Analysis, Modeling and Design of Personalized Digital Learning Environment

Sanjaya Khanal, Shiva Raj Pokhrel
5/17/2024
cs.HCcs.AIcs.SE

No official code found. Best implementation: pli-research-d/Secure-ML-with-BERT (0 stars, last commit 2024-05-18).

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.

View on arXivView PDF

Code Implementations(1)

pli-research-d/Secure-ML-with-BERTCommunity100%

This project demonstrates a secure machine learning environment using BERT to classify learning measures while maintaining data privacy through encryption and anonymization.

01Jupyter NotebookMay 8, 20242 years ago

Similar Papers

PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

87% match2 repos2.2k2023

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

84% match1 repo1862023

Towards Personalized Federated Learning via Comprehensive Knowledge Distillation

84% match2024

Personalized Federated Learning with Contextual Modulation and Meta-Learning

83% match1 repo12023

Federated Learning in Practice: Reflections and Projections

83% match2024

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

83% match2022

Cite this paper

@article{khanal2024analysis,
  title  = {Analysis, Modeling and Design of Personalized Digital Learning Environment},
  author = {Sanjaya Khanal and Shiva Raj Pokhrel},
  year   = {2024},
  eprint = {2405.10476},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.10476v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.10476.svg)](https://codeofpaper.com/paper/2405.10476)

Discussion