skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on June 10, 2026

Title: Team-Based Dynamics of Diagnosis and Intervention in a VR Simulation: Towards a Reasoning Process Model
Accounting neglect of intervention reasoning in CSCL research, we propose a new process model for team-based diagnostic and intervention reasoning in acute care, focusing on interactions among diagnostic activities (DAs), intervention activities (IAs), and collaborative activities (CAs) such as joint information processing, coordination, and communication. Using epistemic network analysis, we analyzed data from a VR-based cardiac arrest simulation to validate this model by comparing expert- and novice-led teams. As expected, expert-led teams demonstrated faster, more cohesive transitions between DAs and IAs, with a streamlined, linear CA pattern, while novice-led teams exhibited slower, fragmented transitions with cyclical CA patterns. These findings support the model’s potential to capture expertise-driven coordination and efficiency in high-stakes settings. Future research may expand this model across diverse team compositions and problem contexts. By refining understanding of acute care team dynamics, this model paves the way for instructional strategies enhancing coordination and performance in collaborative problem solving.  more » « less
Award ID(s):
2202451
PAR ID:
10603865
Author(s) / Creator(s):
; ; ;
Editor(s):
Oshima, J; Chen, B; Vogel, F; Järvelä, J
Publisher / Repository:
Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning - CSCL 2025. Helsinki, Finland: International Society of the Learning Sciences.
Date Published:
ISSN:
2543-0157
ISBN:
979-8-9906980-4-8
Format(s):
Medium: X
Location:
Helsinki, Finland
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, School Psychology Review)
    null (Ed.)
    The primary purpose of this study was to systematically review the literature regarding the characteristics, use, and implementation of an emerging assessment methodology, SRL microanalysis. Forty-two studies across diverse samples, contexts, and research methodologies met inclusion criteria. The majority of studies used microanalysis to either comprehensively address all three phases of SRL (i.e., forethought, performance, or reflection) or to conduct in-depth analyses of one particular phase. Microanalysis has also been used across myriad domains (e.g., academic, athletic, clinical) and tasks (e.g., mathematics problem solving, basketball shooting, diagnostic reasoning) with samples encompassing elementary to graduate school. Although SRL microanalysis has typically been used to differentiate intervention conditions or existing groups (e.g., expert vs. novice), it has increasingly been used as a diagnostic tool to inform instructional and intervention planning. Additional information regarding the types of validity addressed in the studies are discussed, as well as implications for research and school practice. 
    more » « less
  2. ; ; (, Journal of expertise)
    Anders Ericsson’s seminal research on expert performance spurred a number of streams of research across psychological disciplines. Though his work was primarily focused on expert individual performance, there has been increasing interest over the past several decades on the factors underlying expert teamwork. This paper advances eight principles of expert team performance based on decades of team science research: shared mental models, learning and adaptation, role clarity, shared vision, dynamic leadership, psychological safety, cooperation and coordination, and resilience. In addition, we review a number of team development interventions aimed at building team expertise including team training, simulation, coaching, and debriefing. Accordingly, this paper is divided into three sections addressing (1) how expert teams perform, (2) interventions to develop expert team performance, and (3) a reflection on the role Anders Ericsson’s work has played in team science, including a personal reflection from Eduardo Salas on deliberate and guided practice. 
    more » « less
  3. ; ; (, ASEE Conferences)
    Team building activities are popular interventions during early stages of team development. At RIT, in the multidisciplinary capstone course with an average cohort size of around 350, the students on a particular capstone project team may not be mutually acquainted and thus may benefit from such team building activities. Prior literature has studied the effectiveness of various instructor-directed team building activities on student teams. However, our students are generally eager to spend class time working on their projects and often see in-class activities as a distraction rather than an important part of their growth. Instead, the student teams are now allowed to choose an intervention based on team consensus. In this paper, the relationship between attributes of the chosen intervention and student performance, as measured using a series of AACU VALUE rubrics, was studied using statistical measures. The analysis revealed a statistically significant effect of type of team building activity on teamwork, oral communication, and design & problem solving scores of individual students on the team. Also, a statistically significant effect of location of team building activity (on or off campus) on design & problem solving score was observed. 
    more » « less
  4. ; ; ; (, Journal of Computer Assisted Learning)
    Abstract Computational thinking (CT), the ability to devise computational solutions for real‐life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence through collaborative problem solving remains unclear. This study adopted an innovative approach, quantitative ethnography, to analyze the collaborative programming activities of a high‐performing and a low‐performing team. Both the discourse analysis and epistemic network models revealed that across concepts, practices, and identity, the high‐performing team exhibited CT that was systematic, whereas the CT of the low‐performing team was characterized by tinkering or guess‐and‐check approaches. However, the low‐performing group's CT development trajectory ultimately converged towards the high‐performing group's. This study thus improves understanding of how novices learn CT, and it illustrates a useful method for modeling CT based in authentic problem‐solving contexts. 
    more » « less
  5. ; ; ; ; (, Health Systems)
    Healthcare spending in the United States is concentrated on a small percentage of individuals, with 5% of the population accounting for 50% of annual spending. Many patients among the top 5% of spenders have complex health and social needs. Care coordination interventions, often led by a multidisciplinary team consisting of nurses, community health workers and social workers, are one strategy for addressing the challenges facing such patients. Care teams strive to improve health outcomes by forging strong relationships with clients, visiting them on a regular basis, reconciling medications, arranging primary and speciality care visits, and addressing social needs such as housing instability, unemployment and insurance. In this paper, we propose a simulation algorithm that samples longitudinal patient-level encounter histories to estimate the staffing needs for a multidisciplinary care team. Our numerical results illustrate multiple uses of the algorithm for staffing under stationary and non-stationary patient enrollment rates. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email