SBIR Phase II: Linguistic Analysis of Web Content for 21st Century Inquiry Learning

SBIR 第二阶段：21 世纪探究性学习的网络内容语言分析

• 批准号: 1456186
• 负责人: Eleni Miltsakaki
• 金额: $ 74.93万
• 依托单位: Choosito!
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1456186&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Linguistic; Analysis

This Small Business Innovation Research Phase II project engages K-12 students in inquiry-based learning not only in STEM education but across the curriculum. The coming of the information age has mandated radical changes in the skills set required for 21st century. To teach these skills, as articulated in the Common Core Standards, teachers are expected to target students? ability to conduct short as well as more sustained research projects. The Internet is not only the obvious go-to place for teaching these skills but one of the reasons for mandating them. It is the easy access to vast amounts of information that has made it possible for everyone to conduct research, analyze data and build new knowledge. The problem, however, is that the Internet is not a library with reliable and catalogued information that can be searched by topic and, in school libraries, by reading level. The development of the proposed technology, a personalized librarian on-demand, solves this problem. It offers unprecedented opportunity for learners of all ages to engage in inquiry, it enables differentiated learning and gives learners a life-long tool to help them understand and critically engage with digital information. The target market is not limited to the U.S. The developed linguistic algorithms apply to non-English, allowing early targeting of the international market by building web curators for other languages. This project blends research on linguistics, psychology of reading, natural language processing (NLP) and machine learning. Innovative NLP technology is used to detect age-appropriate sites that are relevant to the curriculum. The developed technology analyzes the reading difficulty and thematic content of websites automatically in real time search. The purpose of the system is to make adaptive individualized recommendations that match the student's reading level and are appropriate for her current familiarity with the topic. The patent pending adaptive component is built using machine learning and collaborative filtering methods. The resulting research tool, a personalized web curator, is based on novel methods for modeling the student's familiarity with the topic of web resources. These methods are extended to include novel analysis of video content. Unlike other adaptive educational products, the system's adaptive recommendations do not rely on obtaining test scores from the user and corresponding them to pre-labeled, leveled data. The complete solution includes an integrated gamified experience that helps the students acquire basic and advanced research skills and develop the habit of applying them consistently. The project team includes educational technology experts from academia and schools and conducts extensive studies evaluating learning benefits by the afforded differentiation in instruction, including special education programs and English Language Learners.

这个小企业创新研究第二阶段项目使K-12学生不仅在STEM教育中，而且在整个课程中进行基于探究的学习。 信息时代的到来已经要求21世纪所需的技能发生根本性的变化。为了教授这些技能，正如共同核心标准所阐述的那样，教师应该以学生为目标？进行短期和更持续的研究项目的能力。互联网不仅是教授这些技能的明显去处，也是强制要求他们的原因之一。正是由于可以轻松获取大量信息，每个人都可以进行研究，分析数据和构建新知识。然而，问题是，互联网不是一个图书馆，它提供的信息既不可靠，也没有编目，不能按主题搜索，在学校图书馆，也不能按阅读水平搜索。所提出的技术的发展，个性化图书馆员的需求，解决了这个问题。它为所有年龄段的学习者提供了前所未有的参与探究的机会，它实现了差异化学习，并为学习者提供了终身工具，帮助他们理解和批判性地参与数字信息。目标市场不仅限于美国，开发的语言算法适用于非英语，通过为其他语言建立网络策展人，可以早期瞄准国际市场。该项目融合了语言学，阅读心理学，自然语言处理（NLP）和机器学习的研究。创新的NLP技术用于检测与课程相关的适龄网站。所开发的技术在真实的搜索中自动分析网站的阅读难度和主题内容。该系统的目的是做出自适应的个性化建议，与学生的阅读水平相匹配，并适合她当前对主题的熟悉程度。正在申请专利的自适应组件是使用机器学习和协同过滤方法构建的。由此产生的研究工具，一个个性化的网络策展人，是基于新颖的方法建模的学生熟悉的主题的网络资源。这些方法被扩展到包括视频内容的新颖分析。与其他自适应教育产品不同，该系统的自适应推荐不依赖于从用户那里获得测试分数，并将其与预先标记的分级数据相对应。完整的解决方案包括一个集成的游戏化体验，帮助学生获得基本和先进的研究技能，并养成始终如一地应用它们的习惯。该项目团队包括来自学术界和学校的教育技术专家，并进行了广泛的研究，评估教学中提供的差异化的学习效益，包括特殊教育计划和英语学习者。