Investigating the Relationship Between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach
Investigating the Relationship Between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach
复制标题
研究对话结构与辅导效果之间的关系：隐马尔可夫建模方法
DOI：
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发表时间：
2011
影响因子：
4.9
通讯作者：
James C. Lester
中科院分区：
文献类型：
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作者：
K. Boyer;R. Phillips;Amy Ingram;Eun Ha;M. Wallis;M. Vouk;James C. Lester
确定有效的导师制对话策略是智能导师制研究的关键问题。人与人之间的辅导为识别有效的辅导策略提供了一个有价值的模型，但由于人类对话的丰富性，提取这些策略是一个挑战。本文通过一种机器学习方法解决了这一挑战，该方法1)从人类辅导语料库中学习辅导模式，2)确定学生成绩与学习模式之间的统计关系。建模方法利用隐马尔可夫模型（hmm）来捕获不可观察的随机结构，该结构被认为会影响观察结果，在这种情况下，由面向任务的教程对话生成的对话行为和任务行为。我们将这个不可观察的层称为隐藏对话状态，并将其解释为代表导师和学生的合作意图。我们已经将hmm应用于一个带注释的面向任务的教程对话语料库，为两个有效的人类导师中的每一个学习一个模型。自动提取的辅导模式与学生的学习效果之间存在显著的相关关系。总体而言，研究结果表明hmm可以学习到有意义的隐性教程对话结构。更具体地说，研究结果指出了任务导向的教学对话中与提高学生学习有关的特定机制。这项工作在编写数据驱动的辅导对话系统行为和调查人类辅导的有效性方面有直接的应用。
Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model tbr identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning approach that 1) learns tutorial modes from a corpus of human tutoring, and 2) identifies the statistical relationships between student outcomes and the learned modes. The modeling approach utilizes hidden Markov models (HMMs) to capture the unobservable stochastic structure that is thought to influence the observations, in this case dialogue acts and task actions, that are generated by task-oriented tutorial dialogue. We refer to this unobservable layer as the hidden dialogue state, and interpret it as representing the tutor and students' collaborative intentions. We have applied HMMs to a corpus of annotated task-oriented tutorial dialogue to learn one model for each of two effective human tutors. Significant correlations emerged between the automatically extracted tutoring modes and student learning outcomes. Broadly, the results suggest that HMMs can learn meaningful hidden tutorial dialogue structure. More specifically, the findings point to specific mechanisms within task-oriented tutorial dialogue that are associated with increased student learning. This work has direct applications in authoring data-driven tutorial dialogue system behavior and in investigating the effectiveness of human tutoring.