Exploring User Satisfaction in a Tutorial Dialogue System

Abstract User satisfaction is a common evaluation metric in task-oriented dialogue systems, whereas tutorial dialogue systems are often evaluated in terms of student learning gain. However, user satisfaction is also important for such systems, since it may predict technology acceptance. We present a detailed satisfaction questionnaire used in evaluating the BEETLE II system (REVU-NL), and explore the underlying components of user satisfaction using factor analysis. We demonstrate interesting patterns of interaction between interpretation quality, satisfaction and the dialogue policy, highlighting the importance of more finegrained evaluation of user satisfaction

The Impact of Interpretation Problems on Tutorial Dialogue

Supporting natural language input may improve learning in intelligent tutoring systems. However, interpretation errors are unavoidable and require an effective recovery policy. We describe an evaluation of an error recovery policy in the BEE-TLE II tutorial dialogue system and discuss how different types of interpretation problems affect learning gain and user satisfaction. In particular, the problems arising from student use of non-standard terminology appear to have negative consequences. We argue that existing strategies for dealing with terminology problems are insufficient and that improving such strategies is important in future ITS research.

Beetle II: an adaptable tutorial dialogue system

We present BEETLE II, a tutorial dialogue system which accepts unrestricted language input and supports experimentation with different dialogue strategies. Our first system evaluation compared two dialogue policies. The resulting corpus was used to study the impact of different tutoring and error recovery strategies on user satisfaction and student interaction style. It can also be used in the future to study a wide range of research issues in dialogue systems.

Content, Social, and Metacognitive Statements: An Empirical Study Comparing Human-Human and Human-Computer Tutorial Dialogue

We present a study which compares human-human computer-mediated tutoring with two computer tutoring systems based on the same materials but differing in the type of feedback they provide. Our results show that there are significant differences in interaction style between human-human and human-computer tutoring, as well as between the two computer tutors, and that different dialogue characteristics predict learning gain in different conditions. We show that there are significant differences in the non-content statements that students make to human and computer tutors, but also to different types of computer tutors. These differences also affect which factors are correlated with learning gain and user satisfaction. We argue that ITS designers should pay particular attention to strategies for dealing with negative social and metacognitive statements, and also conduct further research on how interaction style affects human-computer tutoring. © 2010 Springer-Verlag Berlin Heidelberg

Dealing with Interpretation Errors in Tutorial Dialogue.

We describe an approach to dealing with interpretation errors in a tutorial dialogue system. Allowing students to provide explanations and generate contentful talk can be helpful for learning, but the language that can be understood by a computer system is limited by the current technology. Techniques for dealing with understanding problems have been developed primarily for spoken dialogue systems in informationseeking domains, and are not always appropriate for tutorial dialogue. We present a classification of interpretation errors and our approach for dealing with them within an implemented tutorial dialogue system

Diagnosing natural language answers to support adaptive tutoring

Understanding answers to open-ended explanation questions is important in intelligent tutoring systems. Existing systems use natural language techniques in essay analysis, but revert to scripted interaction with short-answer questions during remediation, making adapting dialogue to individual students difficult. We describe a corpus study that shows that there is a relationship between the types of faulty answers and the remediation strategies that tutors use; that human tutors respond differently to different kinds of correct answers; and that re-stating correct answers is associated with improved learning. We describe a design for a diagnoser based on this study that supports remediation in open-ended questions and provides an analysis of natural language answers that enables adaptive generation of tutorial feedback for both correct and faulty answers

Talk Like an Electrician: Student Dialogue Mimicking Behavior in an Intelligent Tutoring System

Abstract. Students entering a new field must learn to speak the specialized language of that field. Previous research using automated measures of word overlap has found that students who modify their language to align more closely to a tutor's language show larger overall learning gains. We present an alternative approach that assesses syntactic as well as lexical alignment in a corpus of human-computer tutorial dialogue. We found distinctive patterns differentiating high and low achieving students. Our high achievers were most likely to mimic their own earlier statements and rarely made mistakes when mimicking the tutor. Low achievers were less likely to reuse their own successful sentence structures, and were more likely to make mistakes when trying to mimic the tutor. We argue that certain types of mimicking should be encouraged in tutorial dialogue systems, an important future research direction