This article presents a new approach to predict learning difficulty in applications such as e-learning using eye movement and pupil response. We have developed 12 eye response features based on psycholinguistics, contextual information processing, anticipatory behavior analysis, recurrence fixation analysis, and pupillary response. A key aspect of the proposed approach is the temporal analysis of the feature response to the same concept. Results show that variations in eye response to the same concept over time are indicative of learning difficulty. A Feature Weighted Linguistics Classifier (FWLC) was developed to predict learning difficulty in real time. The proposed approach predicts learning difficulty with an accuracy of 90%.

中文翻译：

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使用眼动追踪预测学习难度的特征加权语言学分类器

本文提出了一种新方法来预测应用程序中的学习难度，例如使用眼球运动和瞳孔反应的电子学习。我们基于心理语言学、上下文信息处理、预期行为分析、复发注视分析和瞳孔反应开发了 12 种眼睛反应特征。所提出方法的一个关键方面是对同一概念的特征响应进行时间分析。结果表明，随着时间的推移，眼睛对同一概念的反应会发生变化，这表明学习困难。开发了一个特征加权语言学分类器（FWLC）来实时预测学习难度。所提出的方法以 90% 的准确度预测学习难度。