mlsanityData to 14 Oct 2026

UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation

arXiv 2605.16750 ↗First posted 16 May 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying students' dynamic learning needs in modern education. The field is currently driven by two dominant paradigms: Item-Level Exercise Recommendation (ILER) optimizes for immediate single-step state transitions, while Path-Level Exercise Recommendation (PLER) constructs coherent learning paths to maximize cumulative gains. Despite sharing the same ultimate objective, disparate evaluation setups have kept these two lines of research isolated, hindering unified benchmarking and fair comparison. To fill the gap, in this paper, we present a Unified Benchmark for Exercise Recommendation (UniER), a comprehensive evaluation framework that unifies ILER and PLER. Specifically, we introduce Weighted Cognitive Gain (WCG) as a unified metric to measure cross-paradigm algorithmic performance. Our benchmark encompasses 9 datasets spanning four generation methods, facilitating the comparison of 18 representative ILER/PLER methods. Through multi-dimensional analyses covering effectiveness, generalizability, robustness, and efficiency, our results reveal the systematic dominance of PLER and expose the pedagogical failure of ILER's fragmented recommendations under extreme sparsity and noise. Furthermore, we provide an open-source codebase of UniER to foster reproducible research and outline potential directions for future investigations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 38 references that are in the map. First posted on arXiv 16 May 2026.

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References · 17

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5 more
  1. 2025GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendationcited by 6
  2. 2024User-item fairness tradeoffs in recommendationscited by 5
  3. 2025NR4DER: Neural Re-ranking for Diversified Exercise Recommendationcited by 5
  4. 2021RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactionscited by 3
  5. 2024Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendationcited by 2

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