Machine-Learned Computational Models Can Enhance the Study of Text and Discourse: A Case Study Using Eye Tracking to Model Reading Comprehension

Autor/inn/en	D'Mello, Sidney K.; Southwell, Rosy; Gregg, Julie
Titel	Machine-Learned Computational Models Can Enhance the Study of Text and Discourse: A Case Study Using Eye Tracking to Model Reading Comprehension
Quelle	In: Discourse Processes: A Multidisciplinary Journal, 57 (2020) 5-6, S.420-440 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0163-853X
DOI	10.1080/0163853X.2020.1739600
Schlagwörter	Discourse Analysis; Computer Software; Intervention; Computational Linguistics; Eye Movements; Reading Comprehension; Models; Prediction; Scores; Case Studies; Comparative Analysis; Correlation; Private Colleges; College Students; Reading Processes; Classification; Accuracy; Measurement + Suchen Sie Ihr Suchwort? Diskursanalyse; Linguistics; Computerlinguistik; Augenbewegung; Leseverstehen; Analogiemodell; Vorhersage; Case study; Fallstudie; Case Study; Korrelation; Privathochschule; Collegestudent; Leseprozess; Classification system; Klassifikation; Klassifikationssystem; Messverfahren
Abstract	We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and interactivity, and when researchers aspire to take advantage of "big data." Being fully instantiated computer programs, MLCMs can also be used for autonomous assessment and real-time intervention. We illustrate these ideas in the context of an eye movement-based MLCM of textbase comprehension during reading along connected text. Using a dataset where 104 participants read a 6,500-word text, we trained Random Forests models to predict comprehension scores from six eye movement features. The models were highly accurate (area under the receiver operating characteristic curve = 0.902; r = 0.661), robust, and generalized across participants, suggesting possible use in future studies. We conclude by arguing for an increased role of MLCMs in the future of discourse research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01