Adapting to Student Uncertainty over and above Correctness in A Spoken Tutoring Dialogue System

在口语辅导对话系统中适应学生的不确定性而不是正确性

• 批准号: 0631930
• 负责人: Diane Litman
• 金额: --
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0631930&HistoricalAwards=false
• 关键词: Adapting; Student; Uncertainty; over; above

This research investigates whether responding to student uncertainty over and above correctness improves learning during computer tutoring. The investigation is performed in the context of a spoken dialogue tutoring system, where student speech provides many linguistic cues (e.g. intonation, pausing, word usage) that computational linguistics research suggests can be used to detect uncertainty. Intelligent tutoring systems research suggests that uncertainty is part of the learning process, and has hypothesized that to increase system effectiveness, it is critical to respond to more than correctness. However, most existing tutoring systems respond only to student correctness, and few controlled experiments have yet investigated whether also responding to uncertainty can improve learning.This research designs and implements two different enhancements to the spoken dialogue tutoring system, to test two hypotheses in the tutoring literature concerning how tutors can effectively respond to uncertainty over and above correctness. The first hypothesis is that student uncertainty and incorrectness both represent learning impasses, i.e., opportunities to improve understanding. This hypothesis is addressed with an enhanced system version that treats uncertainty in the same way that incorrectness is currently treated (i.e., with additional subdialogue to increase understanding). The second hypothesis is that more optimal responses can be developed by modeling how human tutor responses to correctness change when the student is uncertain. This hypothesis is addressed by analyzing human tutor dialogue act responses (i.e. content and presentation) to student uncertainty over and above correctness in an existing tutoring corpus, then implementing these responses in a second enhanced system version. Two controlled experiments are then performed. The first tests the relative impact of the two adaptations on learning using a Wizard of Oz version of the system, with a human (Wizard) detecting uncertainty and performing speech recognition and language understanding. The second experiment tests the impact of the best-performing adaptation from the first experiment in the context of the real system, with the system processing the speech and language and detecting uncertainty in a fully automated manner.The major intellectual contribution of the research is to demonstrate whether significant improvements in learning are achieved by adapting to student uncertainty over and above correctness during tutoring, to advance the state of the art by fully automating and evaluating user uncertainty detection and adaptation in a working spoken dialogue system, and to investigate any different effects of this adaptation under ideal versus actual system conditions.

这项研究调查了对学生不确定性的反应是否超过正确性，可以改善计算机辅导期间的学习。调查是在口语对话辅导系统的背景下进行的，在该系统中，学生的言语提供了许多语言提示（例如语调，暂停，单词用法），即计算语言学研究表明可以使用来检测不确定性。智能辅导系统研究表明，不确定性是学习过程的一部分，并假设为了提高系统效率，对响应而不是正确性至关重要。但是，大多数现有的辅导系统仅对学生的正确性做出响应，并且很少有受控的实验还研究了对不确定性的反应是否可以改善学习。这项研究设计并实现了对口头对话辅导系统的两种不同增强功能，以测试在辅助文献中如何有效地响应不良性和上方的辅导文献中的两个假设。第一个假设是，学生的不确定性和不正确性都代表了学习的障碍，即改善理解的机会。该假设是通过增强的系统版本来解决的，该系统以与当前处理不正确的方式相同的方式处理不确定性（即，以及增加了增加理解）。第二个假设是，可以通过建模学生在学生不确定时对人类辅导员对正确性变化的响应进行建模，可以开发出更最佳的响应。通过分析人类导师对话法案的回答（即内容和演示）对学生不确定性的响应（即内容和演示）来解决这一假设，而不是现有辅导语料库的正确性，然后在第二个增强的系统版本中实现这些响应。然后进行两个受控的实验。第一个测试了两种改编对学习系统的巫师的相对影响，其中人类（向导）检测不确定性并执行语音识别和语言理解。第二个实验测试了第一个实验在真实系统中最佳表现适应的影响，系统处理语音和语言并以完全自动化的方式检测不确定性，研究的主要智力贡献是为了证明，通过适应学生的不正确且无效的方式来证明学习在学习中是否取得了重大改进，以实现undection，以在上面的不正确范围内进行无效，并且在整个过程中进行了不确定的态度，并且在培训中，在培训过程中，在整个过程中，在整个过程中的态度可以实现，并且在整个过程中的态度进行了不确定性，并且可以在上述过程中进行无效的态度。并在工作的口语对话系统中进行适应，并在理想的系统条件与实际系统条件下研究这种适应的任何不同效果。