Hedged Responses and Expressions of Affect in Human/Human and Human/Computer Tutorial Interactions

Hedged Responses and Expressions of Affect in Human/Human and Human/Computer Tutorial Interactions Khelan Bhatt (bhatkhe@iit.edu) Martha Evens (evens@iit.edu) Shlomo Argamon (argamon@iit.edu) Computer Science Department, Illinois Institute of Technology 10 West 31st Street, Chicago, IL 60616 Abstract periment which showed students to be polite and friendly to human tutors when they met with them face-to-face, but decidedly rude to the same tutors when communicating with them over a slow computer link and told that a machine was tutoring them. The current study has potentially important implications for the future development of our ITS. Investigation of how human tutors respond to student misery, frustration, and rage is the first step toward making systems more friendly and responsive. By contrast, our system's current response to student hedges and expressions of affect (as to any input it does not understand) is to tell the student what kind of input it is expect- ing. The result is dialogue like this: We study how students hedge and express affect when interacting with both humans and computer systems, during keyboard-mediated natural language tutoring sessions in medicine. We found significant differences in such student behavior linked to whether the tutor was human or a computer. Students hedge and apologize often to human tutors, but very rarely to computer tutors. The type of expressions also differed—overt hostility was not encountered in human tutoring sessions, but was a major component in computer-tutored sessions. Little gender-linking of hedging behavior was found, contrary to expectations based on prior studies. A weak gender-linked effect was found for affect in human tutored sessions. Introduction How people interact with computers is of clear importance to the design of effective computer interfaces. The book The Media Equation (Reeves & Nass 1996) claims that people treat computer systems essentially the same as they treat people, though more recent work (Shecht- man & Horowitz 2003; Goldstein et al., 2002) has raised serious questions about this conclu- sion. Differences between how people respond to human beings and how they respond to com- puters have been informally documented since the first experiments with natural language inter- faces (Thompson, 1980). A better elucidation of the issues may improve intelligent systems de- sign. Specifically, understanding these issues bet- ter may aid in the development of more effective tutoring systems. In this paper, we study the differences between student reactions to our In- telligent Tutoring System (ITS), CIRCSIM- Tutor (Michael et al., 2003), and the human tu- tors on which it was modeled. Our goal is to characterize student hedges and expressions of affect and try to determine how our ITS could understand them and respond effectively. We are motivated by experiments (Fox 1993) that suggest such differences for ITSs that carry out a natural language dialogue with the student. Fox carried out a “Wizard-of-Oz” ex- Student: Clueless! Tutor: Please respond with prediction table parameters. Better understanding of how and when students express affect in tutoring sessions and the func- tions of such expressions in the discourse may lead to improvements in student modeling and hence tutoring effectiveness. Background Thompson’s (1980) system was a pioneering natural-language interface designed to help U.S. Navy personnel load cargo onto ships. It thus attempted to delete all affective remarks, to avoid confusing the parser. Although the sys- tem was quite effective at its task, most of its affective input consisted of curses. By contrast, chat-oriented natural language interaction pro- grams like ELIZA (Weizenbaum, 1966) and PARRY (Colby, 1975), can impress their users with simulated charm and intelligence, despite a lack of any deep understanding. Similarly, phy- sicians experienced the natural language interface of Shortliffe's (1982) MYCIN and ONCOCIN programs as attractive, even though