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A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR Simulators

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Titel: A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR Simulators
Autoren: Marcin Wolański Robert, Karol Jędrasiak
Quelle: Safety & Fire Technology, Vol 64, Iss 2, Pp 56-65 (2024)
Verlagsinformationen: CNBOP-PIB Centrum Naukowo-Badawcze Ochrony Przeciwpozarowej, 2024.
Publikationsjahr: 2024
Schlagwörter: adaptive difficulty, 05 social sciences, vr training, scenario simulation, 02 engineering and technology, Engineering (General). Civil engineering (General), fire and rescue training, optimising level adaptation (ola), 0202 electrical engineering, electronic engineering, information engineering, emergency response preparedness, TA1-2040, 0503 education, personalised learning, professional development
Beschreibung: Aim: This study introduces the Optimising Level Adaptation (OLA) algorithm, designed to enhance scenario simulations for professional VR training by dynamically adjusting difficulty levels to match user performance, thereby supporting personalised learning and readiness for high-stakes situations such as firefighting and emergency response. Project and methods: The OLA algorithm divides scenario activities into blocks and adjusts their difficulty based on user performance in comparison to a reference group of AI-controlled agents. The algorithm’s efficacy was tested across three proprietary VR simulators covering diverse professional scenarios: public speaking, hydrogen electrolysis and mechanical technician operations. Each scenario was divided into ten blocks of varying difficulty (easy, medium, difficult), dynamically adjusted based on the user's performance. This structure enables rapid adaptation, making it particularly beneficial for fire and rescue training, where realistic, yet scalable, scenario complexity is critical to preparing for unpredictable conditions in the field. Results: Testing with 30 participants per simulator revealed an average final score of approximately 75%, closely aligning with the target success rate of 70%. The average number of difficulty level switches (between 0.8 and 1.16 across scenarios) demonstrated the algorithm’s effective adaptation to user performance, thus ensuring optimal engagement. The OLA algorithm’s capacity to tailor training difficulty in real time reflects its potential to enhance skill retention and readiness in emergency response settings, where maintaining user engagement at appropriate challenge levels is essential for preparedness in life-threatening situations. Conclusions: The OLA algorithm provides significant advancements in personalised VR training, particularly within fire- and rescue-related applications, by maintaining optimal engagement and adaptive challenge levels. The adaptability demonstrated across multiple scenarios indicates its versatility and potential for use in diverse high-risk training applications. Future research could enhance the OLA algorithm’s effectiveness by refining scenario block determination, therefore contributing to improved response times, decision-making and operational efficiency in the emergency services. Keywords: VR training, personalised learning, adaptive difficulty, fire and rescue training, scenario simulation, professional development, Optimising Level Adaptation (OLA), emergency response preparedness
Publikationsart: Article
ISSN: 2658-0810
2657-8808
DOI: 10.12845/sft.64.2.2024.4
Zugangs-URL: https://doaj.org/article/cdc9080aa2ea4a2da17b75cbd502f0c9
Dokumentencode: edsair.doi.dedup.....940bd574e32dd3e824d708eb6f3b3361
Datenbank: OpenAIRE
Beschreibung
Abstract:Aim: This study introduces the Optimising Level Adaptation (OLA) algorithm, designed to enhance scenario simulations for professional VR training by dynamically adjusting difficulty levels to match user performance, thereby supporting personalised learning and readiness for high-stakes situations such as firefighting and emergency response. Project and methods: The OLA algorithm divides scenario activities into blocks and adjusts their difficulty based on user performance in comparison to a reference group of AI-controlled agents. The algorithm’s efficacy was tested across three proprietary VR simulators covering diverse professional scenarios: public speaking, hydrogen electrolysis and mechanical technician operations. Each scenario was divided into ten blocks of varying difficulty (easy, medium, difficult), dynamically adjusted based on the user's performance. This structure enables rapid adaptation, making it particularly beneficial for fire and rescue training, where realistic, yet scalable, scenario complexity is critical to preparing for unpredictable conditions in the field. Results: Testing with 30 participants per simulator revealed an average final score of approximately 75%, closely aligning with the target success rate of 70%. The average number of difficulty level switches (between 0.8 and 1.16 across scenarios) demonstrated the algorithm’s effective adaptation to user performance, thus ensuring optimal engagement. The OLA algorithm’s capacity to tailor training difficulty in real time reflects its potential to enhance skill retention and readiness in emergency response settings, where maintaining user engagement at appropriate challenge levels is essential for preparedness in life-threatening situations. Conclusions: The OLA algorithm provides significant advancements in personalised VR training, particularly within fire- and rescue-related applications, by maintaining optimal engagement and adaptive challenge levels. The adaptability demonstrated across multiple scenarios indicates its versatility and potential for use in diverse high-risk training applications. Future research could enhance the OLA algorithm’s effectiveness by refining scenario block determination, therefore contributing to improved response times, decision-making and operational efficiency in the emergency services. Keywords: VR training, personalised learning, adaptive difficulty, fire and rescue training, scenario simulation, professional development, Optimising Level Adaptation (OLA), emergency response preparedness
ISSN:26580810
26578808
DOI:10.12845/sft.64.2.2024.4