Explainable Hybrid Deep Learning Approach to Dyslexia Detection Using Multimodal Data in an Adaptive Education Platform

An explainable hybrid deep learning approach that detects dyslexia from multimodal data in an adaptive education platform.

An explainable hybrid deep learning approach that detects dyslexia from multimodal data in an adaptive education platform. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are explanations of each result (feature importance or visual evidence), fusion of more than one type of input, dataset preparation and augmentation, model design and training, and evaluation with standard metrics, plus error analysis. The reference build uses SHAP, Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.