In the realm of educational technology, attention training plays a critical role in tailoring learning experiences to individual needs, especially for learners with autism spectrum disorder (ASD). This study presents an advanced framework that provides insights into the efficacy of reinforcement strategies by predicting their impact on attention enhancement in educational virtual reality (VR) settings, utilizing physiological biomarkers such as eye-tracking (ET), heart rate (HR), and electrodermal activity (EDA). A comprehensive comparative analysis was undertaken to evaluate the performance metrics of various machine learning (ML) and deep learning (DL) algorithms. The results showcased the robustness of gradient boosting (GB) and random forest (RF) in predicting the impact of reinforcement training in attention increase with high F1-score and ROC\\_AUC values. GB achieved remarkable performance on all features dataset with 77.7\\% F1-score and 77.08\\% ROC\\_AUC, while RF excelled on selected features dataset with 80\\% F1-score and 81.94\\% ROC\\_AUC. The study also explores pattern recognition between autistic and non-autistic individuals, providing insights into the distinctive attentional profiles. An LSTM time-series model was also developed for real-time prediction, offering a pathway for personalized and adaptive learning experiences. The integration of artificial intelligence (AI) models and physiological data holds significant promise for enhancing attention training, with implications extending to personalized education for ASD. The study sets the stage for future enhancements in LSTM prediction accuracy and the development of real-time, tailored educational interventions.
","abstract_html":"<p>In the realm of educational technology, attention training plays a critical role in tailoring learning experiences to individual needs, especially for learners with autism spectrum disorder (ASD). This study presents an advanced framework that provides insights into the efficacy of reinforcement strategies by predicting their impact on attention enhancement in educational virtual reality (VR) settings, utilizing physiological biomarkers such as eye-tracking (ET), heart rate (HR), and electrodermal activity (EDA). A comprehensive comparative analysis was undertaken to evaluate the performance metrics of various machine learning (ML) and deep learning (DL) algorithms. The results showcased the robustness of gradient boosting (GB) and random forest (RF) in predicting the impact of reinforcement training in attention increase with high F1-score and ROC\\_AUC values. GB achieved remarkable performance on all features dataset with 77.7\\% F1-score and 77.08\\% ROC\\_AUC, while RF excelled on selected features dataset with 80\\% F1-score and 81.94\\% ROC\\_AUC. The study also explores pattern recognition between autistic and non-autistic individuals, providing insights into the distinctive attentional profiles. An LSTM time-series model was also developed for real-time prediction, offering a pathway for personalized and adaptive learning experiences. The integration of artificial intelligence (AI) models and physiological data holds significant promise for enhancing attention training, with implications extending to personalized education for ASD. The study sets the stage for future enhancements in LSTM prediction accuracy and the development of real-time, tailored educational interventions.</p>","abstract_has_math":false,"creators":["sanku, Bhavya sri","He, Selena","Li, Joy"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Selena He","Dr. Joy Li"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12-10T08:00:00Z","date_published":"2023-12-10T08:00:00Z","updated_at":"2026-07-24T02:44:06Z","subjects":["Neuroscience and Psychology","Healthcare Technology","Computer Science and AI","Educational and Developmental Psychology","Assistive Technology","Human-Computer Interaction","Other Analytical, Diagnostic and Therapeutic Techniques and Equipment"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/60","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Selena He","Dr. Joy Li"]},{"key":"dc:creator","label":"Author","values":["sanku, Bhavya sri","He, Selena","Li, Joy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-10T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neuroscience and Psychology","Healthcare Technology","Computer Science and AI","Educational and Developmental Psychology","Assistive Technology","Human-Computer Interaction","Other Analytical, Diagnostic and Therapeutic Techniques and Equipment"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/60"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In the realm of educational technology, attention training plays a critical role in tailoring learning experiences to individual needs, especially for learners with autism spectrum disorder (ASD). This study presents an advanced framework that provides insights into the efficacy of reinforcement strategies by predicting their impact on attention enhancement in educational virtual reality (VR) settings, utilizing physiological biomarkers such as eye-tracking (ET), heart rate (HR), and electrodermal activity (EDA). A comprehensive comparative analysis was undertaken to evaluate the performance metrics of various machine learning (ML) and deep learning (DL) algorithms. The results showcased the robustness of gradient boosting (GB) and random forest (RF) in predicting the impact of reinforcement training in attention increase with high F1-score and ROC\\_AUC values. GB achieved remarkable performance on all features dataset with 77.7\\% F1-score and 77.08\\% ROC\\_AUC, while RF excelled on selected features dataset with 80\\% F1-score and 81.94\\% ROC\\_AUC. The study also explores pattern recognition between autistic and non-autistic individuals, providing insights into the distinctive attentional profiles. An LSTM time-series model was also developed for real-time prediction, offering a pathway for personalized and adaptive learning experiences. The integration of artificial intelligence (AI) models and physiological data holds significant promise for enhancing attention training, with implications extending to personalized education for ASD. The study sets the stage for future enhancements in LSTM prediction accuracy and the development of real-time, tailored educational interventions.
"]},{"key":"dc:title","label":"Title","values":["Revolutionizing Attention Training in Autism Spectrum Disorder: Pioneering Virtual Reality and Artificial Intelligence"]}]}],"canonical_facts":{"dc:contributor":["Dr. Selena He","Dr. Joy Li"],"dc:creator":["sanku, Bhavya sri","He, Selena","Li, Joy"],"dc:date.available":["2025-01-10T08:00:00Z"],"dc:description.abstract":["In the realm of educational technology, attention training plays a critical role in tailoring learning experiences to individual needs, especially for learners with autism spectrum disorder (ASD). This study presents an advanced framework that provides insights into the efficacy of reinforcement strategies by predicting their impact on attention enhancement in educational virtual reality (VR) settings, utilizing physiological biomarkers such as eye-tracking (ET), heart rate (HR), and electrodermal activity (EDA). A comprehensive comparative analysis was undertaken to evaluate the performance metrics of various machine learning (ML) and deep learning (DL) algorithms. The results showcased the robustness of gradient boosting (GB) and random forest (RF) in predicting the impact of reinforcement training in attention increase with high F1-score and ROC\\_AUC values. GB achieved remarkable performance on all features dataset with 77.7\\% F1-score and 77.08\\% ROC\\_AUC, while RF excelled on selected features dataset with 80\\% F1-score and 81.94\\% ROC\\_AUC. The study also explores pattern recognition between autistic and non-autistic individuals, providing insights into the distinctive attentional profiles. An LSTM time-series model was also developed for real-time prediction, offering a pathway for personalized and adaptive learning experiences. The integration of artificial intelligence (AI) models and physiological data holds significant promise for enhancing attention training, with implications extending to personalized education for ASD. The study sets the stage for future enhancements in LSTM prediction accuracy and the development of real-time, tailored educational interventions.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/cs_etd/60"],"dc:subject":["Neuroscience and Psychology","Healthcare Technology","Computer Science and AI","Educational and Developmental Psychology","Assistive Technology","Human-Computer Interaction","Other Analytical, Diagnostic and Therapeutic Techniques and Equipment"],"dc:title":["Revolutionizing Attention Training in Autism Spectrum Disorder: Pioneering Virtual Reality and Artificial Intelligence"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MSCS)"]},"updated_at":"2026-07-24T02:44:06Z"}