AutoTutor

From Wikipedia, the free encyclopedia

AutoTutor
AutoTutor screenshot
Developed by Institute for Intelligent Systems
OS Microsoft Windows
Genre Intelligent tutoring system and Educational software
Website http://www.autotutor.org/

AutoTutor is an intelligent tutoring system that helps students learn Newtonian physics, computer literacy, and critical thinking topics through tutorial dialogue in natural language [1]. [2]. [3]. [4].

AutoTutor simulates the discourse patterns of human tutors and also incorporates a number of ideal tutoring strategies. It presents a series of challenging problems (or questions) that require verbal explanations and reasoning in an answer. It engages in a collaborative, mixed initiative dialog while constructing the answer, a process that typically takes approximately 100 conversational turns. AutoTutor speaks the content of its turns through an animated conversational agent with a speech engine, some facial expressions, and rudimentary gestures. For some topics, there are graphical displays, animations of causal mechanisms, or interactive simulation environments. AutoTutor tracks the cognitive states of the learner by analyzing the content of the dialogue history. AutoTutor dynamically selects the words and statements in each conversational turn in a fashion that is sensitive to what the learner knows. The most current AutoTutor system also adapts to the learner’s emotional states [5] in addition to their cognitive states.

The impact of AutoTutor in facilitating the learning of deep conceptual knowledge has been validated in over a dozen experiments on college students for topics in introductory computer literacy[6] and conceptual physics[7]. Tests of AutoTutor have produced effect sizes of 0.4 to 1.5 (a mean of 0.8 which is approximately equal to a letter grade), depending on the learning measure, the comparison condition, the subject matter, and version of AutoTutor.

AutoTutor was developed by researchers at the Institute for Intelligent Systems at the University of Memphis.The research on AutoTutor was supported by the National Science Foundation (NSF), the Institute of Education Sciences (IES), the Department of Defense (DoD), and the Office of Naval Research (ONR).

[edit] References

  1. ^ Graesser, A.C., Chipman, P., Haynes, B.C., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions in Education, 48, 612–618
  2. ^ Graesser, A.C., Person, N., Harter, D., & the Tutoring Research Group (2001. Teaching tactics and dialog in AutoTutor. International Journal of Artificial Intelligence in Education, 12, 257–279.
  3. ^ Graesser, A.C., VanLehn, K., Rose, C., Jordan, P., & Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22, 39–51.
  4. ^ Graesser, A.C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R., & the Tutoring Research Group (1999). Auto Tutor: A simulation of a human tutor. Journal of Cognitive Systems Research, 1, 35–51.
  5. ^ D'Mello, S. K., Craig, S. D., Gholson, B., Franklin, S., Picard, R.,& Graesser, A. C. (2005). Integrating affect sensors in an intelligent tutoring system. In Affective Interactions: The Computer in the Affective Loop Workshop at 2005 International conference on Intelligent User Interfaces (pp.7-13) New York: AMC Press
  6. ^ Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H., Ventura, M., Olney, A., & Louwerse, M.M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180-193.
  7. ^ VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P., Olney, A., & Rose, C.P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 3-62

[edit] External links