Using emotion recognition to assess simulation-based learning

doi:10.1016/j.nepr.2019.02.017

2019-03-01

PRE

Leandro Yukio Mano *

Alessandra Mazzo

José Rodrigues Torres Neto

Mateus Henrique Gonçalves Meska

Gabriel T. Giancristofaro

Jó Ueyama

Gerson Alves Pereira Júnior

DOI：10.1016/j.nepr.2019.02.017

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摘要

En 中文

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.

Keyword:

Assessment

Education

Simulation

Emotions

Emotion classification