Autism spectrum disorder–level prediction and personalized education planning using TabNet / A. NITHYA in Autism, 30-1 (January 2026)  

Public ISBD [article]  inAutism > 30-1 (January 2026) . - p.176-186 Titre : Autism spectrum disorder–level prediction and personalized education planning using TabNet Type de document : texte imprimé Auteurs : A. NITHYA, Auteur ; V. SIVASANKARAN, Auteur Article en page(s) : p.176-186 Langues : Anglais (eng) Mots-clés : autism  educational plan  recommendation  rule-based mapping  TabNet  teaching methods Résumé : Students with autism spectrum disorder have an impact on their social, communication, and behavioral skills. Existing studies primarily focus on autism detection and diagnosis but lack effective approaches for predicting autism spectrum disorder levels and linking them to personalized educational strategies. This research aims to develop an autism-level categorization and a tailored education plan recommendation system for autistic students. The proposed methodology involves collecting a data set with attributes such as verbal ability, social interaction, sensory sensitivity, and attention span for students with autism spectrum disorder. These features are preprocessed and used to train a TabNet model to categorize the autism level. The system recommends a personalized education plan through a web application, based on prediction. This study uniquely integrates autism spectrum disorder-level prediction with education planning, achieving an accuracy of 99.37% and precision of 98.91% using the Autism Spectrum Classification for Education Planning data set. This shows the proposed model effectively categorizes autism levels and provides an education plan recommendation system for autistic students.Lay abstract Autism spectrum disorder (ASD) is a critical neurodevelopmental disorder affecting the social and communication skills of autistic students. People with autism spectrum disorder can have different levels of support needs in daily life; understanding these levels is important for providing a correct educational plan for autistic students. We develop a system that predicts the level of support needed for a student and then recommends a personalized educational plan. The system uses information such as the student’s verbal communication skills, social interaction abilities, sensory sensitivity, and attention span. After predicting the level, the system applies a predefined set of rules to suggest specific teaching methods. These are utilized in matching the abilities and needs of the autistic students to study effectively. We developed an interactive web application that enables parents or teachers to input a student’s details and obtain both the support level and personalized learning suggestions. The outcome indicates that the method combines early and correct autism spectrum disorder–level prediction with practical teaching methods, making education more personalized and effective for autistic students. En ligne : https://dx.doi.org/10.1177/13623613251375199 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=578 [article] Autism spectrum disorder–level prediction and personalized education planning using TabNet [texte imprimé] / A. NITHYA, Auteur ; V. SIVASANKARAN, Auteur . - p.176-186. Langues : Anglais (eng) in Autism > 30-1 (January 2026) . - p.176-186 Mots-clés : autism  educational plan  recommendation  rule-based mapping  TabNet  teaching methods Résumé : Students with autism spectrum disorder have an impact on their social, communication, and behavioral skills. Existing studies primarily focus on autism detection and diagnosis but lack effective approaches for predicting autism spectrum disorder levels and linking them to personalized educational strategies. This research aims to develop an autism-level categorization and a tailored education plan recommendation system for autistic students. The proposed methodology involves collecting a data set with attributes such as verbal ability, social interaction, sensory sensitivity, and attention span for students with autism spectrum disorder. These features are preprocessed and used to train a TabNet model to categorize the autism level. The system recommends a personalized education plan through a web application, based on prediction. This study uniquely integrates autism spectrum disorder-level prediction with education planning, achieving an accuracy of 99.37% and precision of 98.91% using the Autism Spectrum Classification for Education Planning data set. This shows the proposed model effectively categorizes autism levels and provides an education plan recommendation system for autistic students.Lay abstract Autism spectrum disorder (ASD) is a critical neurodevelopmental disorder affecting the social and communication skills of autistic students. People with autism spectrum disorder can have different levels of support needs in daily life; understanding these levels is important for providing a correct educational plan for autistic students. We develop a system that predicts the level of support needed for a student and then recommends a personalized educational plan. The system uses information such as the student’s verbal communication skills, social interaction abilities, sensory sensitivity, and attention span. After predicting the level, the system applies a predefined set of rules to suggest specific teaching methods. These are utilized in matching the abilities and needs of the autistic students to study effectively. We developed an interactive web application that enables parents or teachers to input a student’s details and obtain both the support level and personalized learning suggestions. The outcome indicates that the method combines early and correct autism spectrum disorder–level prediction with practical teaching methods, making education more personalized and effective for autistic students. En ligne : https://dx.doi.org/10.1177/13623613251375199 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=578

---

1 (1 - 1 / 1) Par page : 25 50 100 200