Machine-Learned Computational Models Can Enhance the Study of Text and Discourse: A Case Study Using Eye Tracking to Model Reading Comprehension.
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...
| Publicado en: | Discourse Processes Vol. 57; no. 5/6; pp. 420 - 441 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jun/Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=143878323&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 143878323 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0163853X 7LQ jtl: Discourse Processes issn: 0163853X maglogo: N pubinfo: dt: Jun/Jul2020 vid: 57 iid: 5/6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 143878323 10.1080/0163853X.2020.1739600 ppf: 420 ppct: 21 formats: tig: atl: Machine-Learned Computational Models Can Enhance the Study of Text and Discourse: A Case Study Using Eye Tracking to Model Reading Comprehension. aug: au: D'Mello, Sidney K. Southwell, Rosy Gregg, Julie affil: Institute of Cognitive Science, University of Colorado Boulder su: Reading comprehension Eye tracking Receiver operating characteristic curves R-curves Eye movements Listening comprehension sug: subj: Reading comprehension Eye tracking Receiver operating characteristic curves R-curves Eye movements Listening comprehension ab: 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 =.902; r =.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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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