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

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.