School Entry Detection of Struggling Readers using Gameplay Data and Machine Learning

Abstract

Current 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. The app was played in class daily for ten minutes by 1676 Norwegian first graders, over a five-week period during the first months of schooling, generating rich process data. Models were trained on the process data combined with results from the endof-year national screening test. The best machine learning models correctly identified 75% of the students at risk for developing reading difficulties. The present study is among the first to investigate the potential of predicting emerging learning difficulties using machine learning on game process data.