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On Extended Reality Objective Performance Metrics for Neurosurgical Training
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0001-5634-8960
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8543-3774
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Capio Spine Center Stockholm, Löwenströmska Hospital, Stockholm, Sweden.
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Capio Spine Center Stockholm, Löwenströmska Hospital, Stockholm, Sweden.
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2023 (English)In: Responsive and Sustainable Educational Futures: 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings, Springer Nature , 2023, p. 573-579Conference paper, Published paper (Refereed)
Abstract [en]

The adoption of Extended Reality (XR) technologies for supporting learning processes is an increasingly popular research topic for a wide variety of domains, including medical education. Currently, within this community, the metrics applied to quantify the potential impact these technologies have on procedural knowledge acquisition are inconsistent. This paper proposes a practical definition of standard metrics for the learning goals in the application of XR to surgical training. Their value in the context of previous research in neurosurgical training is also discussed. Objective metrics of performance include: spatial accuracy and precision, time-to-task completion, number of attempts. The objective definition of what the learner’s aims are enables the creation of comparable XR systems that track progress during training. The first impact is to provide a community-wide metric of progress that allows for consistent measurements. Furthermore, a measurable target opens the possibility for automated performance assessments with constructive feedback.

Place, publisher, year, edition, pages
Springer Nature , 2023. p. 573-579
Keywords [en]
Extended Reality, Neurosurgical Education, Performance Metrics, Procedural Knowledge, Surgical Simulation
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-337819DOI: 10.1007/978-3-031-42682-7_44Scopus ID: 2-s2.0-85172000439OAI: oai:DiVA.org:kth-337819DiVA, id: diva2:1803569
Conference
Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023, Aveiro, Portugal, Sep 4 2023 - Sep 8 2023
Note

Part of ISBN 9783031426810

QC 20231009

Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-10-12Bibliographically approved

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Iop, AlessandroViberg, OlgaRomero, Mario

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