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ERIC Number: ED566380
Record Type: Non-Journal
Publication Date: 2014-Jul
Pages: 4
Abstractor: As Provided
Reference Count: 14
ISBN: N/A
ISSN: N/A
Domain Independent Assessment of Dialogic Properties of Classroom Discourse
Samei, Borhan; Olney, Andrew M.; Kelly, Sean; Nystrand, Martin; D'Mello, Sidney; Blanchard, Nathan; Sun, Xiaoyi; Glaus, Marcy; Graesser, Art
Grantee Submission, Paper presented at the International Conference on Educational Data Mining (7th, London, United Kingdom, Jul 4-7, 2014)
We present a machine learning model that uses particular attributes of individual questions asked by teachers and students to predict two properties of classroom discourse that have previously been linked to improved student achievement. These properties, uptake and authenticity, have previously been studied by using trained observers to live-code classroom instruction. As a first-step in automating the coding of classroom discourse, we model question properties based on the features of individual questions, without any information about the context or domain. We then compare the machine-coded results to two referents: human-coded individual questions and "gold standard" codes from existing data. The performance achieved by the models is as good as human experts on the comparable task of coding individual questions out of context. Yet ultimately, this study highlights the need to draw on contextualizing information in order to most completely identify question properties associated with individual questions. [This paper was published in: "Proceedings of the Seventh International Conference on Educational Data Mining (EDM) (7th, London, United Kingdom, July 4-7, 2014)" p233-236 (see ED558339).]
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
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