Advanced Learning Technologies ICALT 2012, Rome 4-6 July 2012. IEEE, pp. 652 - 653.