Neural Networks in Adaptive Training
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This work has been funded by a CASI grant:
- Colorado Advanced Software Institute, 8/97--7/98, $34,048, with
B. Draper, CSU, and T. Donohue, SymSystems, Englewood,
CO, Modeling Student Pilots for Intelligent Training
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SymSystems is developing pilot
training systems for NASA. Based
on their experience, student pilots not only learn at different rates, but in
different manners. Some students, for example, tend to overcompensate early
in their training while others experience problems with take-off and landing.
The challenges presented to each student must be tailored to their unique
learning experiences. This requires an intelligent training regime that
exploits a model of each student that predicts where the student's performance
will be deficient. This project developed artificial neural network
algorithms for predicting student actions. Results show that modular neural
networks can discover when a student acquires the various skills necessary to
fly the simulated aircraft. This provides a basis for future
intelligent training systems.
Publications on this project include the following:
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Neural Networks in Adaptive Training Research in CS at CSU, Charles
W. Anderson /
anderson@cs.colostate.edu
Copyright © 1998 Charles Anderson