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

这个小型企业创新研究第二阶段项目不仅在STEM教育中，而且在整个课程中都让K-12学生参与基于探究的学习。信息时代的到来要求21世纪所需的技能发生根本性的变化。为了教授共同核心标准中阐明的这些技能，教师应该以学生为目标？能够进行短期和更持久的研究项目。互联网不仅是教授这些技能的明显首选场所，也是强制要求这些技能的原因之一。正是对海量信息的轻松获取，使每个人都有可能进行研究、分析数据和建立新的知识。然而，问题是，互联网并不是一个拥有可靠的编目信息的图书馆，这些信息可以按主题进行搜索，在学校图书馆中，可以按阅读水平进行搜索。提出的按需个性化馆员技术的发展解决了这一问题。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，它使差异化学习成为可能，并为学习者提供了一个终身工具，帮助他们理解和批判性地参与数字信息。目标市场并不局限于美国。开发的语言算法适用于非英语语言，通过为其他语言建立网络管理员，允许及早瞄准国际市场。这个项目融合了语言学、阅读心理学、自然语言处理(NLP)和机器学习的研究。创新的NLP技术被用来检测与课程相关的适龄网站。开发的技术在实时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与学生的阅读水平相匹配的、适合她目前对该主题的熟悉程度的适应性个性化建议。正在申请专利的自适应组件是使用机器学习和协作过滤方法构建的。由此产生的研究工具，个性化的网络策展人，基于新的方法来模拟学生对网络资源主题的熟悉程度。这些方法被扩展到包括对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考试成绩，并将它们与预先标记的分级数据相对应。完整的解决方案包括集成的游戏化体验，帮助学生获得基本和高级研究技能，并养成始终如一地应用这些技能的习惯。该项目团队包括来自学术界和学校的教育技术专家，并进行广泛的研究，评估通过差异化教学带来的学习好处，包括特殊教育计划和英语学习者。

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.