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

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: N/A
Agency Tracking Number: 0132035
Amount: $0.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2002
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
4940 Pearl East Circle, Suite 200
Boulder, CO 80303
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Darrell Laham
 (303) 545-9113
Business Contact
Phone: () -
Research Institution

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.

* Information listed above is at the time of submission. *

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