The primary goal of this study was to investigate the role of feedback in an intelligent tutoring system (ITS) with natural language dialogue. One core component of tutorial dialogue is feedback, which carries the primary burden of informing students of their performance. AutoTutor is an ITS with tutorial dialogue that was developed at the University of Memphis. This article addresses the effectiveness of two types of feedback (content & progress) while college students interact with AutoTutor on conceptual physics. Content feedback provides qualitative information about the domain content and its accuracy as it is covered in a tutoring session. Progress feedback is a quantitative assessment of the student's advancement through the material being covered (i.e., how far the student has come and how much farther they have to go). A factorial design was used that manipulated the presence or absence of both feedback categories (content & progress). Each student interacted with one of four different versions of AutoTutor that varied the type of feedback. Data analyses showed significant effects of feedback on learning and motivational measures, supporting the notion that “content matters” and the adage “no pain, no gain.”
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com