Affect-driven Learning Outcomes Prediction in Intelligent Tutoring Systems
Affect-driven Learning Outcomes Prediction in Intelligent Tutoring Systems
复制标题
智能辅导系统中情感驱动的学习成果预测
DOI：
10.1109/fg.2019.8756624
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Margrit Betke
中科院分区：
文献类型：
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作者：
Ajjen Joshi;Danielle A. Allessio;John J. Magee;J. Whitehill;I. Arroyo;B. Woolf;S. Sclaroff;Margrit Betke
配备具有解读学生情感信号能力的智能辅导系统（ITS），可以通过虚拟导师监控学生的进步，提供及时的干预，并提供适当的情感反应，从而潜在地改善学生的学习体验。大多数具有影响建模功能的ITSs都试图预测用户的情绪状态。然而，这项工作的重点是试图直接预测学生在解决一组数学问题时的学习结果。使用从视频流中提取的面部特征，我们训练分类器直接预测学生在学生刚刚开始解决问题时尝试回答问题的成功或失败。在这项工作中，我们首先引入了一个新的学生与MathSpring交互的数据集，MathSpring是一个流行的ITS。我们使用典型的面部动作单元激活对不同的问题结果类进行了探索性分析。我们开发了基线模型来预测学生解决数学问题的问题结果标签，并讨论了如何预测和利用早期问题结果标签来提供可能的干预措施。
Equipping an Intelligent Tutoring System (ITS) with the ability to interpret affective signals from students could potentially improve the learning experience of students by enabling the tutor to monitor the students’ progress and provide timely interventions as well as present appropriate affective reactions via a virtual tutor. Most ITSs equipped with affect modeling capabilities attempt to predict the emotional state of users. However, the focus in this work is instead on trying to directly predict the learning outcomes of students from a stream of video capturing the students faces as they work on a set of math problems. Using facial features extracted from a video stream, we train classifiers to directly predict the success or failure of a student’s attempt to answer a question while the student has just begun to work on the problem. In this work, we first introduce a novel dataset of student interactions with MathSpring, a popular ITS. We provide an exploratory analysis of the different problem outcome classes using typical facial action unit activations. We develop baseline models to predict the problem outcome labels of students solving math problems and discuss how early problem outcome labels can be forecasted and utilized to provide possible interventions.