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Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study

BACKGROUND: A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load...

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Detalles Bibliográficos
Autores principales: Mitre-Hernandez, Hugo, Covarrubias Carrillo, Roberto, Lara-Alvarez, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7834946/
https://www.ncbi.nlm.nih.gov/pubmed/33427677
http://dx.doi.org/10.2196/21620
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author Mitre-Hernandez, Hugo
Covarrubias Carrillo, Roberto
Lara-Alvarez, Carlos
author_facet Mitre-Hernandez, Hugo
Covarrubias Carrillo, Roberto
Lara-Alvarez, Carlos
author_sort Mitre-Hernandez, Hugo
collection PubMed
description BACKGROUND: A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty. OBJECTIVE: This study aims to assess the use of task-evoked pupillary responses to measure the cognitive load measure for describing the difficulty levels in a video game. In addition, it proposes an image filter to better estimate baseline pupil size and to reduce the screen luminescence effect. METHODS: We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Then, a classifier with different pupil features was used to classify the difficulty of a data set containing information from students playing a video game for practicing math fractions. RESULTS: We observed that the proposed filter better estimates a baseline. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ(2)(14)=0.05; P=.001); therefore, a Greenhouse-Geisser correction was used (ε=0.47). There was a significant difference in mean pupil diameter change (MPDC) estimated from different baseline images with the scramble filter (F(5,78)=30.965; P<.001). Moreover, according to the Wilcoxon signed rank test, pupillary response features that better describe the difficulty level were MPDC (z=−2.15; P=.03) and peak dilation (z=−3.58; P<.001). A random forest classifier for easy and hard levels of difficulty showed an accuracy of 75% when the gamer data were used, but the accuracy increased to 87.5% when pupillary measurements were included. CONCLUSIONS: The screen luminescence effect on pupil size is reduced with a scrambled filter on the background video game image. Finally, pupillary response data can improve classifier accuracy for the perceived difficulty of levels in educational video games.
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spelling pubmed-78349462021-01-29 Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study Mitre-Hernandez, Hugo Covarrubias Carrillo, Roberto Lara-Alvarez, Carlos JMIR Serious Games Original Paper BACKGROUND: A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty. OBJECTIVE: This study aims to assess the use of task-evoked pupillary responses to measure the cognitive load measure for describing the difficulty levels in a video game. In addition, it proposes an image filter to better estimate baseline pupil size and to reduce the screen luminescence effect. METHODS: We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Then, a classifier with different pupil features was used to classify the difficulty of a data set containing information from students playing a video game for practicing math fractions. RESULTS: We observed that the proposed filter better estimates a baseline. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ(2)(14)=0.05; P=.001); therefore, a Greenhouse-Geisser correction was used (ε=0.47). There was a significant difference in mean pupil diameter change (MPDC) estimated from different baseline images with the scramble filter (F(5,78)=30.965; P<.001). Moreover, according to the Wilcoxon signed rank test, pupillary response features that better describe the difficulty level were MPDC (z=−2.15; P=.03) and peak dilation (z=−3.58; P<.001). A random forest classifier for easy and hard levels of difficulty showed an accuracy of 75% when the gamer data were used, but the accuracy increased to 87.5% when pupillary measurements were included. CONCLUSIONS: The screen luminescence effect on pupil size is reduced with a scrambled filter on the background video game image. Finally, pupillary response data can improve classifier accuracy for the perceived difficulty of levels in educational video games. JMIR Publications 2021-01-11 /pmc/articles/PMC7834946/ /pubmed/33427677 http://dx.doi.org/10.2196/21620 Text en ©Hugo Mitre-Hernandez, Roberto Covarrubias Carrillo, Carlos Lara-Alvarez. Originally published in JMIR Serious Games (http://games.jmir.org), 11.01.2021. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on http://games.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Mitre-Hernandez, Hugo
Covarrubias Carrillo, Roberto
Lara-Alvarez, Carlos
Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study
title Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study
title_full Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study
title_fullStr Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study
title_full_unstemmed Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study
title_short Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study
title_sort pupillary responses for cognitive load measurement to classify difficulty levels in an educational video game: empirical study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7834946/
https://www.ncbi.nlm.nih.gov/pubmed/33427677
http://dx.doi.org/10.2196/21620
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