Personalized learning via task load optimization
Abstract
A method for providing task load-optimized computer-generated training experiences to a user of a training system that includes: a display, a training simulator, a prediction program (ML1), and a training optimization program (ML2). In response to receiving a predicted optimal task load, ML2 provides a first training experience recommendation related to the training content and/or training conditions that, if utilized in providing a training experience to the user, is predicted to result in the predicted actual task load of the user equaling the predicted optimal task load. In response to receiving biometric information or performance metric information, ML1 determines the predicted actual task load. If the predicted actual task load does not match the predicted optimal task load, ML2 provides a second training experience recommendation and a second training experience is provided where at least one of the training content or the training conditions is changed.
- Inventors:
- Issue Date:
- Research Org.:
- Oak Ridge Y-12 Plant (Y-12), Oak Ridge, TN (United States); Avrio Analytics LLC, Knoxville, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1986968
- Patent Number(s):
- 11538352
- Application Number:
- 17/666,228
- Assignee:
- Avrio Analytics LLC (Knoxville, TN)
- DOE Contract Number:
- NA0001942
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 02/07/2022
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Bertolli, Michael G. Personalized learning via task load optimization. United States: N. p., 2022.
Web.
Bertolli, Michael G. Personalized learning via task load optimization. United States.
Bertolli, Michael G. Tue .
"Personalized learning via task load optimization". United States. https://www.osti.gov/servlets/purl/1986968.
@article{osti_1986968,
title = {Personalized learning via task load optimization},
author = {Bertolli, Michael G.},
abstractNote = {A method for providing task load-optimized computer-generated training experiences to a user of a training system that includes: a display, a training simulator, a prediction program (ML1), and a training optimization program (ML2). In response to receiving a predicted optimal task load, ML2 provides a first training experience recommendation related to the training content and/or training conditions that, if utilized in providing a training experience to the user, is predicted to result in the predicted actual task load of the user equaling the predicted optimal task load. In response to receiving biometric information or performance metric information, ML1 determines the predicted actual task load. If the predicted actual task load does not match the predicted optimal task load, ML2 provides a second training experience recommendation and a second training experience is provided where at least one of the training content or the training conditions is changed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {12}
}
Works referenced in this record:
System and Method for Autonomous Training
patent-application, December 2004
- Bedziouk, Serguel; Chardon, Laurent
- US Patent Application 10/455709; 20040248071
Instructor-Lead Training Environment and Interfaces Therewith
patent-application, July 2014
- Slayton, David A.; Newcombe Jr., Dale E.; Preisz, Eric A.
- US Patent Application 14/198297; 20140186801
Simulation based training system for measurement of team cognitive load to automatically customize simulation content
patent, September 2020
- Beaubien, Jeffrey; Feeney, John Joseph; DePriest, William N.
- US Patent Document 10,783,801
Neuroadaptive Intelligent Virtual Reality Learning System and Method
patent-application, March 2020
- Nel, Kyle; Manna, Amanda; Snyder, Michael
- US Patent Application 16/569542; 20200082735