Analyzing Patterns of Participant Behavior in Integrated and Distributed Simulation Using Semi-Structured Learning Design (SSLD) Statements

Introduction: Although simulation has normalized around single mannequin team scenarios, there are many other forms. The authors have in recent years been exploring the possibilities of new web technologies connecting previously isolated simulators to provide innovative structured learning environments. Two key research questions have been tackled by the HSVO Project: (1) how do one or more groups of teachers and learners behave when using multiple simulators in a single educational activity and (2) how do they behave when participating in multi-site synchronous simulation activities? Methods: A series of ten different sessions were run involving learners from four geographically distant medical schools. Scenarios were developed that used multiple simulators (virtual patients, mannequins and part task trainers) in different combinations (single group, site-specific groups, cross-site groups, etc) to be run concurrently across two sites at a time. Each session involved a different scenario or a different configuration of a scenario. A web-based platform was developed to support the execution of multiple device and multiple location scenarios1. This study was passed by the ethics boards (or equivalent) of all participating institutions. Groups of learners at each site were recruited through the local research lead. Each session was defined using a structured script called a semi-structured learning design statement (SSLD). Video recordings were made of all sessions. Participants were also given a pre-session evaluation to gauge their prior experience along with a post-session evaluation of their experiences. Results: the video recordings were selected as the primary source of analysis. The mean session duration was 90 minutes and the median 76 minutes. The mean size of a learner group (at a single site) was 4.9 and the median 4. Prior experience and attitude was relatively normalized within a site but variant between sites. Analysis: A set of codes representing different events and behaviors was developed from an initial review by two coders and refined through a first round analysis of all 10 videos. Coding involved watching each video and recording the observed behaviors and events in two-minute segments. Codes were then grouped under three categories: engagement, communication and environment. The coder pool was expanded to 5 and a second pass of coding was conducted. This yielded a reasonably congruent model of key events and behaviors in each session. At the same time, a time-based sequence of each key event in the session was recorded and compared to the planned sequence and timing in the corresponding SSLD. Discussion: technical issues did not prove to be significant inhibitors; indeed several tutors turned such events into opportunities for further teaching and reflection. Scenarios and sessions that involved more active learning led to greater reflection and engagement amongst the learners. Using more simulators also led to higher level thinking about the nature of practice and their preparation for it. All scenarios where adapted and extemporized with more time given over to briefing and orientation and less to feedback than had originally been planned. The role of the tutor was identified as critical with those that took a more facilitative role proving more effective than those that took a didactic role. Analyses of this rich data set are ongoing. Conclusions: this is a pilot study investigating a new and complex area of simulation-based education and, as such, all findings should be considered provisional. Nevertheless the study has developed new simulation techniques as well as tools and methodologies to explore them. While some findings are not particularly surprising (active learning is better than passive), others show greater promise for further exploration, in particular reflective opportunities and the ways that designs are improvised on in practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.