Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics [Konferenzbericht] Paper presented at the International Conference on Learning Analytics & Knowledge (8th, Sydney, New South Wales, Australia, Mar 7-9, 2018).

Autor/inn/en	Likens, Aaron D.; McCarthy, Kathryn S.; Allen, Laura K.; McNamara, Danielle D.
Titel	Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics [Konferenzbericht] Paper presented at the International Conference on Learning Analytics & Knowledge (8th, Sydney, New South Wales, Australia, Mar 7-9, 2018).
Quelle	(2018), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Reading Comprehension; High School Students; Content Area Reading; Sciences; Reading Skills; Predictor Variables; Summative Evaluation; Intelligent Tutoring Systems; Educational Technology; Technology Uses in Education; Questionnaires; Reading Tests; Logical Thinking; Graphs; Statistical Analysis; Sentences; Qualitative Research; Gates MacGinitie Reading Tests + Suchen Sie Ihr Suchwort? Leseverstehen; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Sinnerfassendes Lesen; Science; Wissenschaft; Reading skill; Lesefertigkeit; Prädiktor; Intelligentes Tutorsystem; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Fragebogen; Lesetest; Grafische Darstellung; Statistische Analyse; Sentence analysis; Satzanalyse; Qualitative Forschung
Abstract	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(R[superscript 2]= 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. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01