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Article
Automating Open Educational Resource Assessments: A Machine Learning Generalization Study
Joint Conference on Digital Libraries Proceedings 2011 (2011)
  • Heather Leary, Utah State University
  • Mimi Recker, Utah State University
  • Andrew Walker, Utah State University
  • Philipp Wetzler, University of Colorado at Boulder
  • Tamara Sumner, University of Colorado at Boulder
  • James Martin, University of Colorado at Boulder
Abstract

Assessing the quality of online educational resources in a cost effective manner is a critical issue for educational digital libraries. This study reports on the approach for extending the Open Educational Resource Assessments (OPERA) algorithm from assessing vetted to peer-produced content. This article reports details of changes to the algorithm, comparisons between human raters and the algorithm, and the extent the algorithm can automate the review process.

Publication Date
2011
Citation Information
Leary, H., Recker, M., Walker, A., Wetzler, P., Sumner, T., Martin, J. (2011). Automating Open Educational Resources Assessments: A Machine Learning Generalization Study. In proceedings of the Joint Conference on Digital Libraries, New York: ACM.