Mitre-Hernandez Hugo, Covarrubias Carrillo Roberto, Lara-Alvarez Carlos
Center for Research in Mathematics, Zacatecas, Mexico.
Center for Research and Advanced Studies of the National Polytechnic Institute, Tamaulipas, Ciudad Victoria, Mexico.
JMIR Serious Games. 2021 Jan 11;9(1):e21620. doi: 10.2196/21620.
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.
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.
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.
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=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=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.
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.
反复被认为容易（或困难）的学习任务可能导致学习效果不佳。玩家数据，如错误次数、尝试次数或完成一项挑战所需的时间，被广泛用于估计感知到的难度水平。在其他情况下，瞳孔测量法被广泛用于测量认知负荷（脑力投入）；因此，这可能描述了感知到的任务难度。
本研究旨在评估任务诱发的瞳孔反应在测量认知负荷指标以描述电子游戏难度水平方面的应用。此外，还提出了一种图像滤波器，以更好地估计基线瞳孔大小并减少屏幕发光效果。
我们进行了一项实验，将我们的滤波器估计的基线与常用方法估计的基线进行比较。然后，使用具有不同瞳孔特征的分类器对一个数据集的难度进行分类，该数据集包含学生玩一款用于练习数学分数的电子游戏的信息。
我们观察到，所提出的滤波器能更好地估计基线。Mauchly球形检验表明球形假设已被违反（χ = 0.05；P = 0.001）；因此，使用了Greenhouse-Geisser校正（ε = 0.47）。使用加扰滤波器从不同基线图像估计的平均瞳孔直径变化（MPDC）存在显著差异（F = 30.965；P < 0.001）。此外，根据Wilcoxon符号秩检验，能更好地描述难度水平的瞳孔反应特征是MPDC（z = -2.15；P = 0.03）和峰值扩张（z = -3.58；P < 0.001）。当使用玩家数据时，用于区分简单和困难难度水平的随机森林分类器的准确率为75%，但当纳入瞳孔测量数据时，准确率提高到了87.5%。
通过在电子游戏背景图像上使用加扰滤波器，可减少屏幕发光对瞳孔大小的影响。最后，瞳孔反应数据可以提高教育电子游戏中关卡感知难度的分类器准确率。
