Literature DB >> 30831482

Using emotion recognition to assess simulation-based learning.

Leandro Y Mano1, Alessandra Mazzo2, José R T Neto3, Mateus H G Meska4, Gabriel T Giancristofaro5, Jó Ueyama6, Gerson A P Júnior7.   

Abstract

Simulation-based assessment relies on instruments that measure knowledge acquisition, satisfaction, confidence, and the motivation of students. However, the emotional aspects of assessment have not yet been fully explored in the literature. This dimension can provide a deeper understanding of the experience of learning in clinical simulations. In this study, a computer (software) model was employed to identify and classify emotions with the aim of assessing them, while creating a simulation scenario. A group of (twenty-four) students took part in a simulated nursing care scenario that included a patient suffering from ascites and respiratory distress syndrome followed by vomiting. The patient's facial expressions were recorded and then individually analyzed on the basis of six critical factors that were determined by the researchers in the simulation scenario: 1) student-patient communication, 2) dealing with the patient's complaint, 3) making a clinical assessment of the patient, 4) the vomiting episode, 5) nursing interventions, and 6) making a reassessment of the patient. The results showed that emotion recognition can be assessed by means of both dimensional (continuous models) and cognitive (discrete or categorical models) theories of emotion. With the aid of emotion recognition and classification through facial expressions, the researchers succeeded in analyzing the emotions of students during a simulated clinical learning activity. In the study, the participants mainly displayed a restricted affect during the simulation scenario, which involved negative feelings such as anger, fear, tension, and impatience, resulting from the difficulty of creating the scenario. This can help determine which areas the students were able to master and which caused them greater difficulty. The model employed for the recognition and analysis of facial expressions in this study is very comprehensive and paves the way for further use and a more detailed interpretation of its components.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Assessment; Education; Emotion classification; Emotions; Simulation

Mesh:

Year:  2019        PMID: 30831482     DOI: 10.1016/j.nepr.2019.02.017

Source DB:  PubMed          Journal:  Nurse Educ Pract        ISSN: 1471-5953            Impact factor:   2.281


  5 in total

1.  Emotion Recognition With Knowledge Graph Based on Electrodermal Activity.

Authors:  Hayford Perry Fordson; Xiaofen Xing; Kailing Guo; Xiangmin Xu
Journal:  Front Neurosci       Date:  2022-06-09       Impact factor: 5.152

2.  Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis.

Authors:  Ortal Slobodin; Ilia Plochotnikov; Idan-Chaim Cohen; Aviad Elyashar; Odeya Cohen; Rami Puzis
Journal:  Int J Environ Res Public Health       Date:  2022-06-04       Impact factor: 4.614

3.  Emotional recognition for simulated clinical environment using unpleasant odors: quasi-experimental study.

Authors:  Mateus Henrique Gonçalves Meska; Leandro Yukio Mano; Janaina Pereira Silva; Gerson Alves Pereira Junior; Alessandra Mazzo
Journal:  Rev Lat Am Enfermagem       Date:  2020-02-14

4.  Emotional Intervention and Education System Construction for Rural Children Based on Semantic Analysis.

Authors:  Xiaobo Zhang
Journal:  Occup Ther Int       Date:  2022-07-04       Impact factor: 1.565

Review 5.  Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review.

Authors:  Christine Buchanan; M Lyndsay Howitt; Rita Wilson; Richard G Booth; Tracie Risling; Megan Bamford
Journal:  JMIR Nurs       Date:  2021-01-28
  5 in total

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