Implications and Identification of Specific Learning Disability Using Weighted Ensemble Learning Model.
Background: Learning disabilities, categorized as neurodevelopmental disorders, profoundly impact the cognitive development of young children. These disabilities affect text comprehension, reading, writing and problem‐solving abilities. Specific learning disabilities (SLDs), most notably dyslexia an...
| Publicado en: | Child: Care, Health & Development Vol. 51; no. 1; pp. 1 - 14 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183897804&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183897804 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03051862 6NX jtl: Child: Care, Health & Development issn: 03051862 maglogo: Y pubinfo: dt: Jan2025 vid: 51 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183897804 183897804 183897804 10.1111/cch.70026 183897804 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Implications and Identification of Specific Learning Disability Using Weighted Ensemble Learning Model. aug: au: Alzahrani, Sultan Algahtani, Faris affil: Department of Special Education, University of Jeddah, Jeddah, Saudi Arabia sug: subj: Learning Disorders Diagnosis Students with Disabilities Psychosocial Factors Artificial Intelligence Utilization Child Development Skill Acquisition Human Child Comparative Studies Dyscalculia Diagnosis Agraphia Diagnosis Dyslexia Diagnosis Reading Writing Sensitivity and Specificity Child: 6-12 years ab: Background: Learning disabilities, categorized as neurodevelopmental disorders, profoundly impact the cognitive development of young children. These disabilities affect text comprehension, reading, writing and problem‐solving abilities. Specific learning disabilities (SLDs), most notably dyslexia and dysgraphia, can significantly hinder students' academic achievement. The timely identification of such students is crucial in providing them with essential assistance and facilitating the development of skills required to overcome their limitations. Methods: The proposed model, which utilizes artificial intelligence (AI), plays a crucial role in identifying and diagnosing SLDs. This system allows students suspected of having SLD to engage in personalized exams and unique tasks tailored to their SLDs. The data generated from these activities, including performance scores and completion times, are fed into the proposed weighted ensemble learning (WEL) variation of the XGBoost (XGB) algorithm. The WEL‐XGB model is designed to detect learning challenges by analysing these datasets, even when dealing with imbalanced data. Results: The WEL‐XGB model has been successfully integrated into a user‐friendly application for assessing reading and writing impairments. The proposed model not only identifies SLD but also offers tailored recommendations for effective instructional strategies for parents and educators. Comparative analyses with other machine learning (ML) and deep learning (DL) models demonstrate the superiority of the WEL‐XGB model, which achieved an accuracy rate of 98.7% for dyslexia datasets and 99.08% for dysgraphia datasets. Conclusion: The proposed WEL‐XGB model effectively identifies learning disabilities in children, offering a powerful tool for both diagnosis and instructional support. Its high accuracy rates underscore its potential to revolutionize the assessment and intervention process for dyslexia and dysgraphia, benefiting students, parents and educators alike. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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