TY - GEN
T1 - Modeling Comprehension Processes via Automated Analyses of Dialogism
AU - Dascalu, Mihai
AU - Allen, Laura K.
AU - McNamara, Danielle S.
AU - Trausan-Matu, Stefan
AU - Crossley, Scott A.
N1 - Funding Information:
The work presented in this paper was partially funded by the University Politehnica of Bucharest through the “Excellence Research Grants” Program, UPB – GEX Contract number 12/26.09.2016, by the EC H2020 project RAGE (Realising and Applied Gaming Eco-System) http://www.rageproject .eu/ Grant agreement No 644187, as well as funding to Arizona State University (IES 305A130124, IES R305A120707, NSF 1417997, NSF 1418378, ONR 12249156, ONR N00014140343). Any opinions or conclusions expressed are those of the authors and do not represent views of the IES, NSF, or ONR.
Funding Information:
The work presented in this paper was partially funded by the University Politehnica of Bucharest through the “Excellence Research Grants” Program, UPB - GEX Contract number 12/26.09.2016, by the EC H2020 project RAGE (Realising and Applied Gaming Eco-System) http://www.rageproject.eu/Grant agreement No 644187, as well as funding to Arizona State University (IES 305A130124, IES R305A120707, NSF 1417997, NSF 1418378, ONR 12249156, ONR N00014140343). Any opinions or conclusions expressed are those of the authors and do not represent views of the IES, NSF, or ONR.
Publisher Copyright:
© CogSci 2017.
PY - 2017
Y1 - 2017
N2 - Dialogism provides the grounds for building a comprehensive model of discourse and it is focused on the multiplicity of perspectives (i.e., voices). Dialogism can be present in any type of text, while voices become themes or recurrent topics emerging from the discourse. In this study, we examine the extent that differences between self-explanations and think-alouds can be detected using computational textual indices derived from dialogism. Students (n = 68) read a text about natural selection and were instructed to generate self-explanations or think-alouds. The linguistic features of these text responses were analyzed using ReaderBench, an automated text analysis tool. A discriminant function analysis using these features correctly classified 80.9% of the students' assigned experimental conditions (self-explanation vs. think aloud). Our results indicate that self-explanation promotes text processing that focuses on connected ideas, rather than separate voices or points of view covering multiple topics.
AB - Dialogism provides the grounds for building a comprehensive model of discourse and it is focused on the multiplicity of perspectives (i.e., voices). Dialogism can be present in any type of text, while voices become themes or recurrent topics emerging from the discourse. In this study, we examine the extent that differences between self-explanations and think-alouds can be detected using computational textual indices derived from dialogism. Students (n = 68) read a text about natural selection and were instructed to generate self-explanations or think-alouds. The linguistic features of these text responses were analyzed using ReaderBench, an automated text analysis tool. A discriminant function analysis using these features correctly classified 80.9% of the students' assigned experimental conditions (self-explanation vs. think aloud). Our results indicate that self-explanation promotes text processing that focuses on connected ideas, rather than separate voices or points of view covering multiple topics.
KW - comprehension
KW - dialogism
KW - discourse analysis
KW - polyphonic model
KW - self-explanation
KW - think-aloud
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M3 - Conference contribution
AN - SCOPUS:85022224176
T3 - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition
SP - 1884
EP - 1889
BT - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
T2 - 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Y2 - 26 July 2017 through 29 July 2017
ER -