In recent years, First Responders (FRs) have faced increasing challenges in their operations, highlighting a growing need for specialized and comprehensive training. In particular, the firefighting Incident Commanders (ICs) are playing a pivotal role, providing directions to field operators and making critical decisions in emergency situations. Over time, traditional training tools in this field have evolved, reaching their pinnacle with Augmented Sand Tables (ASTs). ASTs build on Spatial Augmented Reality (SAR), a form of eXtended Reality (XR) that utilizes projections. Although ASTs enable large-scale visualization of the morphological features of the terrain, by relying solely on SAR it is not possible to fully leverage the potential of XR, which is increasingly recognized as a powerful tool for training. This work introduces a novel approach to training ICs by integrating ASTs with XR, incorporating a learning-by-doing methodology alongside an objective measurement of trainees' performance. To this end, an XR Training System (XRTS) has been developed, combining the capabilities of an AST with personal Mixed Reality (MR) devices and integrating a physically accurate, interactive fire simulator. This system was deployed within a forest firefighting IC training course. All the system components were designed based on the theoretical foundations of decision-making to effectively develop the necessary skills. The proposed approach was compared with traditional AST-based training methods for these roles, focusing on the analysis of learning outcomes, user experience, usability, and cognitive load. The study demonstrated several advantages associated with the use of the XRTS, including improvements in training effectiveness and a notable reduction in overall cognitive load.
Enhancing sand table based incident command training with extended reality and interactive simulations: A use case in forest firefighting / Valente, Lorenzo; De Lorenzis, Federico; Calandra, Davide; Lamberti, Fabrizio. - In: IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES. - ISSN 1939-1382. - ELETTRONICO. - (In corso di stampa). [10.1109/TLT.2025.3545436]
Enhancing sand table based incident command training with extended reality and interactive simulations: A use case in forest firefighting
Valente, Lorenzo;De Lorenzis, Federico;Calandra, Davide;Lamberti, Fabrizio
In corso di stampa
Abstract
In recent years, First Responders (FRs) have faced increasing challenges in their operations, highlighting a growing need for specialized and comprehensive training. In particular, the firefighting Incident Commanders (ICs) are playing a pivotal role, providing directions to field operators and making critical decisions in emergency situations. Over time, traditional training tools in this field have evolved, reaching their pinnacle with Augmented Sand Tables (ASTs). ASTs build on Spatial Augmented Reality (SAR), a form of eXtended Reality (XR) that utilizes projections. Although ASTs enable large-scale visualization of the morphological features of the terrain, by relying solely on SAR it is not possible to fully leverage the potential of XR, which is increasingly recognized as a powerful tool for training. This work introduces a novel approach to training ICs by integrating ASTs with XR, incorporating a learning-by-doing methodology alongside an objective measurement of trainees' performance. To this end, an XR Training System (XRTS) has been developed, combining the capabilities of an AST with personal Mixed Reality (MR) devices and integrating a physically accurate, interactive fire simulator. This system was deployed within a forest firefighting IC training course. All the system components were designed based on the theoretical foundations of decision-making to effectively develop the necessary skills. The proposed approach was compared with traditional AST-based training methods for these roles, focusing on the analysis of learning outcomes, user experience, usability, and cognitive load. The study demonstrated several advantages associated with the use of the XRTS, including improvements in training effectiveness and a notable reduction in overall cognitive load.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2997726