[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124066-en":3,"doc-seo-124066-105":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},124066,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Understanding the learning of disabled students - An exploration of machine learning approaches","Research is limited in showing how learning analytics can support students with learning disabilities, making it essential to examine demographic differences between disabled and non-disabled learners and how these differences relate to achievement. Machine learning can extract insights from large, growing education datasets to model score outcomes and the number of attempts needed to complete modules. The study evaluates AdaBoost, Random Forest, and kNN; AdaBoost achieves the highest prediction accuracy.","“© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”  \nUnderstanding the learning of disabled students: An exploration of machine learning approaches  \nMaliha Homaira  \nFaculty of Engineering & Information Technology University of Technology Sydney Sydney, Australia [maliha.homaira@student.uts.edu.au](maliha.homaira@student.uts.edu.au)  \nAmara Atif  \nFaculty of Engineering & Information Technology University of Technology Sydney Sydney, Australia [amara.atif@uts.edu.au](amara.atif@uts.edu.au)  \nLisa-Angelique Lim Connected Intelligence Centre University of Technology Sydney Sydney, Australia [lisa-angelique.lim@uts.edu.au](lisa-angelique.lim@uts.edu.au)  \nAbstract— Research is limited in demonstrating how learning analytics can support students with a learning disability. It is vital to scrutinise the demographics of students with and without disabilities and their effect on performance. Machine learning algorithms can provide valuable insights by mining this expanding education data. This study aims to analyse the relationship between disabled and nondisabled students' demographic with their scores and the number of attempts to complete a module to develop prediction models. Three models-Adaptive Boosting, Random Forest, and K nearest neighbour-were used. The results found that the Adaptive boosting algorithm delivered the highest prediction accuracy.  \nKeywords—Learning Analytics, Learning Disability, Demographic, Prediction, Adaboost, Random Forest, kNN, Machine Learning  \nI. INTRODUCTION  \nAs technology is developing at a tremendous pace, the use of technology in learning has also developed over time. Along with this, Educational Data Mining (EDM) advancements have seen the rise of learning analytics (LA) as a related field [20] . These developments make it plausible to identify at-risk students, predict their scores, build adaptive learning environments, and facilitate personalised feedback. It enables to development of the quality of education [22] .  \nDespite these developments, little consideration has been given to students with learning disabilities [11] . The term\"learning disability\" is interpreted differently in different countries. In Australia, the term learning disability refers to students with severe difficulty in literacy and numeracy; most of the time, learning disabilities are regarded to have a neurological basis [14] . Dyslexia, dysgraphia, and dyscalculia are a few of the learning disabilities. In most cases, students do not feel it safe or necessary to disclose their disability [13] . At the sametime, learning-disabled students suffer from issues that can impede their learning, such as stress and anxiety, lack of selfesteem, poor study-life balance, personal relationship problems, and financial pressures [16] . Therefore, there is an urgent need to identify students' learning disabilities in a way that allows them to feel safe and support their learning in response to specific disabilities.  \nFor this purpose, developing a fair machine learning model is crucial; [19], however, such models are sparse in the literature.  \nMoreover, the accuracy of predictive models is often in question [19]. In this paper, we take a first step toward developing a fair model for predicting students' on a publicly available dataset, Open University Learning Analytics Dataset (OULAD), Open University, as we could not find any dataset available in the Australian context. This paper uses three prediction models to identify relationships between the demographic factor (disabled and non-disabled students) scores on different assessments. The results from our exploratory analysis provide an initial ins","cbCaio3ZHP3gFRWg","https://ap.wps.com/l/cbCaio3ZHP3gFRWg","pdf",370291,1,7,"English","en",105,"# Introduction\n## Background and motivation\n## Objective and dataset\n# Literature Review\n## Learning Disability\n## Machine Learning Approaches","[{\"question\":\"Why is studying learning analytics for disabled students considered important?\",\"answer\":\"The study argues that evidence on how learning analytics supports learners with learning disabilities is limited, so demographic characteristics and their impact on performance must be examined.\"},{\"question\":\"Which machine learning models were used, and which one performed best?\",\"answer\":\"The paper uses Adaptive Boosting (AdaBoost), Random Forest, and k-nearest neighbor (kNN). AdaBoost delivers the highest prediction accuracy.\"},{\"question\":\"What dataset and prediction targets are used in the research?\",\"answer\":\"The research uses the Open University Learning Analytics Dataset (OULAD) and builds prediction models relating disabled vs. non-disabled demographics to assessment scores and the number of module attempts.\"}]","Understanding the learning of disabled students - An exploration of machine learning approaches | PDF",1785820173,18,{"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},"understanding-the-learning-of-disabled-students-an-exploration-of-machine-learning-approaches","",{"@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/understanding-the-learning-of-disabled-students-an-exploration-of-machine-learning-approaches/124066/",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","2026-08-04",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},"Why is studying learning analytics for disabled students considered important?","Question",{"text":75,"@type":76},"The study argues that evidence on how learning analytics supports learners with learning disabilities is limited, so demographic characteristics and their impact on performance must be examined.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used, and which one performed best?",{"text":80,"@type":76},"The paper uses Adaptive Boosting (AdaBoost), Random Forest, and k-nearest neighbor (kNN). AdaBoost delivers the highest prediction accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and prediction targets are used in the research?",{"text":84,"@type":76},"The research uses the Open University Learning Analytics Dataset (OULAD) and builds prediction models relating disabled vs. non-disabled demographics to assessment scores and the number of module attempts.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]