Developing a multi-method approach to data collection and analysis for explaining the learning during simulation in undergraduate nurse education

Simulation has become an established feature of undergraduate nurse education and as such requires extensive investigation. Research limited to pre-constructed categories imposed by some questionnaire and interview methods 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. This paper details a method of data collection and analysis that captures interaction of individuals within the simulation experience which can be analysed through multiple lenses, including context and through the lens of both researcher and learner. The study utilised a grounded theory approach involving 31 under-graduate third year student nurses. Data was collected and analysed through non-participant observation, digital recordings of simulation activity and focus group deconstruction of their recorded simulation by the participants and researcher. Focus group interviews enabled further clarification. The method revealed multiple levels of dynamic data, concluding that in order to better understand how students learn in social and active learning strategies, dynamic data is required enabling researchers and participants to unpack what is happening as it unfolds in action.