RT&L FOCUS AREA(S): Machine Learning/AI; General Warfighting Requirements
TECHNOLOGY AREA(S): Human Systems
OBJECTIVE: Increasing complexity in many military roles may require increased cognitive agility in areas of situation assessment, strategic and tactical decision making and better skill interpreting information in order to evaluate the need for a change in plans. Significant research has shown that it takes years and many repetitions for an individual to gain the skills. This cultivated knowledge and decision-making capability is necessary to develop expertise in any of these areas. However, recent research suggests that skill acquisition can be accelerated. This proposal explores a learning model that may help military personnel develop the necessary capabilities much faster using virtual environments with granular feedback, which have been shown to accelerate learning.
DESCRIPTION: Significant research has shown that it takes years and many repetitions for an individual to gain the skills, knowledge, and decision-making capability necessary to become an expert in a field. Recent research suggests that this requirement can be accelerated. Three questions come to mind: 1. Given recent research on accelerated learning in specific domains [Ref 1], can expertise that traditionally required years to master take only days or weeks to achieve? 2. What are the implications for expertise in broad cognitive capabilities such as situation assessment, strategic thinking, and tactical or general cognitive agility? Understanding these questions is crucial and time sensitive since the world is maneuvering and innovating faster across key domains including technology, military, politics, business, and education.
The interest in accelerating the mastery of expertise has a long history [Refs 2, 3]. Schneider et al [Ref 4] posited that those considered experts are qualitatively different from novices and journeymen. The process of becoming an expert takes years. He claimed that training the traditional route would not enable novices and journeymen to achieve the highest levels of expertise. Rather than traditional methods of training, the instructional designer and trainer must plumb the depths of learning strategies of those who would be experts. However, recently there has not been interest in this area of research, as evident by the dates of the publications referenced. It is time to revisit this area with the research described in this SBIR topic. This research could support an updated model of expertise acquisition that would advance training environments.
PHASE I: Develop a concept for an advanced training environment in the form of a multi-player game. Identify learning design features that encourage rapid learning. Base the architecture of the game on a model of accelerated learning as applied to military personnel. Ensure that the intent of the game should be to train combat personnel to operate successfully in urban battle settings. Produce and submit a final report that is a design document that describes the multi-player game features and includes a Phase II plan that describes a technical approach to achieve the desired result/product; and includes key component technological milestones such as the proposed experimental design to validate the resulting accelerated learning algorithms within the context of relevant operational tasks and environments.
PHASE II: Apply the instructional design accelerated learning principles in Phase I to a proof-of-concept technical feasibility demonstration. Empirically test the learning model by applying it via a virtual learning environment to a complex military problem. The virtual learning environment should be game based and multiplayer. Junior officers and senior non-commissioned officers (NCOs) will be the target users for the proof of concept. Validate training scenarios. Develop a data collection plan that includes the number and type of subjects; control condition, assessment instruments, and analysis plan.
PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the resulting technology for use in operational environments. Private Sector Commercial Potential: This SBIR topic would provide much needed theory, principles and technology to help the Navy/USMC introduce accelerated learning principles to both instructional designers, instructional personnel, and military personnel. The principles and technology would have broad applicability to learning endeavors within the military and to civilian training interests, particularly commercial game developers.
- Hoffman, R.R., Ward, P., Feltovich, P.J., DiBello, L., Fiore, S.M. and Andrews, D. “Accelerating Learning and Expertise: Concepts and Applications.” Human Factors and Ergonomics Society Annual Meeting Proceedings 54(4), September 2010, pp. 399-402. https://www.researchgate.net/publication/288495066_Accelerated_Learning_Prospects_Issues_and_Applications
- Chi, M., Feltovich, P., and Glaser, R. “Categorization and representation of physics problems by experts and novices.” Cognitive Science, 5(2), 1981, pp. 121-152. https://apps.dtic.mil/dtic/tr/fulltext/u2/a100301.pdf
- Chi, M. T. H., Glaser, R. and Rees, E. “Expertise in problem solving.” R. J. Sternberg (Ed.) Advances in the psychology of human intelligence, Vol. 1, pp. 7-75. https://www.public.asu.edu/~mtchi/papers/ChiGlaserRees.pdf
- Schneider, V. I., Healy, A. F. and Bourne, L. E., Jr. “What is learned under difficult conditions is hard to forget: Contextual interference effects in foreign vocabulary acquisition, retention, and transfer.” Journal of Memory and Language, 46, 2002, pp. 419-440. https://www.academia.edu/19510017/What_Is_Learned_under_Difficult_Conditions_Is_Hard_to_Forget_Contextual_Interference_Effects_in_Foreign_Vocabulary_Acquisition_Retention_and_Transfer