CounterfactualOptimalTopology

CounterfactualOptimalTopology A knowledge graph enhanced framework for learner profile construction and personalized resource recommendation in English learning support systems. Overview CounterfactualOptimalTopology is a research-oriented framework designed to improve personalized English learning through learner profile modeling, counterfactual reasoning, optimal transport alignment, and topology-aware learning path optimization. The project aims to address a key challenge in adaptive learning systems: how to recommend learning resources that are not only relevant to a learner's current profile, but also pedagogically meaningful, causally beneficial, and consistent with the structure of domain knowledge. Key Features - Learner Profile Construction Models learner attributes, knowledge states, and learning trajectories to support adaptive personalization. - Knowledge Graph Enhanced Recommendation Uses structured semantic relationships among concepts, resources, and learner attributes to improve recommendation quality. - Counterfactual State Projector Simulates alternative learner states under hypothetical interventions to estimate the potential impact of different learning resources. - Optimal Transport Adapter Aligns learner needs and resource attributes with the knowledge graph distribution using optimal transport theory. - Topology Aware Trajectory Resolver Refines learning paths by preserving graph-based dependencies and pedagogical consistency. - Constraint-Driven Optimization Incorporates pedagogical rules, learner preferences, and graph constraints to ensure robust and meaningful recommendations. Framework Architecture The framework consists of three main components: 1. Counterfactual State Generation and Adaptation - Encodes the current learner profile. - Generates counterfactual learner states. - Aligns generated states with the target knowledge graph. 2. Topology-Aware Trajectory Optimization - Models learner progression as a sequence of states. - Ensures learning paths follow prerequisite and semantic relationships in the knowledge graph. 3. Joint Optimization Framework - Combines reconstruction loss, optimal transport cost, and topology consistency loss. - Produces personalized resource recommendations and updated learner profiles. Methodology Given a learner profile and a set of learning resources, the framework selects resources that maximize alignment between the learner's knowledge state, resource features, and learning trajectory. Experimental HighlightsThe proposed framework demonstrates improvements in: Recommendation accuracy Learner profile refinement Resource ranking quality Learning path consistency Personalized English learning outcomes Experimental results show that CounterfactualOptimalTopology outperforms several baseline methods across learner profile construction, knowledge graph English learning, personalized resource recommendation, and learning support interaction tasks. Repository Structure├── data/ # Dataset files or preprocessing scripts├── models/ # Model components│ ├── counterfactual.py # Counterfactual State Projector│ ├── transport.py # Optimal Transport Adapter│ └── topology.py # Topology Aware Trajectory Resolver├── configs/ # Training and evaluation configurations├── scripts/ # Training, evaluation, and utility scripts├── experiments/ # Experiment logs and results├── README.md # Project documentation└── requirements.txt # Python dependenciesInstallationClone the repository: git clone https://github.com/your-username/CounterfactualOptimalTopology.gitcd CounterfactualOptimalTopologyCreate a virtual environment: python -m venv venvsource venv/bin/activateInstall dependencies: pip install -r requirements.txtUsageTrain the modelpython scripts/train.py --config configs/default.yamlEvaluate the modelpython scripts/evaluate.py --checkpoint checkpoints/best_model.ptGenerate recommendationspython scripts/recommend.py --learner_id sample_learner_001Examplefrom models import CounterfactualOptimalTopology model = CounterfactualOptimalTopology(config) recommendations = model.recommend( learner_profile=learner_profile, knowledge_graph=knowledge_graph, candidate_resources=resources) print(recommendations)RequirementsRecommended environment: Python >= 3.9PyTorchNumPyPandasscikit-learnNetworkXPyYAMLtqdmOptional dependencies: torch-geometricpotmatplotlibDatasetsThis project is designed for experiments involving: Learner profile construction data Knowledge graph based English learning data Personalized resource recommendation data English learning support interaction data Please place datasets under the data/ directory and update the configuration files accordingly. ResultsThe framework can be evaluated using metrics such as: Accuracy Precision Recall F1-score AUC NDCG@10 Mastery gain Retention score Prerequisite violation rate Path efficiency CitationIf you use this project in your research, please cite the related paper: @article{chen2025counterfactualoptimaltopology, title={Learner Profile Construction and Personalized Resource Recommendation for Knowledge Graph Enhanced English Learning Support}, author={Chen, Bingling}, year={2025}}