LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation

TL;DR

This work presents LiveGraph, a novel active-structure neural re-ranking framework designed to overcome limitations regarding the long-tailed distribution of student engagement and the failure to adapt to idiosyncratic learning trajectories.

Abstract

The continuous expansion of digital learning environments has catalyzed the demand for intelligent systems capable of providing personalized educational content. While current exercise recommendation frameworks have made significant strides, they frequently encounter obstacles regarding the long-tailed distribution of student engagement and the failure to adapt to idiosyncratic learning trajectories. We present LiveGraph, a novel active-structure neural re-ranking framework designed to overcome these limitations. Our approach utilizes a graph-based representation enhancement strategy to bridge the information gap between active and inactive students while integrating a dynamic re-ranking mechanism to foster content diversity. By prioritizing the structural relationships within learning histories, the proposed model effectively balances recommendation precision with pedagogical variety. Comprehensive experimental evaluations conducted on multiple real-world datasets demonstrate that LiveGraph surpasses contemporary baselines in both predictive accuracy and the breadth of exercise diversity.

Figures (5)

• Figure 1: Overview of the LiveGraph framework for active-structure neural exercise recommendation. The Graph-Aware Student Representation Enhancer (Graph-VAE) transforms interaction history $D_s$ into a stochastic mastery distribution $\boldsymbol{\theta}_s$, regularized by LLM-informed priors. In the Uncertainty-Aware Neural Re-ranker, candidate exercises $\mathcal{C}$ are scored based on a multi-signal fusion of relevance $\phi_{\text{rel}}$, diversity $\phi_{\text{div}}$, and Bernoulli Sub-graph Entropy$U(e)$. This fusion is dynamically orchestrated by a Meta-RL Controller that optimizes the exploration-exploitation trade-off via MAML-based adaptation. The Active Learning Probe identifies concept pairs with maximum mutual information $\hat{I}(s_{ij}; R_{ij})$ to inject contrastive probes, triggering real-time Synchronous Kernel Evolution of the Dynamic Knowledge Kernel$\mathbf{S}^{(t)}$ for continuous structural refinement.
• Figure 2: Convergence behaviour: training steps vs. validation NDCG@5. Curves compare runs with and without the Meta-RL controller. Curves and shaded bands show mean $\pm1$ standard deviation over five random seeds.
• Figure 3: Diversity comparison across methods. Grouped bars show DIV@1, DIV@3, DIV@5 and DIV@10 for representative KT-based baselines, MMER/KCPER/KG4Ex, NR4DER-p (current SOTA), and LiveGraph. Bar heights correspond to the mean across seeds and error bars indicate one standard deviation.
• Figure 4: Knowledge--concept distribution for a sampled student (Assist2009). The horizontal axis lists the 20 recommended exercises (sampled), the vertical axis lists the history frequency (top row) followed by 18 knowledge concepts. Color intensity denotes the presence/strength of a concept in each exercise (binary or continuous).
• Figure 5: Hyperparameter sensitivity of the VAE prior coefficient $\beta$ on validation NDCG@5 (log-scale x-axis). Each marker shows the mean validation NDCG@5 across seeds and vertical bars denote one standard deviation. The plot highlights the stable operating range used in our experiments.