Foldnes, N., Uppstad, P.H., Grønneberg, S. et al. (1 more author) (2024) School entry detection of struggling readers using gameplay data and machine learning. Frontiers in Education, 9. 1487694. ISSN 2504-284X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Foldnes, Uppstad, Grønneberg and Thomson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | early detection; reading; machine learning; process data; reading difficulties |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Health Sciences School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2024 09:37 |
Last Modified: | 29 Nov 2024 09:37 |
Status: | Published |
Publisher: | Frontiers Media SA |
Refereed: | Yes |
Identification Number: | 10.3389/feduc.2024.1487694 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220162 |