Data Driven Personalized Learning Pathways for Students with Special Educational Needs

Abstract

The growing variety of learning requirements of the student body and especially the students with Special Educational Needs (SEN) demand the creation of adaptive and inclusive learning systems. This paper presents a data-driven individual learning model, which utilizes the artificial intelligence, learning analytics, and adaptive technologies to create customized learning trajectories. The model combines the multi-dimensional data about the learner, such as academic achievement, behavioral patterns, and engagement indicators, to create individualized learning experiences based on the profile of every learner. A scalable and modular system architecture is created, which includes content delivery mechanisms, multimodal, data processing, intelligent analytics, and personalization engines. Assistive technologies are also incorporated in the system to make it more accessible and inclusive. The implementation plan is based on real time monitoring, feedback and smooth integration with existing educational platforms. The findings of the experiment indicate tremendous enhancements in the academic achievement, participation rates and learning effectiveness of SEN students. The suggested system is superior to the traditional teaching methods because it offers adaptive content, timely intervention and data-driven insights to educators. Also, the framework facilitates fair learning processes through learning gaps between individuals and inclusive learning. The paper points out the promise of data-based solutions in revolutionizing special education and the significance of ethics and privacy of data and scalability to practical implementation. The suggested framework assists in the development of intelligent education systems and preconditions the future studies in the individualized and inclusive learning process.