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Analysis, Modeling and Design of Personalized Digital Learning Environment

Sanjaya Khanal, Shiva Raj Pokhrel

TL;DR

The paper tackles the challenge of delivering highly personalized learning in digital environments without compromising privacy. It introduces Private Learning Intelligence (PLI), a federated learning-based framework that trains local learner models and shares only non-private updates with a central system. The architecture comprises a core PLI framework, local training, data aggregation, and interoperable expert knowledge networks, with a four-stage deployment and privacy safeguards such as sandboxing and differential privacy. Public code availability is claimed, and the approach is positioned to reduce instructional design workload while enabling real-time, adaptive learning.

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.

Analysis, Modeling and Design of Personalized Digital Learning Environment

TL;DR

The paper tackles the challenge of delivering highly personalized learning in digital environments without compromising privacy. It introduces Private Learning Intelligence (PLI), a federated learning-based framework that trains local learner models and shares only non-private updates with a central system. The architecture comprises a core PLI framework, local training, data aggregation, and interoperable expert knowledge networks, with a four-stage deployment and privacy safeguards such as sandboxing and differential privacy. Public code availability is claimed, and the approach is positioned to reduce instructional design workload while enabling real-time, adaptive learning.

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.
Paper Structure (13 sections, 6 figures, 3 tables)

This paper contains 13 sections, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Proposed Idea: originality and benefits
  3. Literature
  4. Proposed PLI Framework
  5. Methodology
  6. Architecture and Features
  7. Components
  8. Interfaces Design
  9. User Data and Privacy
  10. Learner Personalized Model
  11. Federation Services Hub
  12. Network ML Servers
  13. Conclusion

Figures (6)

  • Figure 1: A high-level illustration of the integration and interplay between components of a DLE with PLI, all focused on providing an individualized and interactive learning experience.
  • Figure 2: Architecture of the Private Learning Intelligence (PLI) for personalized learning in DLE
  • Figure 3: Four-stage PLI implementation process, focusing on federated learning enhancement, privacy assurance, secure local model training, collaborative improvement, and performance optimization through real-time monitoring and testing
  • Figure 4: Process of periodic retraining of the models with the latest data to keep them up-to-date
  • Figure :
  • ...and 1 more figures