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ERIC Number: ED539084
Record Type: Non-Journal
Publication Date: 2009-Jul
Pages: 10
Abstractor: As Provided
Reference Count: 16
Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning
Feng, Mingyu; 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)
A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by different methods of teaching the same skill, relative to each other. We compare our results with a previous study, which used classical analysis techniques and reported no main effect. Our results show that there is a marginal difference, suggesting giving students scaffolding questions is less effective at promoting student learning than providing them delayed feedback. Our study utilizes learning decomposition, an easier and quicker approach of evaluating the quality of ITS interventions than experimental studies. We also demonstrate the usage of computer-intensive approach, bootstrapping, for hypothesis testing in educational data mining area. (Contains 4 tables and 1 footnote.) [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:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: Grade 8; Junior High Schools; Middle Schools
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED); Office of Naval Research (ONR); National Science Foundation; Spencer Foundation
Authoring Institution: International Working Group on Educational Data Mining
Identifiers - Location: Massachusetts