Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing

• URL: http://arxiv.org/abs/2605.05958v2
• Date: Fri, 08 May 2026 06:07:38 GMT
• Title: Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing
• Abstract summary: We introduce a doubly robust (DR) formulation for Knowledge Tracing (KT)<n>DR integrates a propensity model with an error imputation model, theoretically guaranteeing unbiasedness if either model is accurate.<n>Building on these theoretical insights, we propose the Temporal Smoothness Doubly Robust (TSDR) framework.
• Score: 29.91193246346701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Tracing (KT) is fundamental to intelligent education systems, yet relies on educational logs that are selectively observed. The non-random nature of exercise recommendations and student choices inevitably induces severe selection bias. Most existing KT methods neglect this issue, training on observed logs using standard empirical risk, which yields biased mastery estimates and accumulates errors in subsequent recommendations. To address this, we introduce a doubly robust (DR) formulation for KT that integrates a propensity model with an error imputation model, theoretically guaranteeing unbiasedness if either model is accurate. Beyond unbiasedness, in the sequential setting of KT, we identify that the estimator's performance is compromised by variance-dependent stochastic deviations that accumulate over time, thereby causing training instability and limiting performance. To mitigate this, we derive a generalization bound that explicitly characterizes the impact of estimator variance and identifies temporal smoothness as a key factor in controlling it. Building on these theoretical insights, we propose the Temporal Smoothness Doubly Robust (TSDR) framework. TSDR jointly optimizes the KT predictor and the imputation model with a smoothness regularizer, effectively reducing variance while preserving the unbiasedness guarantee of DR. Experiments on multiple real-world benchmarks demonstrate that TSDR consistently enhances various state-of-the-art KT backbones, underscoring the vital role of principled bias correction in KT.

Related papers

• Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
  Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-05T14:48:52Z)
• Observationally Informed Adaptive Causal Experimental Design [55.998153710215654]
  We propose Active Residual Learning, a new paradigm that leverages the observational model as a foundational prior.<n>This approach shifts the experimental focus from learning target causal quantities from scratch to efficiently estimating the residuals required to correct observational bias.<n> Experiments on synthetic and semi-synthetic benchmarks demonstrate that R-Design significantly outperforms baselines.
  arXiv  Detail & Related papers  (2026-03-04T06:52:37Z)
• The Procrustean Bed of Time Series: The Optimization Bias of Point-wise Loss [53.542743390809356]
  This paper aims to provide a first-principles analysis of the Expectation of Optimization Bias (EOB)<n>Our analysis reveals a fundamental paradigm paradox: the more deterministic and structured the time series, the more severe the bias by point-wise loss function.<n>We present a concrete solution that simultaneously achieves both principles via DFT or DWT.
  arXiv  Detail & Related papers  (2025-12-21T06:08:22Z)
• Causal Inference as Distribution Adaptation: Optimizing ATE Risk under Propensity Uncertainty [0.0]
  We reframing ATE estimation as a textitdomain adaptation problem under distribution shift.<n>We propose the textbfJoint Robust Estimator (JRE) to train outcome models jointly.
  arXiv  Detail & Related papers  (2025-12-19T21:40:46Z)
• TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation [11.334867025651233]
  We propose TULiP, a theoretically-driven uncertainty estimator for OOD detection.<n>Our approach considers a hypothetical perturbation applied to the network before convergence.<n>Our method exhibits state-of-the-art performance, particularly for near-distribution samples.
  arXiv  Detail & Related papers  (2025-05-22T17:16:41Z)
• Selective Learning: Towards Robust Calibration with Dynamic Regularization [79.92633587914659]
  Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. We introduce Dynamic Regularization (DReg) which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off.
  arXiv  Detail & Related papers  (2024-02-13T11:25:20Z)
• The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data [8.916614661563893]
  We introduce a missing-at-random (decaying MAR) framework and associated approaches for doubly robust causal inference.<n>This simultaneously addresses selection bias in the labeling mechanism and the extreme imbalance between labeled and unlabeled groups.<n>To ensure robust causal conclusions, we propose a bias-reduced SS estimator for the average treatment effect.
  arXiv  Detail & Related papers  (2023-05-22T07:37:12Z)
• StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random [16.700598755439685]
  We show that the doubly robust (DR) methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities. We propose a doubly robust (StableDR) learning approach with a weaker reliance on extrapolation. In addition, we propose a novel learning approach for StableDR that updates the imputation, propensity, and prediction models cyclically.
  arXiv  Detail & Related papers  (2022-05-10T07:04:53Z)
• Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
  The trade-off between robustness and accuracy has been widely studied in the adversarial literature. We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
  arXiv  Detail & Related papers  (2022-02-21T10:36:09Z)
• Learning to Estimate Without Bias [57.82628598276623]
  Gauss theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance.
  arXiv  Detail & Related papers  (2021-10-24T10:23:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.