Adaptive Learning Platform for Dyslexic Students

Adaptive learning systems are a significant area of research in personalized education, especially for students with dyslexia, as structured and responsive instructional support can greatly enhance learning outcomes. Many conventional rule-based methodologies are not readily adaptable for personalized instruction or real-time modification. To overcome this limitation, this paper introduces a lightweight adaptive learning system that utilizes a machine learning model to produce instructional recommendations. The system employs a Decision Tree Classifier trained on structured quiz-response features to assess a learner’s proficiency and recommend the most appropriate learning stages. The platform is not meant to be a diagnostic tool; instead, it is meant to be a post-identification instructional support system. It has three main parts: an interactive quiz interface, a classification module that groups learners into Beginner, Intermediate, or Advanced levels, and a Flask-based backend that makes predictions and sends them out. The model is still computationally efficient, easy to understand, and good for real-time adaptive learning environments because it only uses quiz performance indicators. When tested on a controlled synthetic dataset, the results were very good, with an overall accuracy of 98.25%. Advanced learners had a class-specific \(F_1\) score of 99.15%, beginner learners had a score of 95.87%, and intermediate learners had a score of 96.27%. The macro-average precision, recall, and \(F_1\) -score were 96.93%, 97.32%, and 97.10%, respectively. The weighted averages were all close to 97.00%. These results show that the suggested method is a good and useful way to recommend quiz-based adaptive learning in technology-supported special education settings.