DOI: 10.5281/zenodo.17368880
title: Dynamic Capability Graph (DCGM)

description: DCGM-LL: Dynamic Capability Graph Model for Lifelong Learning Path Optimization

Overview

DCGM-LL (Dynamic Capability Graph Model) is a next-generation framework designed to optimize lifelong learning paths by integrating digital twin environments with blockchain technology.This framework dynamically models learners’ skill trajectories, securely records learning outcomes, and adaptively personalizes educational pathways.

The core of the system is the Dynamic Capability Graph Model (DCGM), which uses graph-based learning representations, real-time digital twin simulations, and blockchain-based credential verification.An Adaptive Integration Strategy (AIS) further enhances personalization and scalability.

This framework provides a transparent, secure, and data-driven solution for continuous learning in a rapidly evolving digital education ecosystem.



✨ Features

Dynamic Capability Graph



Models learning modules and their relationships using graph structures.

Supports adaptive path optimization through shortest-path algorithms.

Dynamically updates graph weights based on learner progression and context.


Digital Twin Integration



Creates real-time virtual representations of learners.

Continuously updates skill vectors and learning status.

Simulates multiple educational trajectories for personalized recommendations.


Blockchain-Enabled Trust



Ensures security, traceability, and immutability of learning records.

Verifies achievements through blockchain transactions and smart contracts.

Supports decentralized credential management and interoperability.


Adaptive Integration Strategy (AIS)



Seamlessly integrates digital twin and blockchain layers.

Dynamically adjusts learning resources based on performance metrics.

Uses optimization functions to minimize cost while maximizing capability gain.




📊 Datasets




Dataset
Description
Use




Digital Twin Learning Path Dataset
Real-time simulation of skill progression
Graph optimization and personalization


Blockchain Capability Enhancement Dataset
Blockchain transaction and credential data
Secure validation of learning records


Lifelong Learning Optimization Dataset
Continuous learning trajectories
Model training and adaptive pathfinding


Dynamic Graph Integration Dataset
Temporal graph structure data
Graph evolution and shortest-path modeling




(See methodology section for detailed dataset description and usage.)



⚙️ Installation



Clone the repository
git clone https://zenodo.org/records/17368881







🚀 Usage



Optimized learning path

Predicted skill acquisition timeline

Blockchain-verified credential trail




🧪 Applications



Personalized lifelong learning recommendation systems

Educational blockchain credential verification

Skill gap forecasting and adaptive resource allocation

Decentralized education platforms and learner autonomy




🧩 Model Components



DCGM (Dynamic Capability Graph Model) — graph-based learning optimization (Fig. 1, p.7)

Digital Twin Layer — real-time simulation of learning progression (Fig. 2, p.7)

Blockchain Layer — secure credential recording and verification

AIS (Adaptive Integration Strategy) — dynamic fusion of learner state and blockchain state (Fig. 3–4, p.9).




📈 Performance




Dataset
Accuracy
Recall
F1 Score
AUC




Digital Twin Learning Path
89.78
89.23
88.67
89.12


Blockchain Capability Enhancement
92.34
91.89
91.23
91.56


Lifelong Learning Optimization
89.45
88.78
89.01
88.67


Dynamic Graph Integration
91.12
90.45
90.67
90.23




Our method outperforms existing baselines such as ResNet, ViT, and BLIP.



🧭 Future Work



Enhance real-time graph updating with lightweight digital twin architectures.

Develop scalable blockchain solutions for large-scale education systems.

Extend support to cross-institutional credential sharing.

Integrate AI agents for autonomous learning path co-pilots.




📜 License

This project is licensed under the MIT License.



🙏 Acknowledgments

This work was conducted at Jinhua University of Vocational Technology.Authors: Shihua Zheng (corresponding author), Zhaohuan Wu.This research integrates digital twin, blockchain, and graph learning to support lifelong learning optimization.