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Articles
Published: 2017-11-01

Embedding preschool assessment methods into digital learning games to predict early reading skills

Department of Psychology University of Jyväskylä, Finland
Niilo Mäki Institute, Finland
computer-based assessment preschool early reading skills slow readers prediction letter knowledge

Abstract

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.

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How to Cite

Puolakanaho, A., & Latvala, J. (2017). Embedding preschool assessment methods into digital learning games to predict early reading skills. Human Technology, 13(2), 216–236. https://doi.org/10.17011/ht/urn.201711104212