Analysis, Modeling and Design of Personalized Digital Learning Environment
This work addresses the problem of personalized learning in digital environments for learners and educators, offering a transformative approach with privacy safeguards, though it appears incremental as it builds on existing federated learning techniques.
The research tackled the challenge of creating personalized digital learning environments by developing a Private Learning Intelligence (PLI) framework that uses federated machine learning to build and refine personalized models for individual learners, ensuring privacy protection.
This research analyzes, models and develops a novel Digital Learning Environment (DLE) fortified by the innovative Private Learning Intelligence (PLI) framework. The proposed PLI framework leverages federated machine learning (FL) techniques to autonomously construct and continuously refine personalized learning models for individual learners, ensuring robust privacy protection. Our approach is pivotal in advancing DLE capabilities, empowering learners to actively participate in personalized real-time learning experiences. The integration of PLI within a DLE also streamlines instructional design and development demands for personalized teaching/learning. We seek ways to establish a foundation for the seamless integration of FL into learning systems, offering a transformative approach to personalized learning in digital environments. Our implementation details and code are made public.