Akademik Veri Yönetim Sistemi
10th International Conference on Computer Science and Engineering, UBMK 2025, İstanbul, Türkiye, 17 - 21 Eylül 2025, ss.102-107, (Tam Metin Bildiri)
This study assesses the effectiveness and suitability of online educational contents for students with Attention Deficit Hyperactivity Disorder (ADHD) by employing a proposed Convolutional Neural Network (CNN) model alongside established models including MobileNetV2, ResNet50, VGG16, DenseNet, and EfficientNet. Utilizing a newly developed ADHD Online Instructor (AOI) dataset - comprising videos and images categorized based on whether the instructor's hand is in motion during online lessons - the models classify the appropriateness of instructional videos for ADHD learners. Results indicate that the proposed CNN model outperforms the others, achieving an accuracy of 0.9984 with fewer training epochs. These findings provide valuable insights for designing more effective and inclusive online instructional materials tailored to the needs of students with ADHD.