Frontiers in Education (Nov 2024)

School entry detection of struggling readers using gameplay data and machine learning

  • Njål Foldnes,
  • Per Henning Uppstad,
  • Steffen Grønneberg,
  • Jenny M. Thomson

DOI
https://doi.org/10.3389/feduc.2024.1487694
Journal volume & issue
Vol. 9

Abstract

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IntroductionCurrent methods for reading difficulty risk detection at school entry remain error-prone. We present a novel approach utilizing machine learning analysis of data from GraphoGame, a fun and pedagogical literacy app.MethodsThe app was played in class daily for 10 min by 1,676 Norwegian first graders, over a 5-week period during the first months of schooling, generating rich process data. Models were trained on the process data combined with results from the end-of-year national screening test.ResultsThe best machine learning models correctly identified 75% of the students at risk for developing reading difficulties.DiscussionThe present study is among the first to investigate the potential of predicting emerging learning difficulties using machine learning on game process data.

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