NotesFAQContact Us
Collection
Advanced
Search Tips
ERIC Number: ED539066
Record Type: Non-Journal
Publication Date: 2009-Jul
Pages: 10
Abstractor: As Provided
Reference Count: 22
ISBN: N/A
ISSN: N/A
Differences between Intelligent Tutor Lessons, and the Choice to Go Off-Task
Baker, Ryan S. J. d.
International Working Group on Educational Data Mining, Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009)
Recent research has suggested that differences between intelligent tutor lessons predict a large amount of the variance in the prevalence of gaming the system. Within this paper, we investigate whether such differences also predict how much students choose to go off-task, and if so, which differences predict how much off-task behavior will occur. We utilize an enumeration of the differences between intelligent tutor lessons, the Cognitive Tutor Lesson Variation Space 1.1 (CTLVS1.1), to identify 79 differences between tutor lessons, within 20 lessons from an intelligent tutoring system for Algebra. We utilize a machine-learned detector of off-task behavior to predict 58 students' off-task behavior within that tutor, in each lesson. Surprisingly, the best model predicting off-task behavior from lesson features contains only one feature: lessons that involve equation-solving. We discuss possible explanations for this finding, and further studies that could shed light on this relationship. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
International Working Group on Educational Data Mining. Available from: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: Grade 10; Grade 9; High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation; Pittsburgh Science of Learning Center
Authoring Institution: International Working Group on Educational Data Mining
Identifiers - Location: Pennsylvania