Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information.
Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information.
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DOI：
10.1007/s10015-022-00809-z
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发表时间：
2023
影响因子：
0.9
通讯作者：
中科院分区：
文献类型：
--
作者：
虽然电子学习讲座允许学生按照自己的节奏学习，但很难管理学生的注意力，这使他们无法从讲座中获得有价值的信息。因此，我们提出了一种使用机器学习来检测在线学习讲座期间学生注意力分散的方法，该方法基于仅使用普通网络摄像头即可收集的人脸和姿势信息。在本研究中，我们首先收集了参加电子学习讲座的受试者面部的视频数据，并使用 OpenFace 和 GAST-Net 库来获取面部和姿势信息。接下来，从面部和姿势数据中，我们提取了眼睛和嘴巴的面积、注视方向的角度以及颈部和肩膀的角度等特征。最后，我们使用各种机器学习模型，例如随机森林和 XGBoost，来检测电子学习讲座期间的分心状态。结果表明，我们的二元分类模型仅针对个人数据进行训练，实现了 90% 以上的召回率。
While e-learning lectures allow students to learn at their own pace, it is difficult to manage students’ concentration, which prevents them from receiving valuable information from lectures. Therefore, we propose a method for detecting student distraction during e-learning lectures using machine learning, based on human face and posture information that can be collected using only an ordinary web camera. In this study, we first collected video data of the faces of subjects taking e-learning lectures and used the OpenFace and GAST-Net libraries to obtain face and posture information. Next, from the face and posture data, we extracted features such as the area of the eyes and mouth, the angle of the gaze direction, and the angle of the neck and shoulders. Finally, we used various machine learning models, such as random forest and XGBoost, to detect states of distraction during e-learning lectures. The results show that our binary classification models trained only on the individual’s data achieved more than 90% recall.
影响因子：
3.7
作者：
Boucsein, Wolfram;Fowles, Don C.;Filion, Diane L.
通讯作者：
Filion, Diane L.
影响因子：
3.9
作者：
Gjoreski, Martin;Gams, Matja S.;Hassan, Teena
通讯作者：
Hassan, Teena