Developing Auto Tutor for Computer Literacy and Physics

University Of Memphis, Memphis TN

Investigators

Abstract

The Tutoring Research Group at the University of Memphis has developed a computer tutor (called AutoTutor) that simulates the discourse patterns and pedagogical strategies of unaccomplished human tutors. The typical tutor in a school system is unaccomplished in the sense that the tutor has had no training in tutoring strategies and has only introductory-to-intermediate knowledge about the topic. The development of AutoTutor was funded by an NSF grant (SBR 9720314, in the Learning and Intelligent Systems program). The discourse patterns and pedagogical strategies in AutoTutor were based on a previous project that dissected 100 hours of naturalistic tutoring sessions. AutoTutor is currently targeted for college students in introductory computer literacy courses, who learn the fundamentals of hardware, operating systems, and the Internet. Instead of merely being an information delivery system, AutoTutor serves as a discourse prosthesis or collaborative scaffold that assists the student in actively constructing knowledge. AutoTutor presents questions and problems from a curriculum script, attempts to comprehend learner contributions that are entered by keyboard, answers student questions, formulates dialog moves that are sensitive to the learner's contributions (such as short feedback, pumps, prompts, assertions, corrections, and hints), and delivers the dialog moves with a talking head. The talking head displays emotions, produces synthesized speech with discourse-sensitive intonation, and points to entities on graphical displays. AutoTutor has seven modules: a curriculum script, language extraction, speech act classification, latent semantic analysis (a statistical representation of domain knowledge), topic selection, dialog management, and a talking head. Evaluations of AutoTutor have shown that the tutoring system improves learning with an effect size that is comparable to typical human tutors in school systems, but not as high as accomplished human tutors and intelligent tutoring systems. The dialog moves of AutoTutor blend in the discourse context very smoothly because students cannot distinguish whether a speech act was generated by AutoTutor or a human tutor. The proposed research will substantially expand the capabilities of AutoTutor by designing the discourse to handle more sophisticated tutoring mechanisms. These mechanisms should further enhance the active construction of knowledge. One enhancement is to get the student to articulate more knowledge, with more formal, symbolic, and precise specification; if the student doesn't say it, it is not considered covered by AutoTutor. Another enhancement is to set up the dialog so that it guides the user in manipulating a 3-dimensional microworld of a physical system; the student attempts to simulate a new state in the physical system by manipulating parameters, inputs, and formulae. The proposed research will develop AutoTutor in the domains of both computer literacy and Newtonian physics, so we will have some foundation for evaluating the generality of AutoTutor's mechanisms. AutoTutor has been designed to be generic, rather than domain-specific; an authoring tool will be developed that makes it easy for instructors to prepare new material on new topics. After the new versions of AutoTutor are completed, we will evaluate its effectiveness on learning gains, conversational smoothness, and pedagogical quality. During the course of achieving these engineering and educational objectives, the proposed project will conduct basic research in cognitive psychology, discourse processes, computer science, and computational linguistics. This research cuts across quadrant 2 (behavioral, cognitive, affective, and social aspects of human learning) and quadrant 3 (SMET learning in formal and informal educational settings).