Automatic coding of questioning patterns in knowledge building discourse

Jin Mu, Jan Van Aalst, Carol Chan, Ella Fu

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
7 Downloads (Pure)

Abstract

We propose a novel method for identifying questioning patterns, which are assumed to be one of the essential factors indicating the quality of knowledge-building discourse. The underlying principle of the proposed method is to extract syntactic and sematic information before segmenting the raw data and annotating them according to a multilayer framework called ACODEA. As a bottom layer of the framework, the "pre-coding" phase makes it possible to translate the raw data into machine-readable and contextindependent language, and to make Natural Language Processing tools aware of users' preferences and underpinning mechanisms of identifying the desired pattern. Explorative but promising evidence is reported toward a more comprehensive perspective by combining qualitative and quantitative methods to analyze the discourse data. Given those findings, we argue in favor of mixed methods of content analysis and they further generated directions for future methodological development and empirical applications.

Original languageEnglish
Title of host publicationProceedings of International Conference of the Learning Sciences, ICLS. Volume 1
Pages333-340
Number of pages8
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event11th International Conference of the Learning Sciences, ICLS 2014 - Boulder, United States
Duration: 23 Jun 201427 Jun 2014
Conference number: 11

Conference

Conference11th International Conference of the Learning Sciences, ICLS 2014
Abbreviated titleICLS
CountryUnited States
CityBoulder
Period23/06/1427/06/14

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