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

本研究调查是否响应学生的不确定性以上的正确性，提高学习过程中的计算机辅导。调查是在口语对话辅导系统的背景下进行的，其中学生的语音提供了许多语言线索（如语调，停顿，单词的使用），计算语言学研究表明，可以用来检测不确定性。智能辅导系统的研究表明，不确定性是学习过程的一部分，并假设，以提高系统的有效性，这是至关重要的，以回应更多的正确性。然而，大多数现有的辅导系统只响应学生的正确性，很少有对照实验尚未调查是否也响应不确定性可以提高learning.This研究设计和实现两个不同的增强口语对话辅导系统，测试两个假设在辅导文学关于如何导师可以有效地应对不确定性和以上的正确性。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僵局，即，有机会增进了解。这个假设是用一个增强的系统版本来解决的，该版本以与当前处理不正确性相同的方式来处理不确定性（即，并附加子对话以增加理解）。第二个假设是，当学生不确定时，可以通过模拟人类导师对正确性变化的反应来开发更优化的反应。通过分析人类导师对话行为的反应（即内容和演示），学生的不确定性和以上的正确性在现有的辅导语料库，然后实现这些反应，在第二个增强的系统版本解决了这个假设。然后进行两个对照实验。第一个测试的相对影响的两个适应学习使用绿野仙踪版本的系统，与人类（向导）检测不确定性和执行语音识别和语言理解。第二个实验在真实的系统中测试了第一个实验中表现最好的适应的影响，该系统处理语音和语言，并以全自动的方式检测不确定性。该研究的主要智力贡献是证明在辅导过程中，通过适应学生的不确定性而不是正确性，是否可以实现学习的显著改善，通过在工作的口语对话系统中完全自动化和评估用户不确定性检测和自适应来推进现有技术，并研究在理想与实际系统条件下该自适应的任何不同效果。

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.