Dynamic Cognitive Load Assessment in Virtual Reality

IF 1.5 Q2 EDUCATION & EDUCATIONAL RESEARCH
Rachel L. Elkin, Jeff M. Beaubien, Nathaniel Damaghi, Todd P. Chang, David O. Kessler
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引用次数: 0

Abstract

BackgroundRecent advances in non-invasive physiologic monitoring leverage machine learning to provide unobtrusive, real-time assessments of a learner’s cognitive load (CL) as they engage in specific tasks. However, the performance characteristics of these novel composite physiologic CL measures are incompletely understood.ObjectivesWe aimed to 1) explore the feasibility of measuring CL in real time using physiologically-derived inputs; 2) evaluate the performance characteristics of a novel composite CL measure during simulated virtual reality resuscitations; and 3) understand how this measure compares to traditional, self-reported measures of CL.MethodsNovice (PGY1-2 pediatric residents) and expert (pediatric emergency medicine fellows and attendings) participants completed four virtual reality simulations as team leader. The scenario content (status epilepticus versus anaphylaxis) and level of distraction (high versus low) were manipulated. Cognitive load was measured in all participants using electroencephalography and electrocardiography data (“real-time CL”) as well as through self-report (NASA-TLX). Scenario performance also was measured.ResultsComplete data were available for 6 experts and 6 novices. Experts generally had lower CL than novices on both measures. Both measures localized the most significant differences between groups to the anaphylaxis scenarios (real-time CL [low-distraction] Cohen’s d -1.33 [95% CI -.2.56, -0.03] and self-reported CL [high-distraction] Cohen’s d -1.41 [95% CI -2.67, -0.10]). No consistent differences were seen with respect to level of distraction. Performance was similar between the two groups, though both exhibited fewer errors over time (F(3,48) = 5.75, p = .002).ConclusionIt is feasible to unobtrusively measure cognitive load in real time during virtual reality simulations. There was convergence between the two CL measures: in both, experts had lower CL than novices, with the most significant effect size differences in the more challenging anaphylaxis scenarios.
虚拟现实中的动态认知负荷评估
背景无创生理监测领域的最新进展是利用机器学习对学习者在完成特定任务时的认知负荷(CL)进行无干扰的实时评估。我们的目的是:1)探索使用生理输入实时测量认知负荷的可行性;2)评估在模拟虚拟现实复苏过程中新型复合认知负荷测量方法的性能特征;3)了解该测量方法与传统的自我报告认知负荷测量方法的比较。模拟场景的内容(癫痫状态与过敏性休克)和分散注意力的程度(高与低)均有不同。使用脑电图和心电图数据("实时 CL")以及自我报告(NASA-TLX)对所有参与者的认知负荷进行了测量。此外,还对情景表现进行了测量。在这两项测量中,专家的 CL 值普遍低于新手。在过敏性休克情景中,两组之间的差异最为显著(实时 CL [低分心] Cohen's d -1.33 [95% CI -.2.56, -0.03]和自我报告 CL [高分心] Cohen's d -1.41 [95% CI -2.67, -0.10])。分散注意力水平方面没有发现一致的差异。两组的表现相似,但随着时间的推移,两组都表现出较少的错误(F(3,48) = 5.75, p = .002)。两种认知负荷测量之间存在趋同性:在这两种测量中,专家的认知负荷均低于新手,在更具挑战性的过敏性休克情景中,效应大小差异最为显著。
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来源期刊
SIMULATION & GAMING
SIMULATION & GAMING EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
5.30
自引率
5.00%
发文量
35
期刊介绍: Simulation & Gaming: An International Journal of Theory, Practice and Research contains articles examining academic and applied issues in the expanding fields of simulation, computerized simulation, gaming, modeling, play, role-play, debriefing, game design, experiential learning, and related methodologies. The broad scope and interdisciplinary nature of Simulation & Gaming are demonstrated by the wide variety of interests and disciplines of its readers, contributors, and editorial board members. Areas include: sociology, decision making, psychology, language training, cognition, learning theory, management, educational technologies, negotiation, peace and conflict studies, economics, international studies, research methodology.
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