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

Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying students' dynamic learning needs in modern education. Existing approaches primarily follow two paradigms: Item-Level Exercise Recommendation (ILER) ranks exercises for immediate recommendation, whereas Path-Level Exercise Recommendation (PLER) plans ordered exercise sequences. 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) to compare estimated knowledge gains under different instructional objectives and shared interaction budgets. We benchmark 18 representative methods spanning four recommendation categories on nine public datasets. Through analyses of effectiveness, generalizability, robustness, and efficiency, we find that the leading methods change with the interaction budget: ILER methods achieve the highest average scores at one step, whereas PLER methods lead at ten steps. Performance under data sparsity, cold-start, and label noise further varies across methods and datasets, providing evidence for selecting recommendation methods according to the intended learning setting.