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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using emotion recognition to assess simulation-based learning

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Mano, Leandro Y. [1] ; Mazzo, Alessandra [2] ; Neto, Jose R. T. [1] ; Meska, Mateus H. G. [2] ; Giancristofaro, Gabriel T. [1] ; Ueyama, Jo [1] ; Junior, Gerson A. P. [3]
Total Authors: 7
[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Paulo - Brazil
[2] Univ Sao Paulo, Ribeirao Preto Coll Nursing, BR-14040902 Sao Paulo - Brazil
[3] Univ Sao Paulo, Campus Bauru, BR-17012901 Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: NURSE EDUCATION IN PRACTICE; v. 36, p. 13-19, MAR 2019.
Web of Science Citations: 0

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. (AU)

FAPESP's process: 16/14267-7 - Exploiting the Internet of Things with Artificial Intelligence in the context of Health Smart Homes: a physical and emotional approach
Grantee:Leandro Yukio Mano Alves
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 16/04261-1 - Clinical simulation: implications from resources in the satisfaction and self-confidence of student and learning strategy
Grantee:Alessandra Mazzo
Support type: Regular Research Grants
FAPESP's process: 15/21642-6 - Enhancing intelligence in IoTs: approaches and applications in sensors, UAVs and smartphones
Grantee:Jó Ueyama
Support type: Regular Research Grants