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Wednesday, October 26 • 1:45pm - 3:15pm
Automating Quality Assessments for Open Educational Resources

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Assessing the thorny and complex problem of quality in online educational resources in a cost effective manner is a critical issue for digital libraries. In a world of increasing peer produced content, where it is often used for educational purposes, determining the quality and usefulness of this content is also important. Acknowledging that quality is contextual and resources differ greatly in terms of writing, presentation, and target audience, there are still certain characteristics that distinguish high quality resources.

The Open Educational Resources Assessment (OPERA) is a machine learning algorithm that was developed to assess the quality of online resources in the Digital Library for Earth Systems Education (DLESE) to support faster discovery and evaluation of resources, especially for K12 teachers. Teachers often point out that finding and evaluating resources takes a large amount of their lesson planning time. OPERA includes over 60 indicators in 6 categories and 24 subcategories to define quality. An evaluation of the initial algorithm was conducted on expert vetted DLESE resources with positive results. Using an updated version of the algorithm, projects in the Instructional Architect (IA.usu.edu) created by K12 math and sciences teachers, were assessed and compared to expert human ratings. Results from the latest iteration of the algorithm are promising.

This session will exhibit details on the categories and indicators with results from some of the work noted above, specifically addressing the value of OPERA in assisting teacher’s selection and evaluation of resources. Additionally it will provide an opportunity for participants to weigh in on the nature and refinement of the quality categories and indicators, the relevance and potential influence of OPERA for open content, and interact with resources tagged by OPERA. Participants will learn how algorithms like OPERA can be useful in instructional settings.

avatar for Heather Leary

Heather Leary

Assistant Professor, Brigham Young University
I am an educational researcher passionate about inquiry-based learning, open education, teacher professional learning, and technology integration (where it makes the most sense) all for increasing student knowledge and skills.

Wednesday October 26, 2011 1:45pm - 3:15pm MDT
White Pine, Painted Horse, Arrowhead

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