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

这项研究调查了在计算机辅导过程中，对学生不确定性的反应是否比正确性更能改善学习。这项研究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线索(例如语调、停顿、词语使用)，计算语言学研究表明，这些线索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，并假设要提高系统的有效性，关键是对更多的正确做出反应。然而，大多数现有的辅导系统只对学生的正确性做出反应，很少有对照实验研究对不确定性的反应是否能改善学习。本研究设计并实现了对口语对话辅导系统的两种不同的增强，以检验辅导文献中关于教师如何有效地应对不确定性的两个假设。第一个假设是，学生的不确定性和不正确性都代表着学习僵局，即提高理解的机会。这一假设是通过增强的系统版本来处理的，该版本以与当前处理不正确的方式相同的方式来处理不确定性(即，通过附加子对话来增加理解)。第二个假设是，当学生不确定时，通过模拟人类导师对正确性的反应是如何变化的，可以开发出更优化的反应。通过在现有的辅导语料库中分析人类教师对话行为对学生不确定性和正确性的反应(即内容和呈现)，然后在第二个增强的系统版本中实施这些反应来解决这一假设。然后进行了两个对照实验。第一个测试是测试两种适应方式对使用绿野仙踪版本的系统学习的相对影响，人类(精灵)检测不确定性并执行语音识别和语言理解。第二个实验在真实系统的背景下测试了第一个实验中表现最好的适应的影响，系统处理语音和语言，并以全自动的方式检测不确定性。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是否实现了显著的改进，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的检测和适应来促进最新技术的发展，并调查这种适应在理想和实际系统条件下的任何不同的效果。

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.