Mitigating the Knowledge-Action Gap: A Bayesian and Distributed Cognition Approach to XR Disaster Training
Traditional disaster drills provide theoretical knowledge but consistently fail to activate the psychomotor speed and decisional agency required under high-stress crisis conditions. This failure leads to the "Knowledge-Action Gap," where individuals experience a paralyzing response during actual seismic events. This article examines an XR-based and AI-driven "Intelligent Disaster Learning Architecture" designed to reprogram human cognition under pressure.
Our systemic approach utilizes a Distributed Cognition model to mitigate cognitive overload, operationalized through characters representing Jungian functions of consciousness. At the architecture's core, a Bayesian Knowledge Tracing (BKT) algorithm analyzes learner performance at 60 FPS, distinguishing between momentary slips and genuine knowledge gaps. By moving beyond subjective self-reports, the study utilizes Stealth Assessment to record millisecond latencies and decision-making patterns, providing an evidence-based model for predicting real-world survival performance.
Problem Framing: The Methodological Inadequacy of Traditional Drills
For decades, disaster preparedness has relied on low-fidelity, static simulations. Traditional drills, characterized by routine movements performed during scheduled alarms, fail to activate the psychomotor speed and critical decision-making required during an actual seismic event. This phenomenon, academically recognized as the "Knowledge-Action Gap," describes the paralysis or "freezing" response individuals experience under high-stress conditions despite possessing theoretical knowledge. As emphasized by Nakana & Katsuya (2021), passive information consumption—via brochures or videos—does not equate to preparedness. True resilience requires an "Intelligent Disaster Learning Architecture" designed to reprogram human cognition under pressure.
Conceptual Lens: Distributed Cognition and Jungian Functions
To mitigate Cognitive Overload during a crisis, our proposed system utilizes the Distributed Cognition model. In this framework, the learner shifts from being an isolated "hero" attempting to process all variables simultaneously to a "system conductor" who distributes cognitive tasks across specialized entities. This architectural approach is operationalized through characters representing Jung’s four functions of consciousness:
Character: Zeki
Cognitive Function: Thinking
Strategic Systemic Role: Analytical risk assessment and object identification.
Character: Kaya
Cognitive Function: Sensation
Strategic Systemic Role: Physical interaction and environmental manipulation.
Character: Cıvıltı
Cognitive Function: Intuition
Strategic Systemic Role: Perceptual awareness and early warning detection.
Character: Toprak
Cognitive Function: Psychomotor Speed
Strategic Systemic Role: Reflexive action and safe evacuation coordination.
Systemic Analysis: Bayesian Knowledge Tracing and Socratic Mentorship
At the core of this architecture is the Bayesian Knowledge Tracing (BKT) algorithm, which analyzes learner performance at 60 FPS. BKT allows the system to distinguish between a "Slip" (a momentary lapse in performance) and a "Knowledge Gap" (a genuine lack of competency). Furthermore, it identifies patterns associated with "Gaming the System," where learners attempt to bypass challenges through trial and error, triggering a more rigorous learning cycle to ensure mastery.
References
- Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge.. User Modeling and User-Adapted Interaction, 4(4), 253-278.https://doi.org/-
- Nakano, G., & Katsuya, T. (2021). Knowledge-action gap in disaster preparedness: A review of theoretical frameworks and empirical evidence.. Journal of Natural Disaster Science (veya ilgili akademik bülten), -, -.https://doi.org/-
- Shute, V. J. (2011). Stealth assessment in next-generation computer-based games.. Developmental Review, 31(2-3), 94-116.https://doi.org/-
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes.. Harvard University Press (Book), -, -.https://doi.org/-
- Hutchins, E. (1995). Cognition in the Wild.. MIT Press (Book), -, -.https://doi.org/-