Näytä suppeat kuvailutiedot

dc.contributor.authorLämsä, Joni
dc.contributor.authorMannonen, Joonas
dc.contributor.authorTuhkala, Ari
dc.contributor.authorHeilala, Ville
dc.contributor.authorHelovuo, Arto
dc.contributor.authorTynkkynen, Ilkka
dc.contributor.authorLampi, Emilia
dc.contributor.authorSipiläinen, Katriina
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.authorHämäläinen, Raija
dc.date.accessioned2023-05-10T06:15:17Z
dc.date.available2023-05-10T06:15:17Z
dc.date.issued2023
dc.identifier.citationLämsä, J., Mannonen, J., Tuhkala, A., Heilala, V., Helovuo, A., Tynkkynen, I., Lampi, E., Sipiläinen, K., Kärkkäinen, T., & Hämäläinen, R. (2023). Capturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators. <i>Journal of Computer Assisted Learning</i>, <i>39</i>(5), 1553-1563. <a href="https://doi.org/10.1111/jcal.12817" target="_blank">https://doi.org/10.1111/jcal.12817</a>
dc.identifier.otherCONVID_183087509
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/86849
dc.description.abstractBackground Cognitive load (CL) management is essential in safety-critical fields so that professionals can monitor and control their cognitive resources efficiently to perform and solve scenarios in a timely and safe manner, even in complex and unexpected circumstances. Thus, cognitive load theory (CLT) can be used to design virtual reality (VR) training programmes for professional learning in these fields. Objectives We studied CL management performance through behavioural indicators in authentic VR flight training and explored if and to what extent physiological data was associated with CL management performance. Methods The expert (n = 8) and novice pilots (n = 6) performed three approach and landing scenarios with increasing element interactivity. We used video recordings of the training to assess CL management performance based on the behavioural indicators. Then, we used the heart rate (HR) and heart rate variability (HRV) data to study the associations between the physiological data and CL management performance. Results and Conclusions The pilots performed effectively in CL management. The experience of the pilots did not remarkably explain the variation in CL management performance. The scenario with the highest element interactivity and an increase in the very low-frequency band of HRV were associated with decreased performance in CL management. Takeaways Our study sheds light on the association between physiological indicators and CL management performance, which has traditionally been assessed with behavioural indicators in professional learning in safety-critical fields. Thus, physiological measurements can be used to supplement the assessment of CL management performance, as relying solely on behavioural indicators can be time consuming.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofseriesJournal of Computer Assisted Learning
dc.rightsCC BY 4.0
dc.subject.otherkognitiivinen kuormitus
dc.subject.othercognitive load
dc.subject.othercognitive load management
dc.subject.otherphysiological measurements
dc.subject.otherprofessional learning
dc.subject.othersimulation
dc.subject.othervirtual reality
dc.titleCapturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202305102930
dc.contributor.laitosKasvatustieteiden laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosKoulutuksen tutkimuslaitosfi
dc.contributor.laitosKasvatustieteiden ja psykologian tiedekuntafi
dc.contributor.laitosDepartment of Educationen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.laitosFinnish Institute for Educational Researchen
dc.contributor.laitosFaculty of Education and Psychologyen
dc.contributor.oppiaineResurssiviisausyhteisöfi
dc.contributor.oppiaineKasvatustiedefi
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningfi
dc.contributor.oppiaineMonitieteinen oppimisen ja opetuksen tutkimusfi
dc.contributor.oppiaineKoulutusteknologia ja kognitiotiedefi
dc.contributor.oppiaineTekniikkafi
dc.contributor.oppiaineDigitalization in and for learning and interactionfi
dc.contributor.oppiaineSchool of Resource Wisdomen
dc.contributor.oppiaineEducationen
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningen
dc.contributor.oppiaineMultidisciplinary research on learning and teachingen
dc.contributor.oppiaineLearning and Cognitive Sciencesen
dc.contributor.oppiaineEngineeringen
dc.contributor.oppiaineDigitalization in and for learning and interactionen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1553-1563
dc.relation.issn0266-4909
dc.relation.numberinseries5
dc.relation.volume39
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 The Authors. Journal of Computer Assisted Learning published by John Wiley & Sons Ltd
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber331817
dc.relation.grantnumber311877
dc.relation.grantnumber292466
dc.relation.grantnumber318905
dc.subject.ysokäyttäytyminen
dc.subject.ysopsyykkinen kuormittavuus
dc.subject.ysofysiologiset vaikutukset
dc.subject.ysovirtuaalitodellisuus
dc.subject.ysomittarit (mittaus)
dc.subject.ysolentäminen
dc.subject.ysokuormitus
dc.subject.ysosimulointi
dc.subject.ysolentäjät
dc.subject.ysotietokoneavusteinen oppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3625
jyx.subject.urihttp://www.yso.fi/onto/yso/p131
jyx.subject.urihttp://www.yso.fi/onto/yso/p11511
jyx.subject.urihttp://www.yso.fi/onto/yso/p7990
jyx.subject.urihttp://www.yso.fi/onto/yso/p21210
jyx.subject.urihttp://www.yso.fi/onto/yso/p16230
jyx.subject.urihttp://www.yso.fi/onto/yso/p17226
jyx.subject.urihttp://www.yso.fi/onto/yso/p4787
jyx.subject.urihttp://www.yso.fi/onto/yso/p15638
jyx.subject.urihttp://www.yso.fi/onto/yso/p7221
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1111/jcal.12817
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundinginformationThe work was supported by the Academy of Finland under Grant numbers 292466, 311877, 318905, and 331817.
dc.type.okmA1


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot

CC BY 4.0
Ellei muuten mainita, aineiston lisenssi on CC BY 4.0