Capturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators

Lämsä, Joni; Mannonen, Joonas; Tuhkala, Ari; Heilala, Ville; Helovuo, Arto; Tynkkynen, Ilkka; Lampi, Emilia; Sipiläinen, Katriina; Kärkkäinen, Tommi; Hämäläinen, Raija

doi:10.1111/jcal.12817

Cited by 5 publications

(1 citation statement)

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“…The LF/HF ratio has been used in previous studies as an objective measure of cognitive load: Usui and Nishida (2017) found the LF/HF ratio to be significantly higher during a mental stress task than before or after and Minkley et al (2021) found that LF/HF ratio is related to the self-reported mental effort of the participants. Lämsä et al (2023) used HRV indices, including LF/HF ratio, to study pilot's cognitive load in a VR flight simulation. Kim and Jo (2019) also used the LF/HF ratio as a measure of cognitive load, and concluded that learners' changing cognitive load could be measured and visualised based on this index.…”

Section: Code Description Examplesmentioning

confidence: 99%

Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data

Sobocinski,

Dever,

Wiedbusch

et al. 2023

Brit J Educational Tech

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This study examines the embodied ways in which learners monitor their cognition while learning about exponential functions in an immersive virtual reality (VR) based game, Pandemic by Prisms of Reality. Traditionally, metacognitive monitoring has been assessed through behavioural traces and verbalised instances. When learning in VR, learners are fully immersed in the learning environment, actively manipulating it based on affordances designed to support learning, offering insights into the relationship between physical interaction and metacognition. The study collected multimodal data from 15 participants, including think‐aloud audio, bird's‐eye view video recordings and physiological data. Metacognitive monitoring was analysed through qualitative coding of the think‐aloud protocol, while movement was measured via optical flow analysis and cognitive load was assessed through heart rate variability analysis. The results revealed embodied metacognition by aligning the data to identify learners' physical states alongside their verbalised metacognition. The findings demonstrated a temporal interplay among cognitive load, metacognitive monitoring, and motion during VR‐based learning. Specifically, cognitive load, indicated by the low‐ and high‐frequency heart rate variability index, predicted instances of metacognitive monitoring, and monitoring predicted learners' motion while interacting with the VR environment. This study further provides future directions in understanding self‐regulated learning processes during VR learning by utilizing multimodal data to inform real‐time adaptive personalised support within these environments. Practitioner notesWhat is already known about this topic Immersive virtual reality (VR) environments have the potential to offer personalised support based on users' individual needs and characteristics. Self‐regulated learning (SRL) involves learners monitoring their progress and strategically regulating their learning when needed. Multimodal data captured during VR learning, such as birds‐eye‐view video, screen recordings, physiological changes and verbalisations, can provide insights into learners' SRL processes and support needs. What this paper adds Provides insights into the embodied aspects of learners' metacognitive monitoring during learning in an immersive VR environment. Demonstrates how SRL processes can be captured via the collection and analysis of multimodal data, including think‐aloud audio, bird's‐eye view video recordings and physiological data, to capture metacognitive monitoring and movement during VR‐based learning. Contributes to the understanding of the interplay between cognitive load, metacognitive monitoring, and motion in immersive VR learning. Implications for practice and/or policy Researchers and practitioners can use the causal relationships identified in this study to identify instances of SRL in an immersive VR setting. Educational technology developers can consider the integration of online measures, such as cognitive load and physiological arousal, into adaptive VR environments to enable real‐time personalised support for learners based on their self‐regulatory needs.

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Section: Code Description Examplesmentioning

confidence: 99%

Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data

Sobocinski,

Dever,

Wiedbusch

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

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Unlocking the power of immersive learning: The FAIRI instructional design proposition for adaptive immersive virtual reality

Obourdin,

de Maeyer,

Van den Bossche

2024

Computers & Education: X Reality

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Maintenance in aviation enabled by Industry 4.0 technologies: exploring the current research trends using a topic modeling approach

Rath,

Jha,

Padhy

et al. 2024

IJPPM

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PurposeSince passenger safety is critical, aviation maintenance is essential. Aviation maintenance management is changing due to Industry 4.0 (I4.0). According to earlier research, I4.0 technologies improve aircraft manufacturing efficiency and responsiveness through automation, predictive maintenance and process self-optimization. Thus, this study examines I4.0 research and aircraft maintenance's potential interaction.Design/methodology/approachUsing a text-mining methodology, this paper looks at the state of the art in aviation maintenance research in the I4.0 era. We used the topic modeling approach and Latent Dirichlet Allocation (LDA) technique to analyze the abstracts and indexed keywords of 929 research articles on the intersection of aviation maintenance and I4.0, subsequently clustering them into eight topics.FindingsWe have mapped out the emerging research trends at the intersection of “aviation maintenance” and “I4.0 technologies”, and presented suggestions for theoretical frameworks, applied frameworks and future lines of inquiry. This paper makes a theoretical contribution to the systematization of literature on I4.0 technologies in aviation maintenance. It provides valuable insight for managers by exploring the implications and opportunities that arise in light of recent innovations brought by I4.0 in aviation maintenance.Originality/valueThis study focuses on the use of Industry 4.0 technologies in aircraft maintenance processes, contributing to the growing research on digital technology in maintenance and maintenance, repair and overhaul (MRO). Furthermore, the study's analysis of the LDA topic model provides valuable insights for future research on using I4.0 technologies to investigate specific areas of application in the context of digital maintenance.

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Capturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators

Cited by 5 publications

References 53 publications

Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data

Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data

Unlocking the power of immersive learning: The FAIRI instructional design proposition for adaptive immersive virtual reality

Maintenance in aviation enabled by Industry 4.0 technologies: exploring the current research trends using a topic modeling approach

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