Promoting Expertise Through Simulation (PETS): A conceptual framework
Learning and Instruction, ISSN: 0959-4752, Vol: 82, Page: 101686
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Questioning how and why simulations can be suitable for supporting intraindividual learning and expertise development motivated this study. We depart from an empirically well supported cognitive perspective of expertise that focusses on cognitive adaptations through long-term engagement with professional work activities. The strength of simulation learning is seen in its multiple explanatory base, which is used to build the framework PETS (Promoting Expertise Through Simulation). The PETS model specifically addresses how simulations can contribute to intraindividual knowledge restructuring through case processing and learner-tailored guidance of a trainer supporting the thorough engagement in deliberate practice activities to go beyond automatization. Preparation, briefing, repetitive practice opportunities, and debriefing are phases of instructional embedding in simulation learning for which the PETS model identifies important prerequisites to enhance understanding of what works for whom under which conditions and why during expertise development.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0959475222001074; http://dx.doi.org/10.1016/j.learninstruc.2022.101686; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85138167747&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0959475222001074; https://dx.doi.org/10.1016/j.learninstruc.2022.101686
Elsevier BV
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