ERIC Number: EJ908619
Record Type: Journal
Publication Date: 2011-Apr
Abstractor: As Provided
Reference Count: 0
Polite Web-Based Intelligent Tutors: Can They Improve Learning in Classrooms?
McLaren, Bruce M.; DeLeeuw, Krista E.; Mayer, Richard E.
Computers & Education, v56 n3 p574-584 Apr 2011
Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge), the "politeness effect" was investigated in the context of web-based intelligent tutoring systems, software that runs on the Internet and employs artificial intelligence and learning science techniques to help students learn. The goal was to pinpoint the appropriate conditions for having the web-based tutors provide polite feedback and hints (e.g., "Let's convert the units of the first item") versus direct feedback and hints (e.g., "Convert the units of the first item now"). In the study presented in this paper, 132 high school students in a classroom setting, grouped as low and high prior knowledge learners according to a pre-intervention knowledge questionnaire, did not benefit more from polite feedback and hints than direct feedback and hints on either an immediate or delayed posttest, both of which contained near transfer and conceptual test items. Of particular interest and contrary to an earlier lab study, low prior knowledge students did not benefit more from using the polite version of a tutor. On the other hand, a politeness effect was observed for the students who made the most errors during the intervention, a different proxy for low prior knowledge, hinting that even in a classroom setting, politeness may be beneficial for more needy students. This article presents and discusses these results, as well as discussing the politeness effect more generally, its theoretical underpinnings, and future directions. (Contains 6 tables and 2 figures.)
Descriptors: Feedback (Response), Test Items, Intervention, Intelligent Tutoring Systems, Prior Learning, Computer Software, Artificial Intelligence, Tutors, Internet, High School Students, Questionnaires, Speech Communication, Learning Strategies
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools
Authoring Institution: N/A