TY - GEN
T1 - What'd you say again? Recurrence quantification analysis as a method for analyzing the dynamics of discourse in a reading strategy tutor
AU - Allen, Laura K.
AU - Likens, Aaron
AU - Perret, Cecile
AU - McNamara, Danielle
N1 - Funding Information:
This research was supported in part by: IES R305G020018-02, IES R305G040046, IES R305A080589, and NSF REC0241133, and NSF IIS-0735682. Opinions, conclusions, or recommendations do not necessarily reflect the views of the IES or NSF. We also thank Matt Jacovina, Scott Crossley, Rod Roscoe and Jianmin Dai for their help with the data collection and developing the ideas found in this paper.
Publisher Copyright:
© 2017 ACM.
PY - 2017/3/13
Y1 - 2017/3/13
N2 - In this study, we investigated the degree to which the cognitive processes in which students engage during reading comprehension could be examined through dynamical analyses of their natural language responses to texts. High school students (n = 142) generated typed self-explanations while reading a science text. They then completed a comprehension test that measured their comprehension at both surface and deep levels. The recurrent patterns of the words in students' self-explanations were first visualized in recurrence plots. These visualizations allowed us to qualitatively analyze the different self-explanation processes of skilled and less skilled readers. These recurrence plots then allowed us to calculate recurrence indices, which represented the properties of these temporal word patterns. Results of correlation and regression analyses revealed that these recurrence indices were significantly related to the students' comprehension scores at both surface- and deep levels. Additionally, when combined with summative metrics of word use, these indices were able to account for 32% of the variance in students' overall text comprehension scores. Overall, our results suggest that recurrence quantification analysis can be utilized to guide both qualitative and quantitative assessments of students' comprehension.
AB - In this study, we investigated the degree to which the cognitive processes in which students engage during reading comprehension could be examined through dynamical analyses of their natural language responses to texts. High school students (n = 142) generated typed self-explanations while reading a science text. They then completed a comprehension test that measured their comprehension at both surface and deep levels. The recurrent patterns of the words in students' self-explanations were first visualized in recurrence plots. These visualizations allowed us to qualitatively analyze the different self-explanation processes of skilled and less skilled readers. These recurrence plots then allowed us to calculate recurrence indices, which represented the properties of these temporal word patterns. Results of correlation and regression analyses revealed that these recurrence indices were significantly related to the students' comprehension scores at both surface- and deep levels. Additionally, when combined with summative metrics of word use, these indices were able to account for 32% of the variance in students' overall text comprehension scores. Overall, our results suggest that recurrence quantification analysis can be utilized to guide both qualitative and quantitative assessments of students' comprehension.
KW - Corpus linguistics
KW - Dynamics
KW - Intelligent tutoring systems
KW - Natural language processing
KW - Reading
KW - Stealth assessment
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U2 - 10.1145/3027385.3027445
DO - 10.1145/3027385.3027445
M3 - Conference contribution
AN - SCOPUS:85016509055
T3 - ACM International Conference Proceeding Series
SP - 373
EP - 382
BT - LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
PB - Association for Computing Machinery
T2 - 7th International Conference on Learning Analytics and Knowledge, LAK 2017
Y2 - 13 March 2017 through 17 March 2017
ER -