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首页> 美国卫生研究院文献>Brain Sciences >Automatic Detection of a Student’s Affective States for Intelligent Teaching Systems
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Automatic Detection of a Student’s Affective States for Intelligent Teaching Systems

机译：自动检测学生对智能教学系统的情感状态

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AutoTutor is an automated computer tutor that simulates human tutors and holds conversations with students in natural language. Using data collected from AutoTutor, the following determinations were sought: Can we automatically classify affect states from intelligent teaching systems to aid in the detection of a learner’s emotional state? Using frequency patterns of AutoTutor feedback and assigned user emotion in a series of pairs, can the next pair of feedback/emotion series be predicted? Through a priori data mining approaches, we found dominant frequent item sets that predict the next set of responses. Thirty-four participants provided 200 turns between the student and the AutoTutor. Two series of attributes and emotions were concatenated into one row to create a record of previous and next set of emotions. Feature extraction techniques, such as multilayer-perceptron and naive Bayes, were performed on the dataset to perform classification for affective state labeling. The emotions ‘Flow’ and ‘Frustration’ had the highest classification of all the other emotions when measured against other emotions and their respective attributes. The most common frequent item sets were ‘Flow’ and ‘Confusion’.
机译：自助手是一种自动化的计算机导师，模拟人道导师，并掌握与自然语言学生的对话。使用从Autotutor收集的数据，寻求以下确定：我们可以自动对智能教学系统进行影响，以帮助检测学习者的情绪状态吗？在一系列对中使用自动调节反馈和指定用户情感的频率模式，可以预测下一对反馈/情感系列吗？通过先验的数据挖掘方法，我们找到了预测下一组响应的主导频繁项目集。在学生和自动助客之间提供200岁的三十四名参与者。两个系列的属性和情绪被连接到一行中，以创建一个上一个和下一组情绪的记录。在数据集上执行特征提取技术，例如多层 - Perceptron和Naive Bayes，以对情感状态标记进行分类。情绪“流动”和“挫败感”在反对其他情绪和各自属性时，对所有其他情绪的分类最高。最常见的频繁项目集是“流量”和“混乱”。

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