SBIR Phase I: READ! Toolkit, A Knowledge-Based Expert System to Develop Beginning Literacy Curricula in Endangered or Underserved Languages

SBIR 第一阶段：阅读！

• 批准号: 1842833
• 负责人: Catherine Callow-Heusser
• 金额: $ 22.5万
• 依托单位: BETTERMENT LABS, L.L.C.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842833&HistoricalAwards=false
• 关键词: SBIR; Phase; READ; Toolkit; Knowledge

This SBIR Phase I project will develop and test a computer-based expert system that will guide development of beginning literacy curricula in any phonics-based language. The system will produce beginning literacy curricula mobile apps for phones and tablets and will include beginning reading, writing, vocabulary-building, and assessment activities. The expert system will (a) minimize cost, time and expertise required to develop early literacy curricula, (b) produce curricula that could increase early reading fluency and bilingualism, which is related to children's improved ability to manage their thoughts, behaviors and emotions, (c) provide effective curricula to learn languages other than English at home or school, (d) revitalize endangered or underserved languages while increasing connections to culture and heritage, which will improve long-term academic, health and economic outcomes for indigenous children or children who speak a language other than English at home, and increase their participation in STEM careers, and (e) contribute to research on bilingualism's impacts on academic, social, emotional, and cultural outcomes. The system will be licensed by underserved language communities, tribes, individuals, and organizations interested in improving literacy, with revenue produced by the mobile app products developed using the expert system shared between curriculum developers and the expert system developer.This SBIR Phase I project will develop and test a knowledge-based expert system that integrates early literacy research; language analysis to provide evidence-based recommendations for developing decodable text for phonics-based early literacy curricula; evidence-based teaching and formative assessment strategies; curriculum development knowledge; user interface/user experience best-practices; natural language processing for pronunciation feedback; and database and technology skills to guide users who may not have the knowledge and skills in developing a beginning literacy curricula in any language. The best practices and methods based on the research will be incorporated by analyzing text to create lists of sounds/letters and words, their frequencies, and sounds and words that come before and after each sound/word. From these lists, the system will generate language-specific recommendations based on the rules for optimal sequencing of sounds and words for learning to decode in another language. While some evidence-based guidelines needed for decoding in English, which is less phonetically regular than many underserved languages, may not apply in a language that is more phonetically regular, most are expected to. These rules will guide recommendations from which users of the knowledge-based expert system can select sequences sounds, words, and phrases to write decodable, connected text, culturally relevant stories for beginning literacy curricula.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该SBIR第一阶段项目将开发和测试一个基于计算机的专家系统，该系统将指导以任何基于语音的语言开发初级扫盲课程。该系统将为手机和平板电脑制作初级扫盲课程移动的应用程序，并将包括初级阅读、写作、词汇积累和评估活动。该专家系统将（a）最大限度地减少开发早期识字课程所需的成本、时间和专业知识，（B）编制可以提高早期阅读流利性和双语能力的课程，这与儿童管理其思想、行为和情绪的能力的提高有关，（c）提供有效的课程，以在家里或学校学习英语以外的语言，（d）振兴濒危或得不到充分利用的语言，同时增加与文化和遗产的联系，这将改善土著儿童或在家里说英语以外语言的儿童的长期学业、健康和经济成果，并增加他们对科学、技术、工程和数学职业的参与，以及（e）促进双语对学术、社会、情感和文化成果影响的研究。该系统将由服务不足的语言社区、部落、个人和对提高识字率感兴趣的组织授权使用，通过课程开发者和专家系统开发者之间共享的专家系统开发的移动的应用程序产品产生收入。语言分析，为基于语音的早期识字课程提供基于证据的建议，以开发可解码的文本;基于证据的教学和形成性评估战略;课程开发知识;用户界面/用户体验最佳做法;发音反馈的自然语言处理;以及数据库和技术技能，以指导那些可能没有知识和技能的用户开发任何语言的初级扫盲课程。基于研究的最佳实践和方法将通过分析文本来创建声音/字母和单词，它们的频率以及每个声音/单词之前和之后的声音和单词的列表。从这些列表中，系统将根据学习用另一种语言解码的声音和单词的最佳排序规则生成特定语言的建议。虽然英语解码所需的一些基于证据的指导方针可能不适用于语音规则更高的语言，但大多数语言都是如此。这些规则将指导建议，用户的知识为基础的专家系统可以选择序列的声音，单词和短语，写解码，连接的文本，文化相关的故事开始扫盲courses.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。