Work

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

Year: 2024

Type: article

Abstract: Introduction 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. Methods The 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. Results The best machine learning models correctly identified 75% of the students at risk for developing reading difficulties. Discussion The present study is among the first to investigate the potential of predicting emerging learning difficulties using machine learning on game process data.

Source: Frontiers in Education

DOI: 10.3389/feduc.2024.1487694

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

Institutions: University of Stavanger, BI Norwegian Business School, University of Sheffield

Language: English

Topic: Reading and Literacy Development

Subfield: Developmental and Educational Psychology

Field: Psychology

Domain: Social Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,285