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

这是一个合作项目，获奖号为0121543（锡拉丘兹大学;伊丽莎白D。Liddy，主要研究者）。 华盛顿大学信息学院和锡拉丘兹大学自然语言处理中心的研究人员正在领导这项工作，并得到了中大陆教育和学习研究（McREL）和Achieve的帮助，Inc.调查人员正在开发一种自然语言处理工具（“StandardConnection”）自动分配内容标准和基准的教育资源在国家科学，数学，工程，和技术教育数字图书馆（NSDL）以及Web上的其他教育资源。 这些标准和基准来自McREL开发的标准和基准汇编以及Achieve标准数据库。 作为对一般描述性元数据的补充，StandardConnection工具生成的内容标准元数据将使任何州的教师都能够使用NSDL查找适当的教学资源，帮助学生达到州规定的特定能力。培养对人工为资源分配内容标准元数据标签所涉及的人类认知过程的复杂理解水平;基于这种理解设计和开发技术;在看不见的数据集上运行StandardConnection工具;分析结果并通过迭代调整工具，直到产生高度可靠的标记;并聘请一组教师专家分析工具将资源映射到标准和基准的质量。0085837和0085838，“打破元数据生成瓶颈”，其中研究者正在处理教育资源的文本，以自动分配用于教育资源的描述性和主题方面的教育材料网关（GEM）元标记。

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.