Proposal Title SCALE: Student Centered Adaptive Learning Engine
Category Information Technology and Communications
Abstract This project proposes to develop and validate a student centered adaptive learning engine that is focused on improving learning outcomes using data collected from educational technology software 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 utilizes adaptive instruction due to high costs of creating adaptive content. Our 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 visualizations providing researchers, developers, and educators the tools to explore student data in ways that will allow for new insights into how students learn. This adaptive learning engine includes the ability to connect to existing and new educational software providing a service to software companies thereby, improving and extending their existing software to adapt to individual students and maximize learning, overcoming the traditionally high cost of creating adaptive instruction. Software companies can add adaptive capabilities without complete redevelopment, bringing intelligent tutors mainstream. Software providers in the K-12, Higher Ed, and Corporate/Government training markets will be able to enhance the learning of students while maintaining existing training and tutoring tools. This engine will connect existing expertise and leading 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.
Amount Awarded $487,500
Keywords adaptive learning, educational big data, software development, intelligent tutoring system, cognitive model, learning assessment, educational data mining, adaptive tutoring system
Point of Contact Mary Jean Blink
[email protected]
TutorGen, Inc.
Location Fort Thomas
Campbell County