TY - GEN
T1 - Recurrence quantification analysis as a method for studying text comprehension dynamics
AU - Likens, Aaron D.
AU - McCarthy, Kathryn S.
AU - Allen, Laura K.
AU - McNamara, Danielle
N1 - Funding Information: This research was supported in part by the Institute of Education Sciences (R305A130124) and the Office of Naval Research (ONR N000141712300). Any opinions, conclusions, or recommendations expressed are those of the authors and do not necessarily represent views of either IES or ONR. Publisher Copyright: © 2018 Association for Computing Machinery.
PY - 2018/3/7
Y1 - 2018/3/7
N2 - Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school students (n = 232) generated self-explanations while they read a science text. Recurrence Plots were generated to show qualitative differences in students’ linguistic sequences that were later quantified by indices derived by Recurrence Quantification Analysis (RQA). To predict self-explanation quality, RQA indices, along with summative measures (i.e., number of words, mean word length, and type-token ration) and general reading ability, served as predictors in a series of regression models. Regression analyses indicated that recurrence in students’ self-explanations significantly predicted human rated self-explanation quality, even after controlling for summative measures of self-explanations, individual differences, and the text that was read (R2 = 0.68). These results demonstrate the utility of RQA in exposing and quantifying temporal structure in student’s self-explanations. Further, they imply that dynamical systems methodology can be used to uncover important processes that occur during comprehension.
AB - Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school students (n = 232) generated self-explanations while they read a science text. Recurrence Plots were generated to show qualitative differences in students’ linguistic sequences that were later quantified by indices derived by Recurrence Quantification Analysis (RQA). To predict self-explanation quality, RQA indices, along with summative measures (i.e., number of words, mean word length, and type-token ration) and general reading ability, served as predictors in a series of regression models. Regression analyses indicated that recurrence in students’ self-explanations significantly predicted human rated self-explanation quality, even after controlling for summative measures of self-explanations, individual differences, and the text that was read (R2 = 0.68). These results demonstrate the utility of RQA in exposing and quantifying temporal structure in student’s self-explanations. Further, they imply that dynamical systems methodology can be used to uncover important processes that occur during comprehension.
KW - Dynamical systems theory
KW - Reading
KW - Recurrence quantification analysis
KW - Self-explanation
KW - Text comprehension
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U2 - 10.1145/3170358.3170407
DO - 10.1145/3170358.3170407
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 111
EP - 120
BT - Proceedings of the 8th International Conference on Learning Analytics and Knowledge
PB - Association for Computing Machinery
T2 - 8th International Conference on Learning Analytics and Knowledge, LAK 2018
Y2 - 5 March 2018 through 9 March 2018
ER -