ERIC Number: ED539087
Record Type: Non-Journal
Publication Date: 2009-Jul
Abstractor: As Provided
Reference Count: 9
Does Self-Discipline Impact Students' Knowledge and Learning?
Gong, Yue; Rai, Dovan; Beck, Joseph E.; Heffernan, Neil T.
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)
In this study, we are interested to see the impact of self-discipline on students' knowledge and learning. Self-discipline can influence both learning rate as well as knowledge accumulation over time. We used a Knowledge Tracing (KT) model to make inferences about students' knowledge and learning. Based on a widely used questionnaire, we measured students' level of self-discipline. When we analyzed the relation of students' self-discipline with their knowledge attributes, we found that high self-discipline students had significantly higher initial knowledge, but there is no consistent relationship of learning while using the tutor. Moreover, higher self-discipline students seemed more careful with respect to making careless mistakes. (Contains 5 figures and 5 tables.) [Additional funding was provided by the Fulbright Program. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
Descriptors: Data Analysis, Self Control, Knowledge Level, Learning, Intelligent Tutoring Systems, Grade 8, Students, Questionnaires, Deception, Inferences, Models, Prior Learning
International Working Group on Educational Data Mining. Available from: International Educational Data Mining Society. e-mail: email@example.com; Web site: http://www.educationaldatamining.org
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: Elementary Education; Grade 8; Junior High Schools; Middle Schools
Sponsor: National Science Foundation; Department of Education (ED); Office of Naval Research (ONR)
Authoring Institution: International Working Group on Educational Data Mining