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Ranking Feature Sets for Emotion Models Used in Classroom Based Intelligent Tutoring Systems

机译：基于教室的智能辅导系统中使用的情感模型的排名功能集

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摘要

Recent progress has been made by using sensors with Intelligent Tutoring Systems in classrooms in order to predict the affective state of students users. If tutors are able to interpret sensor data with new students based on past experience, rather than having to be individually trained, then this will enable tutor developers to evaluate various methods of adapting to each student's affective state using consistent predictions. In the past, our classifiers have predicted student emotions with an accuracy between 78% and 87%. However, it is still unclear which sensors are best, and the educational technology community needs to know this to develop better than baseline classifiers, e.g. ones that use only frequency of emotional occurrence to predict affective state. This paper suggests a method to clarify classifier ranking for the purpose of affective models. The method begins with a careful collection of a training and testing set, each from a separate population, and concludes with a non-parametric ranking of the trained classifiers on the testing set. We illustrate this method with classifiers trained on data collected in the Fall of 2008 and tested on data collected in the Spring of 2009. Our results show that the classifiers for some affective states are significantly better than the baseline model; a validation analysis showed that some but not all classifier rankings generalize to new settings. Overall, our analysis suggests that though there is some benefit gained from simple linear classifiers, more advanced methods or better features may be needed for better classification performance.
机译：通过在教室中使用带有智能辅导系统的传感器来预测学生用户的情感状态，最近取得了进展。如果导师能够根据过去的经验与新学生一起解释传感器数据，而不必接受单独培训，那么这将使导师开发人员可以使用一致的预测来评估适应每个学生的情感状态的各种方法。过去，我们的分类器预测学生情绪的准确度在78％到87％之间。但是，目前尚不清楚哪种传感器最好，教育技术界需要知道这一点才能开发出比基线分类器更好的方法，例如只使用情绪发生频率来预测情感状态的人。本文提出了一种以情感模型为目的来澄清分类器等级的方法。该方法从仔细收集训练和测试集开始，每个训练和测试集均来自单独的总体，最后以测试集上训练后的分类器的非参数排名结束。我们使用在2008年秋季收集的数据进行训练并在2009年春季收集的数据进行测试的分类器来说明该方法。我们的结果表明，某些情感状态的分类器明显优于基线模型；验证分析表明，某些分类器排名（但不是全部）可以推广到新设置。总体而言，我们的分析表明，尽管简单的线性分类器有一些好处，但可能需要更高级的方法或更好的功能才能获得更好的分类性能。

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  • 会议地点 Big Island HI(US);Big Island HI(US)
  • 作者

    David G. Cooper; Kasia Muldner; Ivon Arroyo; Beverly Park Woolf; Winslow Burleson;

  • 作者单位

    University of Massachusetts, Department of Computer Science, 140 Governors Drive, Amherst MA 01003, USA;

    Arizona State University, School of Computing and Informatics, Tempe AZ 85287, USA;

    University of Massachusetts, Department of Computer Science, 140 Governors Drive, Amherst MA 01003, USA;

    University of Massachusetts, Department of Computer Science, 140 Governors Drive, Amherst MA 01003, USA;

    Arizona State University, School of Computing and Informatics, Tempe AZ 85287, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算技术、计算机技术;
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