Student Engagement Dataset
Student Engagement Dataset
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DOI：
10.1109/iccvw54120.2021.00405
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
K. Delgado;Juan Manuel Origgi;Tania Hasanpoor;Hao Yu;Danielle A. Allessio;I. Arroyo;William Lee
中科院分区：
文献类型：
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作者：
K. Delgado;Juan Manuel Origgi;Tania Hasanpoor;Hao Yu;Danielle A. Allessio;I. Arroyo;William Lee
在线学习的一个主要挑战是系统无法支持学生的情感和保持学生的参与度。为了应对这一挑战，计算机视觉已成为一些教学应用中的嵌入式功能。在本文中，我们提出了一个视频数据集的大学生解决数学问题的教育平台MathSpring.org与前置摄像头收集学生的手势视觉反馈。对视频数据集进行注释，以指示学生在特定帧处的注意力是投入还是徘徊。此外，我们还为计算机视觉模块训练基线，以确定学生在远程学习期间的参与程度。基线包括最先进的深度学习图像分类器以及用于头部姿势估计的传统条件和逻辑回归。然后，我们将凝视基线纳入MathSpring学习平台，并使用当前实现的方法评估其性能。
A major challenge for online learning is the inability of systems to support student emotion and to maintain student engagement. In response to this challenge, computer vision has become an embedded feature in some instructional applications. In this paper, we propose a video dataset of college students solving math problems on the educational platform MathSpring.org with a front facing camera collecting visual feedback of student gestures. The video dataset is annotated to indicate whether students’ attention at specific frames is engaged or wandering. In addition, we train baselines for a computer vision module that determines the extent of student engagement during remote learning. Baselines include state-of-the-art deep learning image classifiers and traditional conditional and logistic regression for head pose estimation. We then incorporate a gaze baseline into the MathSpring learning platform, and we are evaluating its performance with the currently implemented approach.