Collaborative Project: StandardConnection--Mapping NSDL Educational Objects to Content Standards

University Of Washington, Seattle WA

Investigators

Abstract

This is a collaborative project with Award No. 0121543 (Syracuse University; Elizabeth D. Liddy, Principal Investigator). Researchers at the University of Washington's Information School and Syracuse University's Center for Natural Language Processing are leading this effort, with assistance from Mid-continent Research for Education and Learning (McREL) and Achieve, Inc. The investigators are developing a natural language processing tool ("StandardConnection") for the automatic assignment of content standards and benchmarks to educational resources in the collections of the National Science, Mathematics, Engineering, and Technology Education Digital Library (NSDL) and to other educational resources on the Web. The standards and benchmarks come from the Compendium of Standards and Benchmarks developed by McREL, and the Achieve Standards Database. Supplementing general descriptive metadata, the content standards metadata generated by the StandardConnection tool will make it possible for a teacher in any state to use the NSDL to locate appropriate teaching resources for helping students achieve a particular competency set by the state. The project entails acquiring a "training" and "testing" collection of educational resources for analysis; cultivating a sophisticated level of understanding of the human cognitive processes involved in manually assigning content standard metadata tags to resources; designing and developing the technology based on this understanding; running the StandardConnection tool on an unseen set of data; analyzing the results and adjusting the tool, through iterations, until a highly reliable tagging is produced; and employing a group of teacher-experts to analyze the quality of the tool's mappings of resources to standards and benchmarks. This project constitutes a logical extension of work conducted under Award Nos. 0085837 and 0085838, "Breaking the Metadata Generation Bottleneck," in which the investigators are processing the text of educational resources to automatically assign Gateway to Educational Materials (GEM) metatags for the descriptive and subject aspects of educational resources.