Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/47528
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGEURTS, Eva-
dc.contributor.authorVERSTRAETE, Arno-
dc.contributor.authorWIJNANTS, Maarten-
dc.date.accessioned2025-10-14T13:25:09Z-
dc.date.available2025-10-14T13:25:09Z-
dc.date.issued2025-
dc.date.submitted2025-10-03T16:26:33Z-
dc.identifier.citationElsevier, p. 2676 -2681-
dc.identifier.issn2405-8963-
dc.identifier.urihttp://hdl.handle.net/1942/47528-
dc.description.abstractVirtual Reality (VR) provides unique opportunities for creating immersive, per-sonalized and responsive learning environments through advanced features like hand and eye tracking. However, traditional VR training often lacks the flexibility to accommodate diverse learning styles. Personalization of training is crucial in industrial assembly, where user profiles and levels of expertise vary greatly. This paper introduces a novel solution for adaptive learning in VR focused on assembly knowledge training, using hand and eye tracking to deliver real-time feedback and individually adjusted learning paths. We applied our approach to two realistic assembly cases to evaluate its practical application. We hope to inspire future research further to explore and refine this adaptive approach, contributing to developing more flexible and effective VR-based training solutions for the manufacturing industry.-
dc.description.sponsorshipThisresearchwassupportedbyFlandersMake,thestrategicresearchcentreforthemanufacturingindustry, inthe projectSKILLEDWORKFORCE.-
dc.language.isoen-
dc.publisherElsevier-
dc.rights2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subject.otherIndustry 40-
dc.subject.otherIndustry 50-
dc.subject.otherVirtual Reality-
dc.subject.otherAssembly Training-
dc.subject.otherAdaptive Learning-
dc.titleEmpowering Adaptive Learning in VR Assembly Training Using Real-time Performance Tracking-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencedate2025, June 30-July 3-
local.bibliographicCitation.conferencename11th IFAC Conference on Manufacturing Modeling, Management and Control-
local.bibliographicCitation.conferenceplaceTrondheim-
dc.identifier.epage2681-
dc.identifier.issue10-
dc.identifier.spage2676-
dc.identifier.volume59-
local.format.pages6-
local.bibliographicCitation.jcatC1-
dc.relation.referencesGe, Z., Xi, M., and Li, Y. (2019). A literature review of the adaptive algorithms adopted in adaptive learning systems. In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), 254–258. IEEE. Havard, V., Baudry, D., Jeanne, B., Louis, A., and Savatier, X. (2021). A use case study comparing augmented reality (ar) and electronic document-based maintenance instructions considering tasks complexity and operator competency level. Virtual Reality, 25(4), 999–1014. doi:10.1007/s10055-020-00493-z. Huang, G., Qian, X., Wang, T., Patel, F., Sreeram, M., Cao, Y., Ramani, K., and Quinn, A.J. (2021). Adap- TutAR: An adaptive tutoring system for machine tasks in augmented reality. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM. doi:10.1145/3411764.3445283. Jasche, F., Hoffmann, S., Ludwig, T., and Wulf, V. (2021). Comparison of different types of augmented reality visualizations for instructions. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI ’21. Association for Computing Machinery, New York, NY, USA. doi:10.1145/3411764.3445724. URL https://doi.org/10.1145/3411764.3445724. Liu, X.W., Li, C.Y., Dang, S., Wang, W., Qu, J., Chen, T., and Wang, Q.L. (2022). Research on training effectiveness of professional maintenance personnel based on virtual reality and augmented reality technology. Sustainability, 14(21), 14351. doi:10.3390/su142114351. Mayer, R.E. (2011). Applying the science of learning to multimedia instruction. In Psychology of learning and motivation, volume 55, 77–108. Elsevier. doi: https://doi.org/10.1016/B978-0-12-387691-1.00003-X. Moreno, R. and Mayer, R.E. (2002). Learning science in virtual reality multimedia environments: Role of methods and media. Journal of educational psychology, 94(3), 598. doi:10.1037/0022-0663.94.3.598. Palmas, F., Labode, D., Plecher, D.A., and Klinker, G. (2019). Comparison of a gamified and non-gamified virtual reality training assembly task. In 2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games). IEEE. doi: 10.1109/vs-games.2019.8864583. Strakos, J.K., Douglas, M.A., McCormick, B., andWright, M. (2023). A learning management system-based approach to assess learning outcomes in operations management courses. The International Journal of Management Education, 21(2), 100802. Studer, K., Lie, H., Zhao, Z., Thomson, B., Turakhia, D.G., and Liu, J. (2024). An open-ended system in virtual reality for training machining skills. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, CHI EA ’24. Association for Computing Machinery, New York, NY, USA. doi: 10.1145/3613905.3648666. Taylor, D.L., Yeung, M., and Bashet, A.Z. (2021). Personalized and Adaptive Learning, 17–34. Springer International Publishing, Cham. doi:10.1007/978-3-030-58948- 6 2. Ulmer, J., Braun, S., Cheng, C.T., Dowey, S., and Wollert, J. (2022). Gamification of virtual reality assembly training: Effects of a combined point and level system on motivation and training results. International Journal of Human-Computer Studies, 165, 102854. doi: 10.1016/j.ijhcs.2022.102854. Van Merrienboer, J.J. and Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review, 17, 147–177. Vincent-Ruz, P. and Boase, N.R.B. (2022). Activating discipline specific thinking with adaptive learning: A digital tool to enhance learning in chemistry. PLOS ONE, 17(11), 1–21. doi:10.1371/journal.pone.0276086. URL https://doi.org/10.1371/journal.pone.0276086. Vosinakis, S. and Koutsabasis, P. (2017). Evaluation of visual feedback techniques for virtual grasping with bare hands using leap motion and oculus rift. Virtual Reality, 22(1), 47–62. doi:10.1007/s10055-017-0313-4. Wolfartsberger, J., Zimmermann, R., Obermeier, G., and Niedermayr, D. (2023). Analyzing the potential of virtual reality-supported training for industrial assembly tasks. Computers in Industry, 147, 103838. doi: https://doi.org/10.1016/j.compind.2022.103838. Yu, J., Kim, H., Zheng, X., Li, Z., and Zhu, X. (2024). Effects of scaffolding and inner speech on learning motivation, flexible thinking and academic achievement in the technology-enhanced learning environment. Learning and Motivation, 86, 101982.-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.identifier.doi10.1016/j.ifacol.2025.09.450-
dc.identifier.isi001583825700449-
local.provider.typePdf-
local.uhasselt.internationalno-
item.fulltextWith Fulltext-
item.fullcitationGEURTS, Eva; VERSTRAETE, Arno & WIJNANTS, Maarten (2025) Empowering Adaptive Learning in VR Assembly Training Using Real-time Performance Tracking. In: Elsevier, p. 2676 -2681.-
item.contributorGEURTS, Eva-
item.contributorVERSTRAETE, Arno-
item.contributorWIJNANTS, Maarten-
item.accessRightsOpen Access-
crisitem.journal.issn2405-8963-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
published-version.pdfPublished version692.53 kBAdobe PDFView/Open
IFAC25__Using_Eye_and_Hand_tracking_to_personalize_assembly_training_in_VR_compressed.pdf
  Restricted Access		Peer-reviewed author version553.04 kBAdobe PDFView/Open    Request a copy
Show simple item record

SCOPUSTM   
Citations

1
checked on Jan 2, 2026

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.