Adaptive Rules of the Road
Abstract
The Problem: Navy warfighters need rules of the road ship handling and tactical decision-making training to help refresh schoolhouse, learning in order to stand watch in congested waters and to act decisively in small craft (FAC/FIAC) scenarios. The development and, enhancement of simulation trainers for teaching and refreshing nautical rules of the road and for tactical decision making will pro,vide strong tactical training built on a foundation of excellent seamanship. Our progress with developing the nautical Rules of the,Road (RoR) simulation trainer for the Surface Warfare School Command (SWSC), Newport, has shown RoRs usability and effectiveness in, refreshing seamanship and ship handling skills. However, RoRs simplistic learn by practicing approach to training needs significan,t improvement to provide relevant, flexible, learner-centered training that increases effectiveness, speed, and learner engagement.P,roposed Solution: Adaptive learning has often been shown to be more effective than traditional training methods [1, 2, 3, 4, 5] and,, more specifically, has been used to improve training effectiveness in RoR-like [5] and tactical decision-making simulation trainers, [4]. We therefore propose to work with SWSC to investigate, develop, and integrate adaptive learning tools that increase RoRs effe,ctiveness, relevance, flexibility, and learner engagement. Furthermore, we propose working with the National Simulation Center Pacif,ic, San Diego (NSCPAC) and our Intelligent Aggressor tactical simulation to develop and integrate adaptive learning for tactical dec,ision-making training in a prototype tactical decision-making trainer based on Intelligent Aggressor (IA). The engineering goal is t,o integrate adaptive learning into RoR and IA for more relevant, effective, and engaging training. The scientific goal is to develop, adaptive learning tools and technique that lead to a strong positive effect within a carefully controlled set of experiments that e,valuate adaptive learnings effect on RoR and IA based training. Three challenges need to be addressed to achieve our goals. First,,we need student learner, content, and feedback models to predict a students level of knowledge, predict the next scenario to presen,t, and predict interventions that maximize learning effectiveness. Next, we need to engineer and integrate the resulting adaptive tr,aining software framework into RoR and IA. And third, we need to evaluate the effect of these changes. These challenges lead to thre,e objectives in integrating and evaluating adaptive learning in RoR and IA.Objectives:1.Investigate, develop, and evaluate learner,,domain content, and feedback models foradaptive training2.Investigate, develop, and integrate an adaptive version of RoR for SWSC an,d prototypeIATac, an adaptive tactical FAC/FIAC trainer based on IA3.Design and conduct experiments that evaluate the effect of adap,tive training as weiteratively refine our models and increase adaptive training effectivenessAPPROVED FOR PUBLIC RELEASE
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 08, 2022
- Source ID
- N000142212122
Entities
People
- Sushil J. Louis
Organizations
- Nevada System of Higher Education
- Office of Naval Research
- United States Navy