SBIR Phase II: The Auto-Autodidact - A Web-Delivered Learning Environment Based on Latent Semantic Analysis (LSA)

SBIR 第二阶段：自动自学者 - 基于潜在语义分析 (LSA) 的网络交付学习环境

• 批准号: 0132035
• 负责人: Darrell Laham
• 金额: $ 49.99万
• 依托单位: Knowledge Analysis Technologies
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-03-01 至 2005-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0132035&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Auto; Autodidact

This Small Business Innovation Research (SBIR) Phase II project will combine the Internet, electronic libraries, and a new machine learning technique that simulates human understanding of text to produce an independent learning and problem-solving environment for individuals and groups. Using Latent Semantic Analysis (LSA), Auto-autodidact (autodidact: a self-taught person) first learns the vocabulary and concepts of a topic by automatic training on textbooks. Then, as students study and write, and groups discuss and plan, it will continuously evaluate what they know and do not know, find relevant information anywhere in an electronic library, and connect participants with complementary needs and knowledge. Auto-autodidact capitalizes on the motivational power of peer interaction, the instant availability of enormous textual resources, and the possibility of sharing individual knowledge over time and space. Auto-autodidact will integrate LSA with a state-of-art environment for distributed knowledge-building discussion and newly available electronic libraries to provide continuous embedded assessment, tutorial dialogue, and meaning-based information insertion. It will be unique in its ability to construct a learning environment for a new domain, customizing it for the needs of either an individual learner or a collaborating team, in a matter of days or even minutes.As we move into a networked world, Knowledge Analysis Technologies' proffered technology has the potential to weave together people and ideas, generating knowledge and fostering collaboration. If the project realizes its potential and consistently delivers useful results to users, it could transform how we interact with data and with one another.

这个小型企业创新研究(SBIR)第二阶段项目将结合互联网、电子图书馆和一种新的机器学习技术，模拟人类对文本的理解，为个人和群体创造一个独立学习和解决问题的环境。使用潜在语义分析(LSA)，自动自学(Autodidact：自学成才的人)首先通过教科书上的自动训练学习一个主题的词汇和概念。然后，随着学生学习和写作，以及小组讨论和计划，它将不断评估他们知道和不知道什么，在电子图书馆中的任何地方找到相关信息，并将参与者与互补的需求和知识联系起来。Auto-Autodidact利用同伴互动的激励力量、大量文本资源的即时可获得性以及跨时间和空间共享个人知识的可能性。Auto-Autodidact将把LSA与用于分布式知识建设讨论的最先进环境和新可用的电子图书馆相结合，以提供持续的嵌入式评估、教程对话和基于意义的信息插入。它的独特之处在于它能够为一个新的领域构建一个学习环境，在几天甚至几分钟内根据个人学习者或协作团队的需求对其进行定制。随着我们进入网络世界，知识分析技术提供的技术具有将人员和想法编织在一起、生成知识和促进协作的潜力。如果该项目实现其潜力，并持续向用户提供有用的结果，它可能会改变我们与数据和彼此交互的方式。