School entry detection of struggling readers using gameplay data and machine learning - Foldnes, Njål; Uppstad, Per Henning; Grønneberg, Steffen; Thomson, Jenny M. (Frontiers in Education, 2024)
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.