Measuring cognitive load in augmented reality with physiological methods: A systematic review.
Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non‐invasive biometric sensors, unbiased real‐time detection of cognitive load in AR is expected. However, a range of sen...
| Publicado en: | Journal of Computer Assisted Learning Vol. 40; no. 2; pp. 375 - 394 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176012452&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176012452 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2024 vid: 40 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 176012452 172861447 176012452 176012452 10.1111/jcal.12882 176012452 ppf: 375 ppct: 19 formats: tig: atl: Measuring cognitive load in augmented reality with physiological methods: A systematic review. aug: au: Suzuki, Yuko Wild, Fridolin Scanlon, Eileen affil: Institute of Educational Technology, The Open University, Milton Keynes, UK sug: subj: Augmented Reality Cognition Evaluation User-Computer Interface Human Systematic Review Wearable Sensors Eye Movement Measurements ERIC Database Psycinfo Electroencephalography Electromyography Skin Physiology ab: Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non‐invasive biometric sensors, unbiased real‐time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject fields, and reported results are fragmented. Objectives: The aim of this review is to analyse systematically how physiological methods have been used to measure cognitive load and what the implications are for the future research on AR‐based tools. Methods: This paper took the systematic review approach. Through screening with 10 exclusion criteria, 23 studies, that contain 3 key elements: AR‐based intervention, cognitive state examination and physiological methods, were identified, analysed and synthesised. Results: Physiological methods in their current form require reference to provide meaningful interpretations and suggestions. Therefore, they are often combined with conventional methods. Many studies investigate the effect of wearable devices in comparison with non‐AR stimuli, which has been controversial, but detection of different causes of cognitive load are on the horizon. Eye‐tracking is the method most used and most consistent in the use of its parameters. Conclusions: A multi‐method approach combining two or more evaluation instruments is essential for the validation of users' cognitive state. In addition to the AR stimuli in question, having another independent variable such as task difficulty in experiment design is useful. Statistical approaches with more data input could help establish a reliable scale. The future research should attempt to dissociate cognitive load caused by different effects such as device, instruction, and other AR techniques as well as intrinsic and extraneous aspects, in a better experimental setup with multiple parameters. Lay Description: What is already known about this topic: The efficacy of AR‐based instructional tools can be evaluated using the concept of cognitive load.Cognitive load has been conventionally measured by self‐reporting and performance.There is a growing interest in physiological measurement of cognitive load with non‐invasive biometric sensors. What this paper adds: This paper systematically analyses how physiological methods are used to measure cognitive load in AR.It synthesises the fragmented results of AR studies conducted with a range of physiological methods. Implications for practice: Combining two or more evaluation instruments is essential for the validation of users' cognitive state.Eye‐tracking is the method most used, and fixation duration is the most consistent parameter.A statistical approach with more biometric data input could help establish a reliable cognitive load scale.Future research should attempt to separate cognitive load caused by different effects and different cognitive aspects. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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