Dynamic Capability Graph (DCGM)

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.