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A Theory-Driven Approach to Predict Frustration in an ITS

机译：一种理论驱动的ITS挫折预测方法

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摘要

The importance of affect in learning has led many intelligent tutoring systems (ITS) to include learners' affective states in their student models. The approaches used to identify affective states include human observation, self-reporting, data from physical sensors, modeling affective states, and mining students' data in log files. Among these, data mining and modeling affective states offer the most feasible approach in real-world settings, which may involve a huge number of students. Systems using data mining approaches to predict frustration have reported high accuracy, while systems that predict frustration by modeling affective states, not only predict a student's affective state but also the reason for that state. In our approach, we combine these approaches. We begin with the theoretical definition of frustration, and operationalize it as a linear regression model by selecting and appropriately combining features from log file data. We illustrate our approach by modeling the learners' frustration in Mindspark, a mathematics ITS with large-scale deployment. We validate our model by independent human observation. Our approach shows comparable results to existing data mining approaches and also the clear interpretation of the reasons for the learners' frustration.
机译：情感在学习中的重要性已导致许多智能辅导系统（ITS）将学习者的情感状态纳入其学生模型中。用于识别情感状态的方法包括人类观察，自我报告，来自物理传感器的数据，对情感状态进行建模以及在日志文件中挖掘学生的数据。其中，数据挖掘和情感状态建模是现实环境中最可行的方法，可能涉及大量学生。使用数据挖掘方法预测挫败感的系统报告了很高的准确性，而通过对情感状态进行建模来预测挫败感的系统不仅可以预测学生的情感状态，还可以预测该情感状态的原因。在我们的方法中，我们将这些方法结合在一起。我们从挫折的理论定义开始，然后通过从日志文件数据中选择并适当组合特征，将其作为线性回归模型进行操作。我们通过在Mindspark（具有大规模部署的数学ITS）中模拟学习者的挫败感来说明我们的方法。我们通过独立的人类观察来验证我们的模型。我们的方法显示出与现有数据挖掘方法相当的结果，并且清楚地解释了学习者感到沮丧的原因。

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