DOI: 10.5281/zenodo.17668428
title: Modeling students' Chinese language learning pathways by introducing a behavior-driven semantic graph construction mechanism

description: BDSGM-Learning Pathways

Behavior-Driven Semantic Graph Modeling for Chinese Language Learning Pathways

Overview

This repository provides a lightweight research implementation inspired by the Behavior-Driven Semantic Graph Model (BDSGM) and its associated strategy for modeling students’ Chinese language learning pathways.The central idea is to integrate behavioral signals, such as time on task, revisit frequency, and assessment performance, with semantic representations of linguistic concepts, constructing a dynamic semantic graph that evolves as learners interact with content.

The framework combines behavior-driven graph construction, multimodal encoding, sequence modeling, and graph-based propagation to offer interpretable pathway analytics and personalized learning recommendations.



Method

Behavior-Driven Semantic Graph Construction

A directed concept graph G=(V,E)G = (V, E)G=(V,E) is built where each node is a learning concept, and edges encode transitions inferred from behavior logs. This combines behavioral importance with conceptual movement patterns.



Multimodal Encoder and Graphical Propagation

A multimodal encoder maps concept and behavior features into embeddings.A graph propagation layer performs message passing with behavior-aware attention coefficients αij\alpha_{ij}αij, reflecting concept influence adjusted by actual learner behavior.

Figures in the referenced paper illustrate the modules:



Fig. 1: Overall BDSGM structure with encoder and propagation (p. 6)

Fig. 2: Multimodal attention fusion blocks (p. 8)

Fig. 3: Strategy with progressive graph blocks and mixture-of-experts (p. 9)

Fig. 4: Spatial–channel attention details (p. 10)




Sequence Modeling and Prediction

Temporal dynamics are modeled with a BiLSTM applied to the concept transition sequence.The temporal representation is fused with graph-level embeddings to predict learning pathway stage, mastery likelihood, or related educational outcomes.An optional adaptive threshold converts probabilities into alert signals for early intervention.



Architecture at a Glance



Behavior-driven edge weights derived from time, frequency, and score signals

Semantic concept embeddings for similarity modeling

BiLSTM encoder for longitudinal learning sequences

Behavior-aware graph propagation for structural refinement

Dual-head outputs for pathway classification and auxiliary tasks




Datasets and Format

The referenced study evaluates the model on four learning datasets: SLAP, SGLE, BALL, and PMLP, demonstrating consistent improvements from behavior-driven propagation.


Training Configuration




Component
Default




Optimizer
AdamW


LR
1e-3


Weight Decay
1e-4


Batch Size
64


Epochs
50


Loss
BCE or CE


Metrics
Accuracy, Macro F1




Efficiency and ROC analyses in the paper (Fig. 6–8, Table 7) show that behavior-aware propagation yields strong accuracy with moderate computational cost.



Repository Structure



train.py — training entry point

model.py — encoder, propagation layers, and prediction heads

dataset.py — JSONL loader for concept and behavior sequences

utils.py — metrics, checkpointing, random seeds, device helpers

inference.py — batch inference and alert generation




Results Summary

Across conceptual learning datasets, the behavior-driven semantic graph consistently improves F1 scores by several points compared to sequence-only or static-graph baselines.Performance also aligns well with semantic consistency and adaptivity metrics.See Tables 1–4 and Fig. 6 in the paper for detailed comparisons and ROC curves.



License

Recommended: MIT or Apache-2.0.Include a LICENSE file in the project root.



Acknowledgments

This implementation is inspired by the behavior-driven semantic graph modeling and associated strategy described in the referenced paper.Figures and formulas correspond to pages 6–10 and 13–21 of the publication.