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The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes

INTRODUCTION: The COVID-19 pandemic led to school closure and loss of in-person instruction during the 2019–2020 academic year across the United States, which had a profound impact on the reading development of beginning readers. In this study we tested if a research-informed educational technology...

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Autores principales: Richter, Caroline G., Siegelman, Noam, Mahaffy, Kelly, Van Den Bunt, Mark, Kearns, Devin M., Landi, Nicole, Sabatini, John, Pugh, Kenneth, Hoeft, Fumiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755674/
https://www.ncbi.nlm.nih.gov/pubmed/36533015
http://dx.doi.org/10.3389/fpsyg.2022.1001555
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author Richter, Caroline G.
Siegelman, Noam
Mahaffy, Kelly
Van Den Bunt, Mark
Kearns, Devin M.
Landi, Nicole
Sabatini, John
Pugh, Kenneth
Hoeft, Fumiko
author_facet Richter, Caroline G.
Siegelman, Noam
Mahaffy, Kelly
Van Den Bunt, Mark
Kearns, Devin M.
Landi, Nicole
Sabatini, John
Pugh, Kenneth
Hoeft, Fumiko
author_sort Richter, Caroline G.
collection PubMed
description INTRODUCTION: The COVID-19 pandemic led to school closure and loss of in-person instruction during the 2019–2020 academic year across the United States, which had a profound impact on the reading development of beginning readers. In this study we tested if a research-informed educational technology (EdTech) program–GraphoLearn–could help alleviate the COVID-19 slide. We also sought to understand the profiles of children who benefitted most from this EdTech program. METHODS: We tested participants’ (N = 172 K-2 children) early literacy skills using a standardized measure (STAR) before and after playing GraphoLearn, and used the pre to post difference as the dependent variable. We first compared children’s STAR actual and expected growth. Then we conducted a multiple regression analysis with data about engagement with GraphoLearn included as predictors. Additional predictors were extracted from GraphoLearn performance at study onset to assess children’s letter-sound knowledge, rime awareness, and word recognition. RESULTS: The difference between actual average reading growth and expected growth in a regular school year was not statistically significant. This suggests that children in our sample seem to be gaining reading skills as expected in a regular school year. Our multiple linear regression model (which accounted for R(2) = 48% of reading growth) showed that older children, with higher baseline GraphoLearn word recognition, who played more units in a fixed number of days, made significantly more early literacy progress. DISCUSSION: While lacking a control group, our preliminary results suggest that an EdTech program such as GraphoLearn may be a useful reading instructional tool during school shutdowns. In addition, our results suggest that practice with GraphoLearn was more effective and efficient when foundational instruction was already in place.
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spelling pubmed-97556742022-12-17 The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes Richter, Caroline G. Siegelman, Noam Mahaffy, Kelly Van Den Bunt, Mark Kearns, Devin M. Landi, Nicole Sabatini, John Pugh, Kenneth Hoeft, Fumiko Front Psychol Psychology INTRODUCTION: The COVID-19 pandemic led to school closure and loss of in-person instruction during the 2019–2020 academic year across the United States, which had a profound impact on the reading development of beginning readers. In this study we tested if a research-informed educational technology (EdTech) program–GraphoLearn–could help alleviate the COVID-19 slide. We also sought to understand the profiles of children who benefitted most from this EdTech program. METHODS: We tested participants’ (N = 172 K-2 children) early literacy skills using a standardized measure (STAR) before and after playing GraphoLearn, and used the pre to post difference as the dependent variable. We first compared children’s STAR actual and expected growth. Then we conducted a multiple regression analysis with data about engagement with GraphoLearn included as predictors. Additional predictors were extracted from GraphoLearn performance at study onset to assess children’s letter-sound knowledge, rime awareness, and word recognition. RESULTS: The difference between actual average reading growth and expected growth in a regular school year was not statistically significant. This suggests that children in our sample seem to be gaining reading skills as expected in a regular school year. Our multiple linear regression model (which accounted for R(2) = 48% of reading growth) showed that older children, with higher baseline GraphoLearn word recognition, who played more units in a fixed number of days, made significantly more early literacy progress. DISCUSSION: While lacking a control group, our preliminary results suggest that an EdTech program such as GraphoLearn may be a useful reading instructional tool during school shutdowns. In addition, our results suggest that practice with GraphoLearn was more effective and efficient when foundational instruction was already in place. Frontiers Media S.A. 2022-12-02 /pmc/articles/PMC9755674/ /pubmed/36533015 http://dx.doi.org/10.3389/fpsyg.2022.1001555 Text en Copyright © 2022 Richter, Siegelman, Mahaffy, Van Den Bunt, Kearns, Landi, Sabatini, Pugh and Hoeft. 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
Richter, Caroline G.
Siegelman, Noam
Mahaffy, Kelly
Van Den Bunt, Mark
Kearns, Devin M.
Landi, Nicole
Sabatini, John
Pugh, Kenneth
Hoeft, Fumiko
The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes
title The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes
title_full The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes
title_fullStr The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes
title_full_unstemmed The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes
title_short The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes
title_sort impact of computer–assisted technology on literacy acquisition during covid-19-related school closures: group–level effects and predictors of individual–level outcomes
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755674/
https://www.ncbi.nlm.nih.gov/pubmed/36533015
http://dx.doi.org/10.3389/fpsyg.2022.1001555
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