Live Sims for First Responder Training

1. Live Sims for First Responder Training Brenda Bannan, PhD Associate Professor Division of Learning Technologies College of Education and Human Development NIST Global City Teams Challenge Action Cluster Lead and Public Safety Supercluster Executive Team This material is based upon work supported by the National Science Foundation under Grant No. DRL-1637263 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

2. Real world problem • Medical and emergency response teams are required to quickly comprehend a complex array of factors including time, information for situational awareness, coordination of team/individual actions, as well as manage physiological stress, any of which can impair performance in high stakes situations. • (Arora, Sevdalis, Nestel, Woloshynowych, Darzi & Kneebone, 2010). • Serious medical errors are more likely to occur, particularly at points of transitions between teams and team members • (Fletcher, Bedwell, Rosen, Catcople &Lazarra, 2014)

3. Simulation-Based Team Training Learning in team-based simulations is constrained to post-simulation reflection, thus limiting a more comprehensive understanding of team- performance and learning Simulations offer participants an opportunity to both practice and reflect together ( Sawyer et al., 2013) Simulations viewed as a strategy to support team- learning without harming patients ( Eppich, et al., 2011) Team-Based Performance in Healthcare is Complex & Dynamic ( Weaver et al., 2010)

4. Prototype & Research for a Connected Future

5. Analytics

7. Multiteam System (MTS) Definition “Two or more teams that interface directly and interdependently… While pursuing different proximal goals, [teams] share at least one common distal goal.” (Mathieu, Marks, & Zaccaro, 2001, p. 290) Individual MTS Team MTS Individual Team 7

8. Next-Gen Distance Learning – Systems Thinking • Multiteam Systems – Learning and Performance Across the System Team Stakeholders: - 911 Dispatcher - EOC - Fire & Rescue - EMS - ED/Trauma Team Stakeholders: - Citizens on scene - 911 Dispatcher - EOC - Fire & Rescue - EMS Team Stakeholders: - EMS Team Stakeholders: - EMS - ED/Trauma

11. Learning theory and sensor-based data analytics • How to support learning and training in this context? • How to support team coordination within and across teams in Fire & Rescue contexts? • Ideas from theory and practice • Reflection-in-action and Knowing-in-action • Reflection-on-action • Revise mental models for future beneficial actions • Adaptive expertise • Situation awareness • Team and Multiteam System coordination • Leadership switching • Stress

12. Human-Centered Design Prototype & Research Cycles • Designing beyond the classroom • Wearable devices for learning • Tracing user activity and experience with connected devices

13. Prototyping Cyber-Physical Systems Now & Future Currently Design for and with: • Contextual awareness • Mobile behavioral analytics • Real-time processing of data • Multimodal and multiple data sources • Visualization of behavior tracing Eventual consideration of: • Ambient intelligence • Sensitive and responsive to people • Spaces in-between devices • Invisibility of embedded devices • Monitor and learn behavior • Adaptive • Machine learning • Artificial intelligence

14. Design for Complex Sociotechnical Systems • Operational • Physical setting of use • Behavioral • Human activity • States of being • Ecological • Networks of relationships • Sociocultural • Shared ways of interpreting the world Rowland, C., Goodman, E., Charlier, M., Light, A. & Lui, A. (2015). Designing connected products: UX for the consumer internet of things. Sebastopol, CA: O’Reilly.

15. Design for Workplace • Place-based Learning • Experiential Learning • In-situ

16. Prototype System for Sensor-based Activity Tracing

17. Design with & for Community

18. Design for Challenging Contexts “Design can investigate and intervene at the interface between people, technologies and the city – developing research and applications that empower citizens to make choices that result in a more livable urban condition.” Carlo Ratti

19. Design of Data Analytics for Other Contexts

20. Analyze distributed work & learning ecosystems

21. Prototyping System - Integrated Data Streams

22. Post event - IoT Proximity Beacon Data

23. Data driven behavior monitoring – HR & Accelerometer

24. 2a) PH1 PH2 PH3 ED1 ED2 ED3 ED4 ED5 0:30 1:00 0:00 2b) PH3 3m 2m ED2 1m PH2 0m ED3 1:00 0:00 0:30 24

25. Real-time team and individual activity tracing Firefighter Suppression Team (blue) and Emergency Medical Services Team (red) proximity to EMS Medic (wearing listening device) visualized in near real-time on-scene.

26. Some Lessons Learned and Challenges • Theory-driven data collection • Defining key moments between teams – inflection points • Sensor data as Proxy data • Heart rate indicates stress • Physical proximity of team members as team coordination • Triangulation of data sources • Wireless connectivity in real world conditions • Latency between sensor and listening device • Data integrity, validity and reliability • Video analysis supporting conclusions from sensor data • Synchronizing data sources – timing and sequence of interactions • Data integration for learning analytics • Meaningful visualization in near real-time • Ethics, trust and privacy • Design for connected devices – complex, challenging, community settings

27. Design for the point of need

28. Designing for Smart Fire Fighting Source: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1191.pdf

29. Considerations for Prototyping and Research for Training • Sensor-based interventions in cities, services and environments • Highlight patterns and relationships in near real-time • Consideration of Universal Design for Learning (UDL) from the start • Multiple modes of representation – flexible designs and data visualizations • Privacy, security and personalization intersect • System, not individual level controls • Aggregated data - opt-in strategies, selection of services • Customizing displays of information • Promoting environmental accessibility and learning • Visual and hearing impaired – operate with autonomy-contextual sensing • Optimizing access • Expand types of feedback – haptic, auditory, etc. • Participatory design opportunities in UX design research • Optimize relevance, value and authenticity http://www.udlcenter.org/sites/udlcenter.org/files/updateguidelines2_0.pdf Thurston & Pineta (2016). Smart Cities for All: A Vision for an Inclusive, Accessible Urban Future. Retreived from http://smartcities4all.org/

30. Future of Fire and Rescue Training? • Learning analytics and behavioral tracing combined • In the box and out of the box activity • Immersive contexts • Workplace-based and remote • Sensor-based data integration • Mine for behavior and communication patterns • Ambient intelligent systems that learn

31. Designing for Connected Learning Ecosystems Striving Toward Just in time, in place and just enough information/analytics to support learning • accurately capturing and making sense of participant, team and cross-team learning • evidence for assessing and tracking learning becomes heterogeneous and complex rather than general across individual students. • a great need to model learning in multiple aspects of participant growth and experiences, which can be applied across different learning activities and contexts • No single absolute order of progression as learning in Technology Rich Environments (TREs) • involves multiple interactions between individuals and situations, which may be too complex for most measurement theories in use that assume linearity and independence. • Clearly, theories of learning progressions in TREs need to be actively researched and validated to realize Technology Rich Environments’ potential. Dede, C. (2016). Data-Intensive Research in Education: Current Work and Next Steps. Computing Research Associated. Retreived from https://cra.org/cra-releases-report-on-data-intensive-research-in-education/

32. Design Research for Technology Rich Environments “Design can become an operative mechanism for crowd sourcing the future based on mutation and selection. By soliciting ideas, response and action from citizens, we hope that design can move society toward the most desirable outcome...” Ratti, C. & Claudel, M. (2016). The city of tomorrow: Sensors, Networks, Hackers, and the future of urban life. New Haven, CT: Yale University Press

33. Thank You! Dr. Brenda Bannan @BrendaBannan Research team members: Dr. Nathalia Peixoto Dr. Hemant Purohit Dr. Stephen Zaccaro Samantha Dubrow Christian Dobbins Jeff Segal Mohammad Ranna Michael Au Lucas Delgado Gonzales Tristan Ritland Fairfax Fire & Rescue Department Inova Fairfax Medical Center Advanced Surgical Technology Education Center (ASTEC)