Teaching with the Help of Talking Heads Arthur C. Graesser, Xiangen Hu Department of Psychology, 202 Psychology Building, University of Memphis, Memphis, TN 38152-3230,
[email protected],
[email protected] Natalie Person Department of Psychology, Rhodes College, 2000 N. Parkway, Memphis, TN 38112,
[email protected] Abstract Talking heads were integrated with two learning systems. In AutoTutor, students learn about computer literacy by holding a conversation with a student. AutoTutor is an animated pedagogical agent that asks deep reasoning questions and engages in a mixed initiative dialog as answers emerge. Students type in information via keyboard whereas AutoTutor delivers discourse sensitive contributions with facial expressions, synthesized speech, and gestures. In the Human Use Regulatory Affairs (HURA) Advisor, high ranking officers in the military learn about the ethical use of human subjects on a web site with a conversational navigational agent.
1. Introduction Researchers have recently developed computergenerated, animated, talking heads that have facial features synchronized with speech and in some cases appropriate gestures [1, 6]. These agents are pedagogical agents if they are designed to promote learning. Conversational pedagogical agents have the potential to help learning in two fundamental ways. The first is to serve as a conversation partner that conveys subject matter knowledge and that produces substantive utterances in a turn-by-turn conversation. The second is to serve as a navigational aid that points out what actions users might take while they interact with the human-computer interface in pursuit of goals. We have developed systems that illustrate these two uses of conversational agents: AutoTutor and the HURA Advisor.
2. AutoTutor
9]. The dialog tactics are based on a previous project that dissected 100 hours of naturalistic tutoring sessions [3]. AutoTutor is currently targeted for college students in a introductory computer literacy course, who learn the fundamentals of hardware, operating systems, and the Internet. Evaluations of AutoTutor have shown that the tutoring system improves learning and memory of the lessons by .5 to .6 standard deviation units compared with an experience of rereading a chapter. The AutoTutor architecture is currently being incorporated in a conceptual physics tutor that resides on the web [4]. Instead of being a mere information delivery system, AutoTutor serves as a collaborative scaffold that assists the student in actively constructing knowledge. A dialog manager coordinates the conversation that occurs between a learner and a pedagogical agent, whereas lesson content and world knowledge are represented in a curriculum script and in latent semantic analysis, i.e., a statistical representation in high dimensional space [7]. The tutor presents dialog moves that are both responsive to the student and that facilitate the student in actively constructing knowledge. The tutor dialog move categories include feedback on the student information (positive, negative, neutral), pumps (“What else?”), prompts for specific information ("The primary memories of the CPU are ROM and _____"), hints (“What about the hard disk?”), assertions (“CD ROM is another storage medium.”), corrections, and summaries. AutoTutor’s dialog moves are delivered by a talking head that synchronizes synthesized speech, facial expressions, and some rudimentary gestures. Microsoft Agent is currently being used as the talking head with synthesized speech, with parameters of the facial expressions and intonation being generated by production rules.
AutoTutor simulates the discourse patterns and pedagogical strategies of a typical human tutor [2, 5,
Proceedings of the IEEE International Conference on Advanced Learning Techniques (ICALT’01) 0-7695-1013-2/01 $10.00 © 2001 IEEE
3. HURA Advisor The HURA Advisor is being developed to help users learn the policies and regulations that are relevant to the use of human subjects in research that is funded by the U.S. Department of Defense [8]. The web site has several modules, including a historical overview, cases, ethical issues, lessons, decision support, mechanisms for querying documents, lessons, a glossary, a library of documents and links to other web sites. However, the most salient components, from the present standpoint, are the talking head and the dialog advancer network (DAN). The talking head guides the user in navigating through the system by giving short messages (typical1y 2 words or less) that guide the user on what to do next. The navigational agent is particularly helpful for new users of a site who are overloaded with display content and who have difficulty knowing how to achieve their goals by taking effective action. The DAN manages the interaction between the user and the HURA Advisor by considering alternative pathways in a state transition network.
[6] W.L. Johnson, J.W. Rickel, and J.C. Lester, “Animated pedagogical agents: Face-to-face interaction in interactive learning environments”, International Journal of Artificial Intelligence in Education, 2000, pp. 47-78. [7] T.K. Landauer, P.W. Foltz, D. Laham, “An introduction to latent semantic analysis”, Discourse Processes, 1998, pp. 259-284. [8] N.K. Person, B. Gholson, B., S. Craig, X. Hu, C. Stewart, and A.C. Graesser, “HURA Advisor: An interactive webbased agent that optimizes information retrieval in a multimedia environment”, Proceedings of ED MEDIA 2001, in press. [9] N.K. Person, A.C. Graesser, R.J. Kreuz, V. Pomeroy, and the Tutoring Research Group, “Simulating human tutor dialog moves in AutoTutor”, International Journal of Artificial Intelligence in Education, in press.
4. Acknowledgements Research on AutoTutor was supported on grants from the National Science Foundation (SBR 9720314) and the Office of Naval Research (N00014-00-1-0600). The HURA Advisor was supported by the Institute for Defense Analyses (AK-2-1801), under Robert Foster and Dexter Fletcher, on a contract to Thoughtware Corporation.
5. References [1] J. Cassell, and K.R. Thorisson, “The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents”, Applied Artificial Intelligence, 1999, pp. 519-538. [2] A.C., Graesser, N.K. Person, D. Harter, and the Tutoring Research Group, “Teaching tactics and dialog in AutoTutor”, International Journal of Artificial Intelligence in Education, in press. [3] A.C. Graesser, N.K. Person, and J.P. Magliano, “Collaborative dialog patterns in naturalistic one-on-one tutoring”, Applied Cognitive Psychology, 1995, pp. 359-387. [4] A.C. Graesser, K. VanLehn, C. Rose, P. Jordan, and D. Harter, “Intelligent tutoring systems with conversational dialogue”, AI Magazine, in press. [5] A.C. Graesser, K. Wiemer-Hastings, P. Wiemer-Hastings, R. Kreuz, and the Tutoring Research Group, “AutoTutor: A simulation of a human tutor”, Journal of Cognitive Systems Research, 1999, pp. 35-51.
Proceedings of the IEEE International Conference on Advanced Learning Techniques (ICALT’01) 0-7695-1013-2/01 $10.00 © 2001 IEEE
