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Simulation-Based Education and Nursing Student Learning Motivations: A Scoping Review
Keisuke Nojima1, Ryosuke Yoshida2, Seiichi Sato1
1Faculty of Nursing, Kyoto Tachibana University, Kyoto, JPN.
Abstract:
This scoping review aimed to explore how simulation-based learning (SBL) influences learning motivation among undergraduate nursing students and to clarify the factors within SBL that contribute to increased motivation. A scoping review methodology was employed following the Joanna Briggs Institute (JBI) guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting standards. Literature published between January 2014 and November 2024 was systematically searched across MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Education Resources Information Center (ERIC), and PubMed using relevant keywords based on the population-concept-context (PCC) framework. Studies were selected and reviewed by multiple independent reviewers, and data were extracted and analyzed thematically using an inductive approach. Twenty-one studies were included. SBL was found to positively influence nursing students' intrinsic motivation, self-efficacy, critical thinking, and academic engagement. Simulation modalities included high-fidelity simulation, virtual reality, video-based learning, and standardized patients. Key motivational factors identified were realism in clinical scenarios, opportunities for repetition and reflection, structured debriefing, and emotionally engaging learning environments. Integration with problem-based learning and immersive technologies further enhanced learner engagement. However, challenges such as high implementation costs, the need for trained facilitators, and limited adaptability to diverse learner needs were noted. This review offers insight into how SBL strategies can be optimized to support both cognitive and emotional aspects of learning motivation in nursing education and contributes to the literature by systematically mapping motivational factors across diverse simulation modalities.
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