Faculty of Computing

Education systems worldwide struggle to support slow learners, who face difficulties in traditional classrooms due to learning challenges in writing, mathematics, attention, and memory. Slow learners, characterized by an Intelligence Quotient (IQ) between 70 and 85, require additional time and adaptive learning methods to grasp concepts effectively. However, existing educational frameworks lack comprehensive screening and targeted interventions. This research introduces "DiverseMind", an integrated framework designed to identify and assist slow learners among Grade 4 primary school children in Sri Lanka using advanced Machine Learning (ML) algorithms, image processing, and multi-model architecture. The system evaluates academic abilities through four key assessments of writing skills, mathematical proficiency, attention span, and short-term memory. A Convolutional Neural Network (CNN) based model, achieving a training accuracy of 98% combined with a Python-based weighted condition function, classifies writing accuracy, while Decision Tree (DT) classifiers analyze mathematical capabilities with 98% accuracy. Attention span is assessed using facial landmark detection, gaze tracking, and emotion recognition, where the CNN model trained on 28,709 images achieved a training accuracy of 80%. Short-term memory is evaluated through ML driven cognitive tasks, with the DT model achieving 99% accuracy. In addition to comprehensive assessments and interventions, the system provides a dedicated dashboard for the teachers to monitor the student progress. By integrating gamification and AI-driven learning analytics, "DiverseMind"promotes inclusive education and bridges the gap in support for slow learners, ensuring they receive the necessary resources to reach their full potential.

Faculty of Computing

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