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

这个小企业创新研究（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，为个人和团体创造一个独立的学习和解决问题的环境。Auto-autodidact （autodidact：自学成才的人）首先使用潜在语义分析（LSA），通过教科书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，以及小组讨论和计划时，它将不断评估他们知道的和不知道的，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。自动自学充分利用了同伴互动的动力、海量文本资源的即时可用性，以及跨越时间和空间共享个人知识的可能性。Auto-autodidact将把LSA与最先进的分布式知识构建讨论环境和最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。它在为新领域构建学习环境的能力将是独一无二的，它可以根据个人学习者或协作团队的需要在几天甚至几分钟内进行定制。随着我们进入一个网络化的世界，Knowledge Analysis Technologies提供的技术有可能将人和想法编织在一起，产生知识并促进合作。如果这个项目实现了它的潜力，并始终如一地为用户提供有用的结果，它可能会改变我们与数据以及彼此之间的交互方式。

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.