An activity theory perspective of how scenario-based simulations support learning: a descriptive analysis
Tóm tắt
The dominant frameworks for describing how simulations support learning emphasize increasing access to structured practice and the provision of feedback which are commonly associated with skills-based simulations. By contrast, studies examining student participants’ experiences during scenario-based simulations suggest that learning may also occur through participation. However, studies directly examining student participation during scenario-based simulations are limited. This study examined the types of activities student participants engaged in during scenario-based simulations and then analyzed their patterns of activity to consider how participation may support learning. Drawing from Engeström’s first-, second-, and third-generation activity systems analysis, an in-depth descriptive analysis was conducted. The study drew from multiple qualitative methods, namely narrative, video, and activity systems analysis, to examine student participants’ activities and interaction patterns across four video-recorded simulations depicting common motivations for using scenario-based simulations (e.g., communication, critical patient management). The activity systems analysis revealed that student participants’ activities encompassed three clinically relevant categories, including (a) use of physical clinical tools and artifacts, (b) social interactions, and (c) performance of structured interventions. Role assignment influenced participants’ activities and the complexity of their engagement. Importantly, participants made sense of the clinical situation presented in the scenario by reflexively linking these three activities together. Specifically, student participants performed structured interventions, relying upon the use of physical tools, clinical artifacts, and social interactions together with interactions between students, standardized patients, and other simulated participants to achieve their goals. When multiple student participants were present, such as in a team-based scenario, they distributed the workload to achieve their goals. The findings suggest that student participants learned as they engaged in these scenario-based simulations when they worked to make sense of the patient’s clinical presentation. The findings may provide insight into how student participants’ meaning-making efforts are mediated by the cultural artifacts (e.g., physical clinical tools) they access, the social interactions they engage in, the structured interventions they perform, and the roles they are assigned. The findings also highlight the complex and emergent properties of scenario-based simulations as well as how activities are nested. Implications for learning, instructional design, and assessment are discussed.
Tài liệu tham khảo
Issenberg SB, et al. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10–28.
McGaghie WC, et al. A critical review of simulation-based medical education research: 2003-2009. Med Educ. 2010;44(1):50–63.
Kneebone R, et al. Blurring the boundaries: scenario-based simulation in a clinical setting. Med Educ. 2005;39(6):580–7.
Reznick RK, MacRae Teaching H. Surgical skills—changes in the wind. N Engl J Med. 2006;355(25):2664–9.
Jeffries P. A framework for designing, implementing, and evaluating simulations. Nurse Education Perspectives. 2005;26(2):97–104.
Alessi SM. Simulation design for training and assessment. Aircrew training and assessment. 2000:197–222.
Sanko JS, et al. Establishing a convention for acting in healthcare simulation: merging art and science. Simul Healthc. 2013;8(4):215–20.
Dieckmann P, Gaba D, Rall M. Deepening the theoretical foundations of patient simulation as social practice. Simul Healthc. 2007;2(3):183–93.
Kassab E, Tun JK, Kneebone RL. A novel approach to contextualized surgical simulation training. Simul Healthc. 2012;7(3):155–61.
Cook DA, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011;306(9):978–88.
Cant RP, Cooper SJ. Simulation-based learning in nurse education: systematic review. J Adv Nurs. 2010;66(1):3–15.
Fanning RM, Gaba DM. The role of debriefing in simulation-based learning. Simul Healthc. 2007;2(2):115–25.
Palaganas JC, Fey M, Simon R. Structured debriefing in simulation-based education. AACN Adv Crit Care. 2016;27(1):78–85.
Rudolph JW, et al. There's no such thing as “nonjudgmental” debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49–55.
Mikkelsen J, Reime MH, Harris AK. Nursing students’ learning of managing cross-infections—scenario-based simulation training versus study groups. Nurse Educ Today. 2008;28(6):664–71.
Lasater K. High-fidelity simulation and the development of clinical judgment: students’ experiences. J Nurs Educ. 2007;46(6)
Yamagata-Lynch LC. Understanding cultural historical activity theory. In: Activity systems analysis methods. New York: Springer; 2010. p. 13–26.
Engeström Y, et al. Activity theory and individual and social transformation. Perspectives on activity theory. Cambridge: Cambridge University Press; 1999. pp. 19–38.
Engeström Y. New forms of learning in co-configuration work. J Work Learn. 2004;16(1/2):11–21.
Engestrom, Y., Developmental studies of work as a testbench of activity theory: the case of primary care medical practice. Understanding practice: perspectives on activity and context, 1993.
Jonassen DH, Rohrer-Murphy L. Activity theory as a framework for designing constructivist learning environments. Educ Technol Res Dev. 1999;47(1):61–79.
Maxwell JA. Qualitative research design: an interactive approach, vol. 41. Thousand Oaks: Sage publications; 2012.
Derry SJ, et al. Conducting video research in the learning sciences: guidance on selection, analysis, technology, and ethics. J Learn Sci. 2010;19(1):3–53.
Ollerenshaw JA, Creswell JW. Narrative research: a comparison of two restorying data analysis approaches. Qual Inq. 2002;8(3):329–47.
Hindmarsh J, Tutt D. Video in analytic practice, Advances in visual methodology; 2012. p. 57–73.
Saldaña J. The coding manual for qualitative researchers. Thousand Oaks: Sage; 2015.
Strauss A, Corbin J. Open coding. Basics of qualitative research: grounded theory procedures and techniques. 1990;2(1990):101–21.
Lave J, Wenger E. Situated learning: legitimate peripheral participation. Cambridge: Cambridge university press; 1991.
Lioce L, et al. Standards of best practice: simulation standard III: participant objectives. Clinical Simulation in Nursing. 2013;9(6):S15–8.
Tobler K, Grant E, Marczinski C. Evaluation of the impact of a simulation-enhanced breaking bad news workshop in pediatrics. Simul Healthc. 2014;9(4):213–9.
Leighton K, Dubas J. Simulated death: an innovative approach to teaching end-of-life care. Clinical Simulation in Nursing. 2009;5(6):e223–30.
Barab SA, et al. Using activity theory to understand the systemic tensions characterizing a technology-rich introductory astronomy course. Mind Cult Act. 2002;9(2):76–107.
Hutchins E. The distributed cognition perspective on human interaction. Roots of human sociality: culture, cognition and interaction. 2006;1:375.
Nardi BA. Studying context: a comparison of activity theory, situated action models, and distributed cognition. Context and consciousness: Activity theory and human-computer interaction. Cambridge: MIT Press; 1996. pp. 69–102.