Predicting Affective States expressed through an Emote-Aloud Procedure from AutoTutor's Mixed-Initiative Dialogue
Predicting Affective States expressed through an Emote-Aloud Procedure from AutoTutor's Mixed-Initiative Dialogue
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发表时间：
2006
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通讯作者：
S. D’Mello;Scotty D. Craig;Jeremiah Sullins;A. Graesser
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作者：
S. D’Mello;Scotty D. Craig;Jeremiah Sullins;A. Graesser
本文研究了与智能辅导系统AutoTutor的混合主动对话的频繁对话模式如何能够显著预测用户的情感状态（例如，困惑，顿顿，沮丧）。这项研究采用了一种大声表达情绪的程序，参与者在与AutoTutor互动时用语言表达自己的情感状态，并将其记录下来。导师与学生的互动是根据对话直接性（导师向学习者提供的信息量，对特定信息>提示的断言>的理论顺序）、反馈（积极、中性、消极）和从导师日志文件中获得的每个学生贡献的内容覆盖分数进行编码的。相关分析和回归分析证实了对话特征可以显著预测困惑、顿悟和沮丧的情感状态的假设。采用标准分类技术对会话特征对学习者影响自动检测的可靠性进行评估。我们讨论了将AutoTutor扩展为情感感知智能辅导系统的前景。
This paper investigates how frequent conversation patterns from a mixed-initiative dialogue with an intelligent tutoring system, AutoTutor, can significantly predict users' affective states (e.g. confusion, eureka, frustration). This study adopted an emote-aloud procedure in which participants were recorded as they verbalized their affective states while interacting with AutoTutor. The tutor-tutee interaction was coded on scales of conversational directness (the amount of information provided by the tutor to the learner, with a theoretical ordering of assertion > prompt for particular information > hint), feedback (positive, neutral, negative), and content coverage scores for each student contribution obtained from the tutor's log files. Correlation and regression analyses confirmed the hypothesis that dialogue features could significantly predict the affective states of confusion, eureka, and frustration. Standard classification techniques were used to assess the reliability of the automatic detection of learners' affect from the conversation features. We discuss the prospects of extending AutoTutor into an affect-sensing intelligent tutoring system.