发展报告攥写小组

Attention is key to effective learning, but mind wandering, a phenomenon in which attention shifts from task-related processing to task-unrelated thoughts, is pervasive across learning tasks. Therefore, intelligent learning environments should benefit from mechanisms to detect and respond to attentional lapses, such as mind wandering. As a step in this direction, we report the development and validation of the first student-independent facial feature-based mind wandering detector. We collected training data in a lab study where participants self-reported when they caught themselves mind wandering over the course of completing a 32.5 min narrative film comprehension task. We used computer vision techniques to extract facial features and bodily movements from videos. Using supervised learning methods, we were able to detect a mind wandering with an F1 score of .390, which reflected a 31% improvement over a chance model. We discuss how our mind wandering detector can be used to adapt the learning experience, particularly for online learning contexts.

走神现象，即注意力从与任务相关的处理转移到与任务无关的想法，在学习任务中普遍存在，并且会影响学习效果。

我们的方法是在用户在电脑屏幕上观看短片时收集视频和MW的自我报告。为了避免思想探针的干扰作用，我们采用了自捕获的方法来检测毫米波。我们从视频中提取面部特征和身体动作，并使用监督分类技术建立模型，在短时间内识别用户的MW。

我们一共提取了78个特征(75个面部特征+ 3个身体运动特征)。我们使用Winsorization(一种常见的离群值处理技术[18])处理离群值，该方法将离群值定义为离均值大于三个标准差的值。

RELIEF-F特征选择(仅针对训练数据)来对特征进行排序

在前期实验的基础上，我们使用WEKA toolkit选择了9个分类器进行更多的测试(Naive Bayes, Support Vector Machines, Simple Logistic Regression, LogitBoost, Random Forest, C4.5 trees, Random Gradient Descent, Classification via Regression, Bayes Net)