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

SBIR 第一阶段：Auto-Autodidact - 基于潜在语义分析 (LSA) 的网络交付学习环境

• 批准号: 0060133
• 负责人: Darrell Laham
• 金额: $ 9.99万
• 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: 2001
• 资助国家: 美国
• 起止时间: 2001-01-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0060133&HistoricalAwards=false
• 关键词: SBIR; Phase; Auto; Autodidact; Web

This Small Business Innovation Research (SBIR)Phase I 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 what they do not know, find relevant information anywhere in the electronic library, and connect participants with complementary needs and knowledge. Auto autodidact capitalize s 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 Knowledge Forum, a state-of-the-art facilitator 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 in a matter of days. Knowledge Analysis Technologies proffers a learning environment technology that has potential value for science and engineering education throughout the life cycle and for research and design organizations. The firm plans to commercialize the technology directly and through publishers, distance education providers, and educational testing organizations.

这个小企业创新研究（SBIR）第一阶段项目将结合联合收割机互联网，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，为个人和团体创造一个独立的学习和解决问题的环境。 使用潜在语义分析（LSA），Auto-autodidact（自学者：自学成才的人）首先通过课本上的自动训练来学习主题的词汇和概念。 然后，当学生学习和写作，小组讨论和计划时，它将不断评估他们知道什么和不知道什么，在电子图书馆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。 自动自学利用了同伴互动的激励力量，大量文本资源的即时可用性，以及跨时间和空间共享个人知识的可能性。 Auto autodidact将LSA与知识论坛（Knowledge Forum）集成，后者是分布式知识构建讨论的最先进的促进者，以及新提供的电子图书馆，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。它将在几天内为一个新领域构建学习环境的能力方面是独一无二的。知识分析技术提供了一种学习环境技术，对整个生命周期的科学和工程教育以及研究和设计组织具有潜在价值。 该公司计划直接或通过出版商、远程教育提供商和教育测试组织将该技术商业化。