Supervised vs Unsupervised Learning on Gaze Data to Classify Student Distraction Level in an Educational VR Environment
Supervised vs Unsupervised Learning on Gaze Data to Classify Student Distraction Level in an Educational VR Environment
复制标题
在教育 VR 环境中对注视数据进行监督与无监督学习以对学生分心程度进行分类
DOI：
10.1145/3485279.3488283
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Borst, Christoph W.
中科院分区：
文献类型：
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作者：
Asish, Sarker Monojit;Kulshreshth, Arun K;Borst, Christoph W.
与传统学习方法相比，教育虚拟现实可以通过提高学生的参与度或提高记忆力来帮助学生。然而，学生在 VR 环境中可能会因为压力、走神、不必要的噪音、外部警报等而分心。学生的视线可用于检测这些分心。我们探索基于深度学习的方法来检测注视数据中的干扰。我们设计了一个教育 VR 环境，并使用监督和无监督学习方法训练了三种深度学习模型（CNN、LSTM 和 CNN-LSTM）来衡量学生对注视数据的分心程度。我们的结果表明，与无监督学习方法相比，监督学习提供了更好的测试准确性。
Educational VR may help students by being more engaging or improving retention compared to traditional learning methods. However, a student can get distracted in a VR environment due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting these distraction. We explore deep-learning-based approaches to detect distractions from gaze data. We designed an educational VR environment and trained three deep learning models (CNN, LSTM, and CNN-LSTM) to gauge a student’s distraction level from gaze data, using both supervised and unsupervised learning methods. Our results show that supervised learning provided better test accuracy compared to unsupervised learning methods.
探索用于识别教育 VR 中分心学生的眼睛注视可视化技术
DOI：
10.1109/vr46266.2020.00009
发表时间：
2020
期刊：
2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR
影响因子：
--
作者：
Rahman, Yitoshee;Asish, Sarker M.;Fisher, Nicholas P.;Bruce, Ethan C.;Kulshreshth, Arun K.;Borst, Christoph W.
通讯作者：
Borst, Christoph W.