[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-599452-105":59,"doc-detail-599452-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","identifying-reading-fluency-in-pupils-with-and-without-dyslexia-using-a-machine-learning-model-on-texts-assessed-with-a-readability-application","Identifying Reading Fluency in Pupils with and without Dyslexia Using a Machine Learning Model on Texts Assessed with a Readability Application","","The study investigates how reading fluency can differentiate pupils with diagnosed dyslexia from peers without dyslexia using a machine learning model. Readability formulas are used to assess textual difficulty, while oral reading performance is captured through transcriptions of audio recordings. Data from 27 pupils aged 8–9 are analyzed, focusing on measurable aspects of speed, accuracy, and proper expression. The goal is objective discrimination between groups based on fluency signals derived from readability-assessed texts.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/identifying-reading-fluency-in-pupils-with-and-without-dyslexia-using-a-machine-learning-model-on-texts-assessed-with-a-readability-application/599452/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/identifying-reading-fluency-in-pupils-with-and-without-dyslexia-using-a-machine-learning-model-on-texts-assessed-with-a-readability-application/599452.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1790064749","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What reading abilities define reading fluency in this study?","Question",{"text":112,"@type":113},"Reading fluency is defined as reading speed, accuracy, and proper expression during oral reading.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study create an objective assessment of text difficulty?",{"text":117,"@type":113},"Text difficulty is quantified using readability formulas produced by a readability application, which extract features and output a difficulty score.",{"name":119,"@type":110,"acceptedAnswer":120},"What data and participants were used to train or evaluate the model?",{"text":121,"@type":113},"The analysis is based on transcriptions of audio-recorded oral readings from 27 pupils aged 8–9, including a dyslexia group and a control group without identified dyslexia.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},599452,1792190867,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},3985747859154,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","DOI: [https://doi.org/10.26529/cepsj.1367](https://doi.org/10.26529/cepsj.1367)  \nIdentifying Reading Fluency in Pupils with and without Dyslexia Using a Machine Learning Model on Texts Assessed with a Readability Application  \nJure Žabkar1, Tajda Urankar1, Karmen Javornik2 and Milena Košak Babuder*3  \n• Measurement of readability is an important tool for assessing reading disorders such as dyslexia. Among the screening procedures for dyslexia is the reading fluency test, which is defined as the ability to read with speed, accuracy and proper expression. The reading fluency test often consists of a sequence of unrelated written texts ranging from simple short sentences to more difficult and longer paragraphs. In psychological testing instruments, subjective text assessment is often replaced by objective readability formulas, e.g., the Automated Readability Index. Readability formulas extract multiple features from a given text and output a score indicating the difficulty of the text. The aim of the present study is to build a machine learning model that discriminates between pupils identified with dyslexia and a control group without dyslexia based on fluency in oral reading of texts assessed with a readability application developed within the project For the Quality of Slovenian Textbooks. We focus on differentiation between both groups of pupils by analysing data obtained from transcriptions of audio recordings of oral reading. The empirical study was conducted with 27 pupils aged  \n8 and 9 with officially diagnosed dyslexia and a control group without identified dyslexia.  \nKeywords: dyslexia, readability application, reading fluency, machine learning  \n1 Faculty of Computer and Information Science, University of Ljubljana, Slovenia.  \n2 Faculty of Education, University of Ljubljana, Slovenia.  \n3 *Corresponding Author. Faculty of Education, University of Ljubljana, Slovenia; [Milena.Kosak-Babuder@pef.uni-lj.si](Milena.Kosak-Babuder@pef.uni-lj.si).  \n234 identifying reading fluency in pupils with and without dyslexia using ...  \nPrepoznavanje tekočnosti branja pri učencih z disleksijoin brez nje z uporabo modela strojnega učenja na besedilih, ocenjenih z aplikacijo za berljivost  \nJure Žabkar, Tajda Urankar, Karmen Javornik in Milena Košak Babuder  \n• Merjenje berljivosti je pomembno orodje za ocenjevanje motenj branja, kot je disleksija. Med presejalnimi postopki za disleksijo je tudi preizkustekočnosti branja. Tekočnost branja je opredeljena kot sposobnost hitrega in natančnega branja ter pravilnega izražanja. Preizkus tekočnosti branja je pogosto sestavljen iz zaporedja nepovezanih zapisanih besedil, od preprostih kratkih povedi do zahtevnejših in daljših odstavkov. V psiholoških testih subjektivno ocenjevanje besedil pogosto nadomeščajo objektivne formule berljivosti, npr. avtomatski indeks berljivosti (Automated Readability Index) . Formule za berljivost iz danega besedilaizluščijo več značilnosti in izpišejo oceno, ki označuje težavnost besedila. Cilj te raziskave je zgraditi model strojnega učenja, ki bo na podlagitekočnosti ustnega/glasnega branja besedil, ocenjenih z aplikacijo zaocenjevanje berljivosti, razvite v okviru projekta Za kakovost slovenskihučbenikov (KaUč), razlikoval med učenci, pri katerih je bila ugotovljena disleksija, in kontrolno skupino učencev brez disleksije. Pri tem se osredinjamo na razlikovanje med obema skupinama učencev z analizo podatkov, pridobljenih s transkripcijami zvočnih posnetkov ustnega/ glasnega branja. V empirični raziskavi je sodelovalo 27 učencev, starih  \n8 in 9 let, s potrjeno disleksijo in kontrolna skupina brez ugotovljenedisleksije.  \nKljučne besede: disleksija, aplikacija za berljivost, tekočnost branja, strojno učenje  \nIntroduction  \nReading and writing are basic skills that are taken for granted in today’s society. They are key elements of literacy, enabling individuals to develop the skills of reflection, critique and empathy, leading to a sense of self-efficacy, ide","cbCaieOR5Dj8Z6le","https://ap.wps.com/l/cbCaieOR5Dj8Z6le","pdf",334802,24,"English","# Introduction\n# Research Aim and Approach\n## Readability and Fluency Concepts\n## Machine Learning Model Based on Oral Reading Data\n# Empirical Study Design\n## Participants and Grouping","[{\"question\":\"What reading abilities define reading fluency in this study?\",\"answer\":\"Reading fluency is defined as reading speed, accuracy, and proper expression during oral reading.\"},{\"question\":\"How does the study create an objective assessment of text difficulty?\",\"answer\":\"Text difficulty is quantified using readability formulas produced by a readability application, which extract features and output a difficulty score.\"},{\"question\":\"What data and participants were used to train or evaluate the model?\",\"answer\":\"The analysis is based on transcriptions of audio-recorded oral readings from 27 pupils aged 8–9, including a dyslexia group and a control group without identified dyslexia.\"}]","Identifying Reading Fluency in Pupils with and without Dyslexia Using a Machine Learning Model on Texts Assessed with a Readability Application | PDF",1792088797]