Automating Open Educational Resources Assessments: A Machine LearningGeneralization Study
Document Type
Conference Paper
Journal/Book Title/Conference
Proceedings of the Joint Conference on Digital Libraries
Publication Date
2011
First Page
283
Last Page
286
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.
Recommended Citation
Leary, H., Recker, M., Walker, A., & Recker, M., 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 (pp. 283-286), New York: ACM.
