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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8358684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT litingxuan identifyingmixturecomponentsfromlargescalekeystrokelogdata |