Active Learning for Student Affect Detection
Active Learning for Student Affect Detection
复制标题
主动学习用于学生情绪检测
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Andrew S. Lan
中科院分区：
文献类型：
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作者：
Tsung;R. Baker;Christoph Studer;N. Heffernan;Andrew S. Lan
“无传感器”学生情绪检测器仅使用学生活动数据，不使用物理或生理传感器，具有成本效益，并且有可能在实际教室中大规模应用。这些检测器使用从人类观察者收集的学生影响标签进行训练，因为他们观察学生在真实教室的智能辅导系统（ITS）中学习。由于学生活动和情感动态的固有多样性，对于情感检测器来说，观察某些学生在特定时间的情感状态可能比观察其他学生提供更多信息。因此，精心设计的观察计划可能会带来更有意义的观察并改进情感检测器。在本文中，我们研究了主动（机器）学习方法（一系列自适应地选择下一个信息最丰富的观察的方法）是否可以提高情感标签收集过程的效率。我们研究了几种现有的主动学习方法，并提出了一种非常适合情感检测中问题设置的新方法。我们使用在部署 ASSISTments ITS 的真实教室中收集的真实学生影响数据集进行了一系列实验。结果表明，一些主动学习方法只需使用少量信息丰富的观察结果就可以产生高质量的情感检测器。我们还讨论了如何在真实课堂中部署主动学习方法，以改进情感标签收集过程，从而改进无传感器情感检测器。
“Sensor-free” detectors of student affect that use only student activity data and no physical or physiological sensors are cost-effective and have potential to be applied at large scale in real classrooms. These detectors are trained using student affect labels collected from human observers as they observe students learn within intelligent tutoring systems (ITSs) in real classrooms. Due to the inherent diversity of student activity and affect dynamics, observing the affective states of some students at certain times is likely to be more informative to the affect detectors than observing others. Therefore, a carefully-crafted observation schedule may lead to more meaningful observations and improved affect detectors. In this paper, we investigate whether active (machine) learning methods, a family of methods that adaptively select the next most informative observation, can improve the efficiency of the affect label collection process. We study several existing active learning methods and also propose a new method that is ideally suited for the problem setting in affect detection. We conduct a series of experiments using a real-world student affect dataset collected in real classrooms deploying the ASSISTments ITS. Results show that some active learning methods can lead to high-quality affect detectors using only a small number of highly informative observations. We also discuss how to deploy active learning methods in real class-rooms to improve the affect label collection process and thus sensor-free affect detectors.
Rasch模型的估计与分析框架
DOI：
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发表时间：
2018
期刊：
Proceedings of the 35th International Conference on Machine Learning
影响因子：
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作者：
Lan, Andrew;Chiang, Mung;Studer, Christoph
通讯作者：
Studer, Christoph
DOI：
10.1109/ciss.2018.8362200
发表时间：
2018-02
期刊：
2018 52nd Annual Conference on Information Sciences and Systems (CISS)
影响因子：
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作者：
Andrew S. Lan;M. Chiang;Christoph Studer
通讯作者：
Andrew S. Lan;M. Chiang;Christoph Studer