Unidad Zacatecas, Centro de Investigación en Matemáticas, A.C., Zacatecas, Zacatecas, Mexico.
PeerJ. 2022 Mar 29;10:e12864. doi: 10.7717/peerj.12864. eCollection 2022.
Knowing the difficulty of a given task is crucial for improving the learning outcomes. This paper studies the difficulty level classification of memorization tasks from pupillary response data. Developing a difficulty level classifier from pupil size features is challenging because of the inter-subject variability of pupil responses. Eye-tracking data used in this study was collected while students solved different memorization tasks divided as low-, medium-, and high-level. Statistical analysis shows that values of pupillometric features (as peak dilation, pupil diameter change, and suchlike) differ significantly for different difficulty levels. We used a wrapper method to select the pupillometric features that work the best for the most common classifiers; Support Vector Machine (SVM), Decision Tree (DT), Linear Discriminant Analysis (LDA), and Random Forest (RF). Despite the statistical difference, experiments showed that a random forest classifier trained with five features obtained the best F1-score (82%). This result is essential because it describes a method to evaluate the cognitive load of a subject performing a task using only pupil size features.
了解任务的难度对于提高学习效果至关重要。本文研究了从瞳孔反应数据中对记忆任务进行难度水平分类的方法。由于瞳孔反应的个体间可变性，从瞳孔大小特征中开发难度水平分类器具有挑战性。本研究中使用的眼动追踪数据是在学生解决不同难度水平的记忆任务时收集的，这些任务被分为低、中、高三个等级。统计分析表明，不同难度水平的瞳孔测量特征（如峰值扩张、瞳孔直径变化等）值存在显著差异。我们使用包装器方法选择了最适合最常见分类器的瞳孔测量特征；支持向量机（SVM）、决策树（DT）、线性判别分析（LDA）和随机森林（RF）。尽管存在统计学差异，但实验表明，使用五个特征训练的随机森林分类器获得了最佳的 F1 分数（82%）。这一结果至关重要，因为它描述了一种仅使用瞳孔大小特征评估主体执行任务时认知负荷的方法。
