[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119157-en":3,"doc-seo-119157-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119157,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","School entry detection of struggling readers using gameplay data and machine learning","Reading difficulty detection at school entry remains error-prone. This study proposes a machine learning approach that analyzes process data generated by children playing GraphoGame, a literacy learning app, during the first months of schooling. The models are trained on rich in-game learning traces and combined with results from an end-of-year national screening test. The best models identify 75% of students at risk for developing later reading difficulties.","TYPE Original Research PUBLISHED 22 November 2024 DOI 10. 3389/feduc.2024.1487694  \nOPEN ACCESS  \nEDITED BY  \nMohammad Khalil,  \nUniversity of Bergen, Norway  \nREVIEWED BY  \nQuan Zhang,  \nJiaxing University, China Ioannis Dimakos, University of Patras, Greece  \n*CORRESPONDENCE  \nNjål Foldnes  \n [njal.foldnes@gmail.com](njal.foldnes@gmail.com)  \nRECEIVED 28 August 2024  \nACCEPTED 23 October 2024  \nPUBLISHED 22 November 2024  \nCITATION  \nFoldnes N, Uppstad PH, Grønneberg S and Thomson JM (2024) School entry detection of struggling readers using gameplay data and machine learning. Front. Educ. 9:1487694 .  \ndoi: 10.3389/feduc.2024.1487694  \nCOPYRIGHT  \n© 2024 Foldnes, Uppstad, Grønneberg and Thomson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . 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.  \nSchool entry detection of struggling readers using gameplay data and machine learning  \nNjål Foldnes1*, Per Henning Uppstad1 , Ste􀀀en Grønneberg2 and Jenny M. Thomson3  \n1 Norwegian Centre for Reading Education and Research, University of Stavanger, Stavanger, Norway,  \n2 BI Norwegian Business School, Oslo, Norway, 3 School of Allied Health Professions, Nursing and Midwifery, The University of She􀀈eld, She􀀈eld, United Kingdom  \nIntroduction: Current methods for reading di􀀈culty 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.  \nMethods: 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.  \nResults: The best machine learning models correctly identified 75% of the students at risk for developing reading di􀀈culties.  \nDiscussion: The present study is among the first to investigate the potential of predicting emerging learning di􀀈culties using machine learning on game process data.  \nKEYWORDS  \nearly detection, reading, machine learning, process data, reading di􀀈culties  \n1 Introduction  \nLearning to read is a crucial skill acquired during the 􀀂rst years of school and children with di􀀔culties in acquiring this skill may face adverse educational, vocational, and health outcomes (McLaughlin et al., 2014; DeWalt et al., 2004) . While researchers have pointed to the relative ease with which a majority of students learn to read (Shankweilerand Liberman, 1989), the same process is extremely e􀀓ortful for struggling readers and more so for students with dyslexia. If di􀀔culties in this learning process are not identi􀀂ed promptly and ameliorated, the performance gap between struggling readers and their non-struggling peers will only widen, a phenomenon coined, the “Matthew E􀀓ect” (Stanovich, 2009) . Much research has therefore focused on how to optimize early and accurate identi􀀂cation of struggling readers. The most common form of early detection tool is currently a one-time multi-component assessment where early reading skills and their precursors are examined (Thompson et al., 2015; Phillips et al., 2009) . Learning to read involves mastering a number of di􀀓erent component skills over a protracted time course; these component skills include letter-sound decoding, whole word recognition, reading 􀀃uency, and the ultimate goal of reading—comprehension (Scarborough et al., 2009) . As the statistical analysis capacity of reading research has expanded, we are increasingly able to identify and quantify the relative contributions of these di􀀓erent factors at di􀀓erent p","cbCaieNW6BfpD6dZ","https://ap.wps.com/l/cbCaieNW6BfpD6dZ","pdf",983498,1,11,"English","en",105,"# Introduction\n## Methods\n## Results\n## Discussion\n## Keywords","[{\"question\":\"What problem does the study address?\",\"answer\":\"Early detection of reading difficulties at school entry is prone to errors, leading to false positives and false negatives for students’ risk status.\"},{\"question\":\"How does the study collect data for prediction?\",\"answer\":\"Norwegian first graders played GraphoGame in class daily for 10 minutes over five weeks, generating detailed game process data later used for model training.\"},{\"question\":\"How accurate are the best machine learning models?\",\"answer\":\"The best models correctly identified 75% of students at risk for developing reading difficulties.\"}]","School entry detection of struggling readers using gameplay data and machine learning | PDF",1785722795,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"school-entry-detection-of-struggling-readers-using-gameplay-data-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/school-entry-detection-of-struggling-readers-using-gameplay-data-and-machine-learning/119157/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"Early detection of reading difficulties at school entry is prone to errors, leading to false positives and false negatives for students’ risk status.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study collect data for prediction?",{"text":80,"@type":76},"Norwegian first graders played GraphoGame in class daily for 10 minutes over five weeks, generating detailed game process data later used for model training.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the best machine learning models?",{"text":84,"@type":76},"The best models correctly identified 75% of students at risk for developing reading difficulties.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]