Related Experiment Video
Updated: Jun 29, 2026
09:00
Advancing Dyslexia Assessment in Children Through Computerized Testing
Published on: August 16, 2024
Adaptive Learning Platform for Dyslexic Students.
Mandhadi Thanshita Bharathi1, Chintalapudi Likhitha Bhavana1, Kammari Srinivas1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Tamilnadu, 600127, India.
Scientific Reports
|June 27, 2026
Summary
This study introduces a lightweight adaptive learning system using a machine learning model to recommend learning stages for students with dyslexia. The system achieved 98.25% accuracy, offering efficient, personalized educational support.
Area of Science:
- Educational Technology
- Machine Learning in Education
- Special Education
Background:
- Adaptive learning systems offer personalized education, crucial for students with dyslexia.
- Conventional rule-based systems lack adaptability for real-time personalized instruction.
- Existing methods struggle to provide responsive support for diverse learning needs.
Purpose of the Study:
- To introduce a lightweight adaptive learning system for dyslexia support.
- To utilize a machine learning model for personalized instructional recommendations.
- To enhance learning outcomes through responsive, technology-supported interventions.
Main Methods:
- Developed a Decision Tree Classifier trained on quiz-response data.
- Implemented a system with an interactive quiz, classification module, and Flask backend.
- Focused on quiz performance indicators for computational efficiency and real-time application.
Main Results:
- Achieved an overall accuracy of 98.25% on a synthetic dataset.
- Class-specific F1-scores: Advanced (99.15%), Beginner (95.87%), Intermediate (96.27%).
- Macro-average precision, recall, and F1-score were 96.93%, 97.32%, and 97.10% respectively.
Conclusions:
- The proposed system effectively recommends quiz-based adaptive learning stages.
- The model is computationally efficient and suitable for real-time adaptive learning.
- This approach provides a valuable tool for technology-supported special education settings.
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