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Identifying Mixture Components From Large-Scale Keystroke Log Data

In a computer-based writing assessment, massive keystroke log data can provide real-time information on students’ writing behaviors during text production. This research aims to quantify the writing process from a cognitive standpoint. The hope is that the quantification may contribute to establish...

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Autor principal: Li, Tingxuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358684/
https://www.ncbi.nlm.nih.gov/pubmed/34393876
http://dx.doi.org/10.3389/fpsyg.2021.628660
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author Li, Tingxuan
author_facet Li, Tingxuan
author_sort Li, Tingxuan
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description In a computer-based writing assessment, massive keystroke log data can provide real-time information on students’ writing behaviors during text production. This research aims to quantify the writing process from a cognitive standpoint. The hope is that the quantification may contribute to establish a writing profile for each student to represent a student’s learning status. Such profiles may contain richer information to influence the ongoing and future writing instruction. Educational Testing Service (ETS) administered the assessment and collected a large sample of student essays. The sample used in this study contains nearly 1,000 essays collected across 24 schools in 18 U.S. states. Using a mixture of lognormal models, the main findings show that the estimated parameters on pause data are meaningful and interpretable with low-to-high cognitive processes. These findings are also consistent across two writing genres. Moreover, the mixture model captures aspects of the writing process not examined otherwise: (1) for some students, the model comparison criterion favored the three-component model, whereas for other students, the criterion favored the four-component model; and (2) students with low human scores have a wide range of values on the mixing proportion parameter, whereas students with higher scores do not possess this pattern.
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spelling pubmed-83586842021-08-13 Identifying Mixture Components From Large-Scale Keystroke Log Data Li, Tingxuan Front Psychol Psychology In a computer-based writing assessment, massive keystroke log data can provide real-time information on students’ writing behaviors during text production. This research aims to quantify the writing process from a cognitive standpoint. The hope is that the quantification may contribute to establish a writing profile for each student to represent a student’s learning status. Such profiles may contain richer information to influence the ongoing and future writing instruction. Educational Testing Service (ETS) administered the assessment and collected a large sample of student essays. The sample used in this study contains nearly 1,000 essays collected across 24 schools in 18 U.S. states. Using a mixture of lognormal models, the main findings show that the estimated parameters on pause data are meaningful and interpretable with low-to-high cognitive processes. These findings are also consistent across two writing genres. Moreover, the mixture model captures aspects of the writing process not examined otherwise: (1) for some students, the model comparison criterion favored the three-component model, whereas for other students, the criterion favored the four-component model; and (2) students with low human scores have a wide range of values on the mixing proportion parameter, whereas students with higher scores do not possess this pattern. Frontiers Media S.A. 2021-07-29 /pmc/articles/PMC8358684/ /pubmed/34393876 http://dx.doi.org/10.3389/fpsyg.2021.628660 Text en Copyright © 2021 Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Li, Tingxuan
Identifying Mixture Components From Large-Scale Keystroke Log Data
title Identifying Mixture Components From Large-Scale Keystroke Log Data
title_full Identifying Mixture Components From Large-Scale Keystroke Log Data
title_fullStr Identifying Mixture Components From Large-Scale Keystroke Log Data
title_full_unstemmed Identifying Mixture Components From Large-Scale Keystroke Log Data
title_short Identifying Mixture Components From Large-Scale Keystroke Log Data
title_sort identifying mixture components from large-scale keystroke log data
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358684/
https://www.ncbi.nlm.nih.gov/pubmed/34393876
http://dx.doi.org/10.3389/fpsyg.2021.628660
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