Learning through Simulation in Undergraduate Nurse Education – It’s All About the Action. Data Collection & Analysis Using Video to Explore the Visual and the Narrative.

Simulation has over the last decade become an established feature of undergraduate nurse education in many countries and as such requires educators to become familiar with its attributes in the context of student learning. Research limited to the pre-constructed categories imposed by some questionnaire and interview methods can offer accounts of subjective experience but may only provide partial understanding. This is problematic in understanding the mechanisms of learning in simulation-based education as contemporary distributed theories of learning posit that learning can be understood as the interaction of individual identity with context.

To better understand the learning process inherent in simulation we need data which enables the capture of the interaction of individuals within the simulation experience.

This paper details a method of data collection and analysis using video to access students understanding of their learning. The paper will present extracts of the data (including video data) to illustrate how this method offers so much more than narrative data alone. The study adopts a grounded theory approach (Glaser and Strauss, 1967) to explore how students learn through simulation-based education. Video recordings were used as a ‘show and tell’ approach to enable researcher and participants to unpack what is happening as it unfolds in action.

This approach to data collection and analysis has a particular relevance to both simulation-based education and grounded theory. Simulation in nursing is often constructed in groups and grounded theory has its foundations in symbolic interactionism which interrogates the relationship between individuals and their interactions with others. By capturing student actions and interaction on video the simulation exercise can be analysed through multiple lenses including context, interaction and importantly through the lens of both the researcher and learner. Thus the method provides multiple levels of dynamic data to address the complexity of learning in simulation-based education.