Publications & Resources
CRESST Shiphandling Automated Assessment Engine: Mooring at a Pier
Alan D. Koenig, John J. Lee, and Markus R. Iseli
To meet the challenges of training shiphandling skills more effectively, the U.S. Navy seeks to automate the assessment of shiphandling skills to allow for less supervised practice and, therefore, reduced instructor load. As part of a broader initiative at the Surface Warfare Officers School (SWOS), CRESST has been working to develop this capability using an automated assessment engine (AAE), which infers student shiphandling proficiency based on meaningful, observable actions. This report describes the rubrics used in the AAE, as well as the inference model used therein. Plans for a future validation study are also outlined.
Koenig, A. D., Lee, J. J., & Iseli, M. R. (2016). CRESST shiphandling automated assessment engine: Mooring at a pier (CRESST Report 852). Los Angeles: University of California, Los Angeles, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).