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Currently submitted to: JMIR Serious Games

Date Submitted: Jun 19, 2020
Open Peer Review Period: Jun 19, 2020 - Aug 14, 2020
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Game and Pupillometry Data for Cognitive Load Measurement: Classifying Cognitive Load of Educational Video Game Levels

  • Hugo Mitre-Hernandez; 
  • Roberto Covarrubias-Carrillo; 
  • Carlos Lara-Alvarez; 

ABSTRACT

Background:

Besides the current challenge, the perceived difficulty level of a learning task depends on the student's previous knowledge and skills. When a learning task is recurrently perceived as easy (or hard), it 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 the cognitive load (mental effort); hence, this may describe the perceived task difficulty.

Objective:

Objective:

This study aimed to assess the use of pupillary data as a cognitive load measure for describing the difficulty levels in a video game. Also, it proposes an image filter to better estimate the 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. Different pupil features were used to classify the difficulty of a dataset containing information from students playing a video game for practicing math fractions.

Results:

Results:

Results showed that the proposed filter allows to better estimate a baseline, Mauchly’s Test of Sphericity indicated that the assumption of sphericity had been violated, χ2(14) =0.045389, p = .001; and therefore, a Greenhouse-Geisser correction was used, ε = 0.47, there was a significant difference against Mean Pupil Diameter Change (MPDC) estimated from different baseline images with the scramble filter, F(2.35) = 30.965, p < .001. Moreover, according to the Wilcoxon signed-rank test, pupillary features that better describe the difficulty level were MPDC (Z = -2.15, p <0.05) and Peak Dilation (Z = -3.58, p<0.00); a random forest classifier for easy- and hard-level of difficult showed an accuracy of 75% when the gamer data is used, but the accuracy increases to 87.5 % by including pupillary measurements.

Conclusions:

The screen luminescent effect on pupil size was reduced with a scrambled filter on the background video game image. Finally, pupillary data can improve the classifier accuracy of the perceived difficulty of gamers in educational video games.


 Citation

Please cite as:

Mitre-Hernandez H, Covarrubias-Carrillo R, Lara-Alvarez C

Game and Pupillometry Data for Cognitive Load Measurement: Classifying Cognitive Load of Educational Video Game Levels

JMIR Preprints. 19/06/2020:21620

DOI: 10.2196/preprints.21620

URL: https://preprints.jmir.org/preprint/21620

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