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ERIC Number: ED560879
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 10
Modeling Classroom Discourse: Do Models That Predict Dialogic Instruction Properties Generalize across Populations?
Samei, Borhan; Olney, Andrew M.; Kelly, Sean; Nystrand, Martin; D'Mello, Sidney; Blanchard, Nathan; Graesser, Art
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
It has previously been shown that the effective use of dialogic instruction has a positive impact on student achievement. In this study, we investigate whether linguistic features used to classify properties of classroom discourse generalize across different subpopulations. Results showed that the machine learned models perform equally well when trained and validated on different subpopulations. Correlation-Based Feature Subset evaluation revealed an inclusion relationship between different subsets in terms of their most predictive features. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Middle Schools; Secondary Education; Junior High Schools
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED); National Science Foundation (NSF)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305A130030; IIS1352207