Cognitive versus Technical Debriefing after Simulation Training

Bond, William F.; Deitrick, Lynn; Eberhardt, M.; Barr, Gavin C.; Kane, Bryan G; Worrilow, Charles C.; Arnold, Darryl C.; Croskerry, Pat

doi:10.1197/j.aem.2005.10.013

Cited by 36 publications

(11 citation statements)

References 33 publications

(31 reference statements)

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“…Debriefing can be accomplished through several methods based on various factors. Previous experimental studies mainly explore four factors including timing, grouping, technology, and facilitation and their relationships or combined effects on various dependent variables (Bond et al., 2006; Van der Meij et al., 2013; Van Heukelom et al., 2010). Different strategies based on these popular factors were applied and resulted in inconsistent results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies comparing different debriefing strategies are rare and their results are inconsistent, generally focusing on one type of factor such as different types of technology, grouping, or timing. Furthermore, in previous studies, the general aim was to explore the effect of different debriefing strategies on game performances and user beliefs about their learning achievements with surveys and questionnaires (Boet et al., 2011; Bond et al., 2006; Chronister & Brown, 2012; Grant, Moss, Epps, & Watts, 2010; Morgan et al., 2009). However, giving support for building higher game motivation and self-efficacy in game play beliefs are also essential objectives of debriefing.…”

Section: Introductionmentioning

confidence: 99%

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How Debriefing Strategies Can Improve Student Motivation and Self-Efficacy in Game-Based Learning

Bilgin

Baek

Park³

2015

Journal of Educational Computing Research

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This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at Hyungsung Park ShinGu University AbstractDebriefing is an important step in game-based learning environments. In the present study, the effect of different debriefing strategies in terms of two factors, grouping (self vs. team) and timing (in-game vs. post-game), was investigated on the motivation and self-efficacy levels of students. In a 2x2 ANOVA design, 62 sixth grade students were randomly assigned into two debriefing groups: self-debriefing and team debriefing. About half of members in each group performed either one of the two debriefing: in-game debriefing or post-game debriefing. Students in the self-debriefing as well as in the team-briefing group played the game three days a week over nine weeks. As students finished the task, motivation and self-efficacy scales were administered and semi-structured interviews were conducted. Findings indicate that students showed higher motivation and self-efficacy scores in the team debriefing than in the selfdebriefing. Moreover, the in-game debriefing group outperformed the post-game debriefing group in terms of self-efficacy and motivation levels. Semi-structured interviews supported the quantitative results that students benefited more from collaborative debriefing sessions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Debriefing Strategies Can Improve Student Motivation and Self-Efficacy in Game-Based Learning

Bilgin

Baek

Park³

2015

Journal of Educational Computing Research

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“…In the medical field, which also heavily uses simulation training, the literature points to Cognitive dispositions to respond (CDR) as patterns of thought that may lead to suboptimal decisions. These patterns have three components: heuristics, affective and emotional (Bond, Deitrick, Barr, Kane, Worrilow, Arnold & Croskerry, 2006). A debrief to identify these patterns as well as styles of thinking such as "thinking in silos," a vertical line failure, are essential in reducing decision errors.…”

Section: Application To Power Grid Critical Decision Trainingmentioning

confidence: 99%

Naturalistic Decision Making for Power System Operators

Greitzer

Podmore

Robinson

et al. 2010

International Journal of Human-Computer Interaction

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Motivation -Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three T's: Trees, Training and Tools. To document and understand the mental processes used by expert operators when making critical decisions, a naturalistic decision making (NDM) model was developed. Transcripts of conversations were analyzed to reveal and assess NDM-based performance criteria. Findings/Design -An item analysis indicated that the operators' Situation Awareness Levels, mental models, and mental simulations can be mapped at different points in the training scenario. This may identify improved training methods or analytical/ visualization tools. Originality/Value -This study applies for the first time, the concepts of Recognition Primed Decision Making, Situation Awareness Levels and Cognitive Task Analysis to training of electric power system operators. Take away message -The NDM approach provides a viable framework for systematic training management to accelerate learning in simulator-based training scenarios for power system operators and teams.

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“…There were 19 studies that used designs in which one approach to simulation-based education was compared with another (see Table 3). [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] Twelve of the 19 studies included prehospital providers as participants, and nine of these studies focused on airway management training. Because the research questions in these 19 studies varied substantially, we were unable to provide a quantitative synthesis of results.…”

Section: Comparison Between Alternate Types Of Technologyenhanced Simmentioning

confidence: 99%

Technology‐enhanced Simulation in Emergency Medicine: A Systematic Review and Meta‐Analysis

Ilgen

Sherbino

Cook

2013

Academic Emergency Medicine

160

122

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Objectives: Technology-enhanced simulation is used frequently in emergency medicine (EM) training programs. Evidence for its effectiveness, however, remains unclear. The objective of this study was to evaluate the effectiveness of technology-enhanced simulation for training in EM and identify instructional design features associated with improved outcomes by conducting a systematic review. Methods:The authors systematically searched MEDLINE, EMBASE, CINAHL, ERIC, PsychINFO, Scopus, key journals, and previous review bibliographies through May 2011. Original research articles in any language were selected if they compared simulation to no intervention or another educational activity for the purposes of training EM health professionals (including student and practicing physicians, midlevel providers, nurses, and prehospital providers). Reviewers evaluated study quality and abstracted information on learners, instructional design (curricular integration, feedback, repetitive practice, mastery learning), and outcomes.Results: From a collection of 10,903 articles, 85 eligible studies enrolling 6,099 EM learners were identified. Of these, 56 studies compared simulation to no intervention, 12 compared simulation with another form of instruction, and 19 compared two forms of simulation. Effect sizes were pooled using a random-effects model. Heterogeneity among these studies was large (I 2 ! 50%). Among studies comparing simulation to no intervention, pooled effect sizes were large (range = 1.13 to 1.48) for knowledge, time, and skills and small to moderate for behaviors with patients (0.62) and patient effects (0.43; all p < 0.02 except patient effects p = 0.12). Among comparisons between simulation and other forms of instruction, the pooled effect sizes were small ( 0.33) for knowledge, time, and process skills (all p > 0.1). Qualitative comparisons of different simulation curricula are limited, although feedback, mastery learning, and higher fidelity were associated with improved learning outcomes.Conclusions: Technology-enhanced simulation for EM learners is associated with moderate or large favorable effects in comparison with no intervention and generally small and nonsignificant benefits in comparison with other instruction. Future research should investigate the features that lead to effective simulation-based instructional design.ACADEMIC EMERGENCY MEDICINE 2013; 20:117-127 © 2013 T echnology-enhanced simulation has emerged as a cornerstone of many emergency medicine (EM) training programs. 1-3 Simulation allows complex tasks to be deconstructed into more manageable learning objectives and fosters repeated practice on specific tasks that occur infrequently or expose patients to risk when performed by novice learners. 4 Computer-based virtual reality simulators, robotic and static mannequins, artificial models, live animals, inert animal products, and human cadavers have all been employed as educational tools for these purposes.1 At the 2008 Academic Emergency Medicine (AEM) consensus conference entitled...

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Cognitive versus Technical Debriefing after Simulation Training

Abstract: Background: Recent literature describes ''cognitive dispositions to respond'' (CDRs) that may lead physicians to err in their clinical reasoning.

Cited by 36 publications

References 33 publications

How Debriefing Strategies Can Improve Student Motivation and Self-Efficacy in Game-Based Learning

How Debriefing Strategies Can Improve Student Motivation and Self-Efficacy in Game-Based Learning

Naturalistic Decision Making for Power System Operators

Technology‐enhanced Simulation in Emergency Medicine: A Systematic Review and Meta‐Analysis

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