Identifying core concepts in educational resources

Authors:
James M. Foster;Md. Arafat Sultan;Holly Devaul;Ifeyinwa Okoye;Tamara Sumner
Affiliations:
University of Colorado, Boulder, Boulder, CO, USA;University of Colorado, Boulder, Boulder, CO, USA;University Corporation for Atmospheric Research, Boulder, CO, USA;University of Colorado, Boulder, Boulder, CO, USA;University of Colorado, Boulder, Boulder, CO, USA
Venue:
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Year:
2012

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Abstract

This paper describes the results of a study designed to assess human expert ratings of educational concept features for use in automatic core concept extraction systems. Digital library resources provided the content base for human experts to annotate automatically extracted concepts on seven dimensions: coreness, local importance, topic, content, phrasing, structure, and function. The annotated concepts were used as training data to build a machine learning classifier as part of a tool used to predict the core concepts in the document. These predictions were compared with the experts' judgment of concept coreness.