Embedding preschool assessment methods into digital learning games to predict early reading skills
The aim of this pilot study was to explore the predictive accuracy of computer-based assessment tasks (embedded within the GraphoLearn digital learning game platform) in identifying slow and normal readers. The results were compared to those obtained from the traditional paper-and-pencil tasks curre...
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University of Jyväskylä
2017-11-01
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Online Access: | https://humantechnology.jyu.fi/archive/vol-13/issue-2-1/puolakanaho_latvala |
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doaj-ce9e7576060c4d7da541848d18c0aa0f2020-11-24T20:40:42ZengUniversity of JyväskyläHuman Technology1795-68892017-11-0113221623610.17011/ht/urn.201711104212Embedding preschool assessment methods into digital learning games to predict early reading skillsAnne Puolakanaho0Juha-Matti Latvala1University of Jyväskylä, FinlandNiilo Mäki Institute, FinlandThe aim of this pilot study was to explore the predictive accuracy of computer-based assessment tasks (embedded within the GraphoLearn digital learning game platform) in identifying slow and normal readers. The results were compared to those obtained from the traditional paper-and-pencil tasks currently used to assess school readiness in Finland. The data were derived from a cohort of preschool-age children (mean age 6.7 years, N = 57) from a town in central Finland. A year later, at the end of first grade, participants were categorized as either slow (n = 11) or normal readers (n = 46) based on their reading scores. Logistic regression analyses indicated that computer tasks were as efficient as traditional methods in predicting reading outcomes, and that a single computer-based task—the letter–sound knowledge task,—provided an easy method of accurately predicting reading achievement (sensitivity 95.7%; specificity 81.8%). The study has practical implications in classrooms.https://humantechnology.jyu.fi/archive/vol-13/issue-2-1/puolakanaho_latvalacomputer-based assessmentpreschoolearly reading skillsslow readerspredictionletter knowledge |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anne Puolakanaho Juha-Matti Latvala |
spellingShingle |
Anne Puolakanaho Juha-Matti Latvala Embedding preschool assessment methods into digital learning games to predict early reading skills Human Technology computer-based assessment preschool early reading skills slow readers prediction letter knowledge |
author_facet |
Anne Puolakanaho Juha-Matti Latvala |
author_sort |
Anne Puolakanaho |
title |
Embedding preschool assessment methods into digital learning games to predict early reading skills |
title_short |
Embedding preschool assessment methods into digital learning games to predict early reading skills |
title_full |
Embedding preschool assessment methods into digital learning games to predict early reading skills |
title_fullStr |
Embedding preschool assessment methods into digital learning games to predict early reading skills |
title_full_unstemmed |
Embedding preschool assessment methods into digital learning games to predict early reading skills |
title_sort |
embedding preschool assessment methods into digital learning games to predict early reading skills |
publisher |
University of Jyväskylä |
series |
Human Technology |
issn |
1795-6889 |
publishDate |
2017-11-01 |
description |
The aim of this pilot study was to explore the predictive accuracy of computer-based assessment tasks (embedded within the GraphoLearn digital learning game platform) in identifying slow and normal readers. The results were compared to those obtained from the traditional paper-and-pencil tasks currently used to assess school readiness in Finland. The data were derived from a cohort of preschool-age children (mean age 6.7 years, N = 57) from a town in central Finland. A year later, at the end of first grade, participants were categorized as either slow (n = 11) or normal readers (n = 46) based on their reading scores. Logistic regression analyses indicated that computer tasks were as efficient as traditional methods in predicting reading outcomes, and that a single computer-based task—the letter–sound knowledge task,—provided an easy method of accurately predicting reading achievement (sensitivity 95.7%; specificity 81.8%). The study has practical implications in classrooms. |
topic |
computer-based assessment preschool early reading skills slow readers prediction letter knowledge |
url |
https://humantechnology.jyu.fi/archive/vol-13/issue-2-1/puolakanaho_latvala |
work_keys_str_mv |
AT annepuolakanaho embeddingpreschoolassessmentmethodsintodigitallearninggamestopredictearlyreadingskills AT juhamattilatvala embeddingpreschoolassessmentmethodsintodigitallearninggamestopredictearlyreadingskills |
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