[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-187049-en":3,"doc-seo-187049-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},187049,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","International Journal of Special Education - Volume 41 - Issue 2 - 2026","A study presents an Intelligent Adaptive Learning and Monitoring System (IALMS) for learners with special educational needs (SEN). It defines SEN personas and assigns mean usefulness scores, then details module functions: a Learner Profile Engine for disability-aware segmentation, a Content Repository & Adaptation Engine using NLP to simplify and adjust reading levels with audio/visual alternatives, and a recommendation and pathway engine for ZPD-calibrated sequencing. Real-time learning analytics monitor engagement and risk to trigger early intervention workflows.","| Persona | SEN Profile | Mean Usefulness Score (1–5) | Key IALMS Adaptive Behaviours Demonstrated |\n| --- | --- | --- | --- |\n| Aisha, 22, MBA Year 1 | Dyslexia + ADHD | 4.58 (SD = 0.19) | ALPS audio preference captured; case text simplified to GR Level 8; chunked pathway; microassessments; attention-lapse monitoring |\n| Rajan, 25, BBA Year 3 | ASD Level 1 | 4.52 (SD = 0.22) | Systemising style flag; explicit task structure; visual schedule overlay; sequential pathway with clear prerequisites; solo assessment alternative |\n| Priya, 28, Executive MBA | Partial Visual\u003Cbr>Impairment | 4.64 (SD = 0.17) | Screen-reader optimised content; all graphics alt-texted; audio-first pathway; text-only assessments; full content accessibility confirmed |\n\n| IALMS Module | Primary ML Algorithm(s) | Input Data | SEN Accommodation Function |\n| --- | --- | --- | --- |\n| Learner Profile Engine (LPE) | K-means Clustering; Rule-based LNF | ALPS survey; preassessment; accessibility preferences | Disability-aware learner segmentation; needs flagging with explicit consent |\n| Content Repository & Adaptation Engine (CRAE) | NLP Pipeline (BERT, T5); Flesch-Kincaid scoring | Curriculum content; multimedia; assessment items | Text simplification; multimodal content generation; reading level adjustment; audio/visual alternatives |\n| ML Recommendation | Hybrid Collaborative | Interaction logs; | Personalised content sequencing; |\n| & Pathway Engine | Filtering; Deep Q- | completion rates; | ZPD-calibrated difficulty; |\n| (MRPE) | Network (DQN) | performance scores | engagement optimisation |\n| \u003Cbr>Real-Time Learning | \u003Cbr>LSTM Networks; | Time-series engagement | Academic risk prediction; |\n| Analytics Dashboard | Random Forest; | data; assessment results; | trajectory monitoring; overload |\n| (RLAD) | Isolation Forest | session logs | detection; early intervention alerts |\n| Faculty & Admin Coordination Interface (FACI) | Explainable AI (SHAP); dashboard visualisation | RLAD outputs; institutional data; faculty inputs | Interpretable risk alerts; accommodation workflow; curriculum adaptation recommendations |\n\n| Disability Category | LPE Profile Flag | CRAE Adaptation | MRPE Pathway Adjustment | RLAD Monitoring Focus |\n| --- | --- | --- | --- | --- |\n| Dyslexia | High extraneous load; low reading fluency | Text simplification; audio alternatives; font spacing | Reduced readingheavy sequences; extended time allocation | Reading speed deceleration; incomplete content access |\n| ADHD | Variable attention; high engagement variance | Shorter chunks; microassessments; gamification elements | Interleaved variety; spaced repetition scheduling | Session length; offtask navigation; assessment timing |\n| Autism Spectrum\u003Cbr>Disorder | Systemising\u003Cbr>preference; routine sensitivity | Explicit structure; visual schedules; reduced ambiguity | Sequential pathways; explicit prerequisite mapping | Rigid navigation; deviation from expected patterns |\n\n\n| Visual\u003Cbr>Impairment | Screen reader dependency; audio preference | Full audio narration; alttext; no image-only content | Audio-first sequences; textbased assessments only | Screen reader event logs; content access success |\n| --- | --- | --- | --- | --- |\n| Hearing Impairment | Caption dependency; no audio-only access | Auto-captioned video; visual-first content | Captioned multimedia; textbased discussion options | Caption accuracy; video completion rates |\n| Dyscalculia | Quantitative processing difficulty | Numerical scaffolding; worked examples; calculator support | Extended quantitative pacing; additional worked LOs | Quantitative performance; timeon-task in finance modules |","cbCaigZXxXIBvbga","https://ap.wps.com/l/cbCaigZXxXIBvbga","pdf",722802,3,1,14,"English","en",105,"# IALMS Persona Profiles\n## Adaptive behaviours and usefulness scores\n# IALMS Module Architecture\n## Learner Profile Engine (LPE)\n## Content Repository & Adaptation Engine (CRAE)\n## ML Recommendation and Pathway Engine (MRPE)\n## Real-Time Learning Analytics Dashboard (RLAD)\n## Faculty & Admin Coordination Interface (FACI)\n# Disability Categories and Adaptations\n## Dyslexia\n## ADHD\n## Autism Spectrum Disorder\n## Visual impairment\n## Hearing impairment\n## Dyscalculia","[{\"question\":\"What is the role of the Learner Profile Engine (LPE) in IALMS?\",\"answer\":\"LPE segments learners by disability-aware flags and accessibility preferences. It uses survey and preassessment inputs and obtains explicit consent for needs flagging.\"},{\"question\":\"How does the Content Repository \\u0026 Adaptation Engine (CRAE) adapt learning content?\",\"answer\":\"CRAE applies NLP techniques to simplify text and adjust reading levels, and generates multimodal alternatives. It supports audio-first access and ensures graphics have appropriate alt-text.\"},{\"question\":\"How does IALMS monitor learner risk during real-time learning?\",\"answer\":\"RLAD analyzes time-series engagement and session logs to predict academic risk and detect overload or navigation issues. It triggers early intervention alerts and informs accommodation workflows through FACI.\"}]","International Journal of Special Education - Volume 41 - Issue 2 - 2026 | PDF",1788379882,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"international-journal-of-special-education-volume-41-issue-2-2026","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/international-journal-of-special-education-volume-41-issue-2-2026/187049/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-06","2026-09-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the role of the Learner Profile Engine (LPE) in IALMS?","Question",{"text":76,"@type":77},"LPE segments learners by disability-aware flags and accessibility preferences. It uses survey and preassessment inputs and obtains explicit consent for needs flagging.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the Content Repository & Adaptation Engine (CRAE) adapt learning content?",{"text":81,"@type":77},"CRAE applies NLP techniques to simplify text and adjust reading levels, and generates multimodal alternatives. It supports audio-first access and ensures graphics have appropriate alt-text.",{"name":83,"@type":74,"acceptedAnswer":84},"How does IALMS monitor learner risk during real-time learning?",{"text":85,"@type":77},"RLAD analyzes time-series engagement and session logs to predict academic risk and detect overload or navigation issues. 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