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STTR Phase I: A Student Centered Adaptive Learning Engine

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1346448
Agency Tracking Number: 1346448
Amount: $225,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: EA
Solicitation Number: N/A
Timeline
Solicitation Year: 2013
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-01-01
Award End Date (Contract End Date): 2015-06-30
Small Business Information
1037 S Fort Thomas Ave
Fort Thomas, KY 41075-2281
United States
DUNS: 967553566
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mary Blink
 (513) 378-1668
 mjblink@tutorgen.com
Business Contact
 Mary Blink
Phone: (513) 378-1668
Email: mjblink@tutorgen.com
Research Institution
 Carnegie-Mellon University
 
5000 Forbes Avenue
Pittsburgh, PA 15213-
United States

 () -
 Nonprofit college or university
Abstract

This STTR Phase I project proposes to develop and validate a student centered adaptive learning engine that is focused on improving learning outcomes using data collected from new and existing educational technology projects combined with advanced technology to automatically generate adaptive capabilities, thus creating ready-to-go intelligent tutoring systems. Providing adaptive instruction to students has been shown to be an effective way to improve student performance, yet very little educational software takes advantage of adaptive instruction due to high cost of creating adaptive content. This data-driven engine will significantly reduce the cost of adaptive learning by creating new methods of deriving intelligent tutoring capabilities from collected student data. Unlike pure machine learning solutions, this engine will allow for human input to maximize improvements through refinement over time. By using large datasets previously collected from existing tutors, these objectives can be tested and validated. The combination of human input with machine learning has the potential to make important gains in understanding student modeling. Finally, the engine will include new visualizations providing researchers, developers, and educators the tools to explore student data in ways that will allow for new insights into how students learn. The broader/commercial impact of an adaptive learning engine includes the ability to connect to educational software providing a service to software companies thereby, improving and extending their new and/or existing software to adapt to individual students and maximize learning. Adding adaptive instruction to existing software has traditionally been difficult due to the high costs of creating adaptive instruction. This engine reduces the cost of offering adaptive instruction capabilities by providing connections to existing and new software. Existing software can add capabilities without complete redevelopment, creating whole new markets for existing educational software companies, while bringing intelligent tutors mainstream. The Engine will provide educators new tools to understand how students learn with software systems. Key software providers in the K-12, Higher Ed, and Corporate/Government educational markets will enhance the learning of their students while maintaining existing training and tutoring tools. Companies and organizations are looking for effective online teaching and training solutions that are flexible to meet varying learning needs and preferences of users to maximize learning efficiency. This engine will connect existing expertise and research with the innovative vision to expand the capabilities of intelligent tutoring systems to reach to a variety of markets using a human-centered, data-driven approach.

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

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