{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140613"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140613","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Collaborative Multimodal XR-based Training Environments for Collocated Medical Teams","abstract":"Collaborative Extended Reality (XR) systems hold growing promise as training platforms in domains that demand high levels of coordination, communication, and shared situational awareness. However, the current landscape of XR-based training tools remains predominantly focused on individual skill development, with limited support for realistic, synchronous collaboration among physically collocated teams. Furthermore, existing evaluation methods often rely on individual performance metrics and fail to capture the nuanced dynamics of teamwork. This dissertation addresses these critical gaps by proposing both design and evaluation frameworks tailored for collaborative XR training environments. The research is structured around two core research questions. First, it investigates how XR training systems can be designed to support realistic, high-fidelity collaboration among collocated professional teams. Through naturalistic observations, stakeholder interviews, and iterative prototyping, the study identifies key design factors and formulates a set of principles that inform the development of a task-driven XR training simulator. Second, it introduces a theoretically grounded, multimodal, user-centered evaluation evaluation based on the Distributed Cognition Theory. This framework integrates behavioral, communicative, and perceptual data to assess team-level performance in XR, extending beyond traditional task metrics to include communication flow, role coordination, and temporal organization. Together, the design and evaluation components contribute to a robust methodological pipeline for advancing Collaborative XR systems. The work not only bridges theoretical and practical gaps in XR training but also lays the groundwork for scalable, evidence-based tools that better reflect the realities of team-based performance in complex environments. Through these contributions, the dissertation advances the state of the art in collaborative immersive training and supports the development of next-generation XR platforms for real-world readiness.","abstract_html":"Collaborative Extended Reality (XR) systems hold growing promise as training platforms in domains that demand high levels of coordination, communication, and shared situational awareness. However, the current landscape of XR-based training tools remains predominantly focused on individual skill development, with limited support for realistic, synchronous collaboration among physically collocated teams. Furthermore, existing evaluation methods often rely on individual performance metrics and fail to capture the nuanced dynamics of teamwork. This dissertation addresses these critical gaps by proposing both design and evaluation frameworks tailored for collaborative XR training environments. The research is structured around two core research questions. First, it investigates how XR training systems can be designed to support realistic, high-fidelity collaboration among collocated professional teams. Through naturalistic observations, stakeholder interviews, and iterative prototyping, the study identifies key design factors and formulates a set of principles that inform the development of a task-driven XR training simulator. Second, it introduces a theoretically grounded, multimodal, user-centered evaluation evaluation based on the Distributed Cognition Theory. This framework integrates behavioral, communicative, and perceptual data to assess team-level performance in XR, extending beyond traditional task metrics to include communication flow, role coordination, and temporal organization. Together, the design and evaluation components contribute to a robust methodological pipeline for advancing Collaborative XR systems. The work not only bridges theoretical and practical gaps in XR training but also lays the groundwork for scalable, evidence-based tools that better reflect the realities of team-based performance in complex environments. Through these contributions, the dissertation advances the state of the art in collaborative immersive training and supports the development of next-generation XR platforms for real-world readiness.","abstract_has_math":false,"creators":["Donekal Chandrashekar, Nikitha"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Computer Science & Applications","degree_department":"Computer Science and#38; Applications","school":null,"contributors":[],"advisors":[],"committee_chairs":["Gracanin, Denis"],"committee_members":["Lee, Sang Won","David-John, Brendan Matthew","Muniyandi, Manivannan","Safford, Shawn D."],"year":2026,"date_issued":"2026-01-06","date_published":"2026-01-06","updated_at":"2026-07-22T22:19:31Z","subjects":["Extended Reality","Collaboration","Medical Team Training","Multimodality"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45507"],"render_values":[{"text":"vt_gsexam:45507","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140613","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Gracanin, Denis"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lee, Sang Won","David-John, Brendan Matthew","Muniyandi, Manivannan","Safford, Shawn D."]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science and#38; Applications"]},{"key":"dc:creator","label":"Author","values":["Donekal Chandrashekar, Nikitha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-07T09:00:52Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-07T09:00:52Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-06"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Extended Reality","Collaboration","Medical Team Training","Multimodality"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45507"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140613"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Collaborative Extended Reality (XR) systems hold growing promise as training platforms in domains that demand high levels of coordination, communication, and shared situational awareness. However, the current landscape of XR-based training tools remains predominantly focused on individual skill development, with limited support for realistic, synchronous collaboration among physically collocated teams. Furthermore, existing evaluation methods often rely on individual performance metrics and fail to capture the nuanced dynamics of teamwork. This dissertation addresses these critical gaps by proposing both design and evaluation frameworks tailored for collaborative XR training environments. The research is structured around two core research questions. First, it investigates how XR training systems can be designed to support realistic, high-fidelity collaboration among collocated professional teams. Through naturalistic observations, stakeholder interviews, and iterative prototyping, the study identifies key design factors and formulates a set of principles that inform the development of a task-driven XR training simulator. Second, it introduces a theoretically grounded, multimodal, user-centered evaluation evaluation based on the Distributed Cognition Theory. This framework integrates behavioral, communicative, and perceptual data to assess team-level performance in XR, extending beyond traditional task metrics to include communication flow, role coordination, and temporal organization. Together, the design and evaluation components contribute to a robust methodological pipeline for advancing Collaborative XR systems. The work not only bridges theoretical and practical gaps in XR training but also lays the groundwork for scalable, evidence-based tools that better reflect the realities of team-based performance in complex environments. Through these contributions, the dissertation advances the state of the art in collaborative immersive training and supports the development of next-generation XR platforms for real-world readiness."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Extended Reality (XR) technologies are becoming powerful tools for professional training in fields like medicine, emergency response, and technical operations. These immersive systems allow people to practice complex tasks in realistic, risk-free environments. However, most XR training tools today are built to train individuals, not teams. In the real world, professionals often work in groups where success depends not just on technical ability, but on how effectively team members communicate, collaborate, and make decisions together in real time. This research aims to make XR training more collaborative and more realistic for teams who work physically together—what we call collocated teams. The first goal is to design XR environments that support natural teamwork by enabling team members to interact, coordinate, and share information as they would in real-life scenarios. This includes understanding what makes teamwork effective and turning those insights into design guidelines for building better XR simulators. The second goal is to develop smarter ways to evaluate the collaborative XR settings through user data. Most current evaluation tools only measure individual performance, which misses key teamwork behaviors like coordination, leadership, or shared awareness. This research proposes a new framework for evaluating collaborative XR systems and the teams training in it by combining different kinds of data like communication, movement, and user performance to get a better picture of how well does the developed system support the team training. The outcomes of this work will help create XR training systems that are not only more effective but also more aligned with the realities of how professionals operate in high-pressure environments. Overall this research can lead to better team preparedness, improved outcomes, and enhanced safety across critical industries that rely on expert collaboration."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Collaborative Multimodal XR-based Training Environments for Collocated Medical Teams"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Gracanin, Denis"],"dc:contributor.committeemember":["Lee, Sang Won","David-John, Brendan Matthew","Muniyandi, Manivannan","Safford, Shawn D."],"dc:contributor.department":["Computer Science and#38; Applications"],"dc:creator":["Donekal Chandrashekar, Nikitha"],"dc:date.accessioned":["2026-01-07T09:00:52Z"],"dc:date.available":["2026-01-07T09:00:52Z"],"dc:date.issued":["2026-01-06"],"dc:description.abstract":["Collaborative Extended Reality (XR) systems hold growing promise as training platforms in domains that demand high levels of coordination, communication, and shared situational awareness. However, the current landscape of XR-based training tools remains predominantly focused on individual skill development, with limited support for realistic, synchronous collaboration among physically collocated teams. Furthermore, existing evaluation methods often rely on individual performance metrics and fail to capture the nuanced dynamics of teamwork. This dissertation addresses these critical gaps by proposing both design and evaluation frameworks tailored for collaborative XR training environments. The research is structured around two core research questions. First, it investigates how XR training systems can be designed to support realistic, high-fidelity collaboration among collocated professional teams. Through naturalistic observations, stakeholder interviews, and iterative prototyping, the study identifies key design factors and formulates a set of principles that inform the development of a task-driven XR training simulator. Second, it introduces a theoretically grounded, multimodal, user-centered evaluation evaluation based on the Distributed Cognition Theory. This framework integrates behavioral, communicative, and perceptual data to assess team-level performance in XR, extending beyond traditional task metrics to include communication flow, role coordination, and temporal organization. Together, the design and evaluation components contribute to a robust methodological pipeline for advancing Collaborative XR systems. The work not only bridges theoretical and practical gaps in XR training but also lays the groundwork for scalable, evidence-based tools that better reflect the realities of team-based performance in complex environments. Through these contributions, the dissertation advances the state of the art in collaborative immersive training and supports the development of next-generation XR platforms for real-world readiness."],"dc:description.abstractgeneral":["Extended Reality (XR) technologies are becoming powerful tools for professional training in fields like medicine, emergency response, and technical operations. These immersive systems allow people to practice complex tasks in realistic, risk-free environments. However, most XR training tools today are built to train individuals, not teams. In the real world, professionals often work in groups where success depends not just on technical ability, but on how effectively team members communicate, collaborate, and make decisions together in real time. This research aims to make XR training more collaborative and more realistic for teams who work physically together—what we call collocated teams. The first goal is to design XR environments that support natural teamwork by enabling team members to interact, coordinate, and share information as they would in real-life scenarios. This includes understanding what makes teamwork effective and turning those insights into design guidelines for building better XR simulators. The second goal is to develop smarter ways to evaluate the collaborative XR settings through user data. Most current evaluation tools only measure individual performance, which misses key teamwork behaviors like coordination, leadership, or shared awareness. This research proposes a new framework for evaluating collaborative XR systems and the teams training in it by combining different kinds of data like communication, movement, and user performance to get a better picture of how well does the developed system support the team training. The outcomes of this work will help create XR training systems that are not only more effective but also more aligned with the realities of how professionals operate in high-pressure environments. Overall this research can lead to better team preparedness, improved outcomes, and enhanced safety across critical industries that rely on expert collaboration."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45507"],"dc:identifier.uri":["https://hdl.handle.net/10919/140613"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Extended Reality","Collaboration","Medical Team Training","Multimodality"],"dc:title":["Collaborative Multimodal XR-based Training Environments for Collocated Medical Teams"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Computer Science & Applications"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:31Z"}