Analysis, Modeling and Design of Personalized Digital Learning Environment

TL;DR

This paper introduces a novel Digital Learning Environment enhanced by a Private Learning Intelligence framework that uses federated machine learning to create personalized, privacy-preserving learning models, improving real-time educational experiences.

Contribution

The paper presents a new PLI framework integrating federated learning into DLEs, enabling personalized models with robust privacy protections, which is a significant advancement over existing systems.

Findings

Successful implementation of federated learning for personalized education

Enhanced privacy protection in digital learning environments

Streamlined instructional design through PLI integration

Abstract

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.