Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Training Sequences on Category Learning

• URL: http://arxiv.org/abs/2407.15020v1
• Date: Sat, 22 Jun 2024 20:22:19 GMT
• Title: Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Training Sequences on Category Learning
• Abstract summary: This study introduces a novel integration of attentional factors and spacing into the logistic knowledge tracing (LKT) models. Our findings reveal that incorporating both attentional factors and spacing features in the Additive Factors Model (AFM) significantly enhances its capacity to capture the effects of interleaving and blocking.
• Score: 37.80414603767789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In category learning, a growing body of literature has increasingly focused on exploring the impacts of interleaving in contrast to blocking. The sequential attention hypothesis posits that interleaving draws attention to the differences between categories while blocking directs attention toward similarities within categories. Although a recent study underscores the joint influence of memory and attentional factors on sequencing effects, there remains a scarcity of effective computational models integrating both attentional and memory considerations to comprehensively understand the effect of training sequences on students' performance. This study introduces a novel integration of attentional factors and spacing into the logistic knowledge tracing (LKT) models to monitor students' performance across different training sequences (interleaving and blocking). Attentional factors were incorporated by recording the counts of comparisons between adjacent trials, considering whether they belong to the same or different category. Several features were employed to account for temporal spacing. We used cross-validations to test the model fit and predictions on the learning session and posttest. Our findings reveal that incorporating both attentional factors and spacing features in the Additive Factors Model (AFM) significantly enhances its capacity to capture the effects of interleaving and blocking and demonstrates superior predictive accuracy for students' learning outcomes. By bridging the gap between attentional factors and memory processes, our computational approach offers a more comprehensive framework for understanding and predicting category learning outcomes in educational settings.

Related papers

• Test-Time Learning of Causal Structure from Interventional Data [50.06913286558919]
  We propose TICL (Test-time Interventional Causal Learning), a novel method that synergizes Test-Time Training with Joint Causal Inference.<n>Specifically, we design a self-augmentation strategy to generate instance-specific training data at test time, effectively avoiding distribution shifts.<n>By integrating joint causal inference, we developed a PC-inspired two-phase supervised learning scheme, which effectively leverages self-augmented training data while ensuring theoretical identifiability.
  arXiv  Detail & Related papers  (2026-02-22T11:23:05Z)
• Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics [14.688567166793234]
  This study addresses the challenges of dataset bias and explores shortcut learning'' or Clever Hans effect'' in binary classifiers.<n>Our framework incorporates intervention and observational perspectives, employing a linear mixed-effects model for post-hoc analysis.
  arXiv  Detail & Related papers  (2026-01-25T10:47:42Z)
• Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning [4.662814261930481]
  We introduce a scientific methodology for analyzing the learning process through quantitative analysis of saliency.<n>This approach aggregates saliency information at the object and modality level into hierarchical attention profiles.<n>This methodology uncovers algorithm-specific attention biases, reveals unintended reward-driven strategies, and diagnoses overfitting to redundant sensory channels.
  arXiv  Detail & Related papers  (2025-11-25T18:20:42Z)
• Causal Structure and Representation Learning with Biomedical Applications [23.11061319442]
  We outline a statistical and computational framework for causal structure and representation learning motivated by fundamental biomedical questions.<n>We show how to effectively use observational and perturbational data to perform causal discovery on observed causal variables.
  arXiv  Detail & Related papers  (2025-11-06T20:17:58Z)
• Mitigating Spurious Correlations with Causal Logit Perturbation [22.281052412112263]
  This study introduces a novel Causal Logit Perturbation (CLP) framework to train classifiers with generated causal logit perturbations for individual samples.<n>The framework is optimized by an online meta-learning-based learning algorithm and leverages human causal knowledge by augmenting metadata in both counterfactual and factual manners.
  arXiv  Detail & Related papers  (2025-05-21T08:21:02Z)
• Class-Dependent Perturbation Effects in Evaluating Time Series Attributions [5.136283512042341]
  We show previously overlooked class-dependent effects in feature attribution metrics. Our analysis suggests that perturbation-based evaluation may reflect specific model behaviors rather than intrinsic attribution quality. We propose an evaluation framework with a class-aware penalty term to help assess and account for these effects.
  arXiv  Detail & Related papers  (2025-02-24T10:22:03Z)
• DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing [51.665582274736785]
  Knowledge Tracing (KT) predicts future performance by students' historical computation, and understanding students' affective states can enhance the effectiveness of KT. We propose Affect Dynamic Knowledge Tracing (DASKT) to explore the impact of various student affective states on their knowledge states. Our research highlights a promising avenue for future studies, focusing on achieving high interpretability and accuracy.
  arXiv  Detail & Related papers  (2025-01-18T10:02:10Z)
• Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations [76.2226569692207]
  Correlations between input parameters play a crucial role in many scientific classification tasks. We present a new adversarial attack algorithm called Random Distribution Shuffle Attack (RDSA) We demonstrate the RDSA effectiveness on six classification tasks.
  arXiv  Detail & Related papers  (2025-01-09T21:45:09Z)
• How much do we really know about Structure Learning from i.i.d. Data? Interpretable, multi-dimensional Performance Indicator for Causal Discovery [3.8443430569753025]
  causal discovery from observational data imposes strict identifiability assumptions on the formulation of structural equations utilized in the data generating process. Motivated by the lack of unified performance assessment framework, we introduce an interpretable, six-dimensional evaluation metric, i.e., distance to optimal solution (DOS) This is the first research to assess the performance of structure learning algorithms from seven different families on increasing percentage of non-identifiable, nonlinear causal patterns.
  arXiv  Detail & Related papers  (2024-09-28T15:03:49Z)
• On Understanding Attention-Based In-Context Learning for Categorical Data [49.40350941996942]
  We develop a network composed of attention blocks, with each block employing a self-attention layer followed by a cross-attention layer, with associated skip connections.<n>This model can exactly perform multi-step functional GD inference for in-context inference with categorical observations.
  arXiv  Detail & Related papers  (2024-05-27T15:03:21Z)
• In-context Contrastive Learning for Event Causality Identification [26.132189768472067]
  Event Causality Identification aims at determining the existence of a causal relation between two events. Recent prompt learning-based approaches have shown promising improvements on the ECI task. This paper proposes an In-Context Contrastive Learning model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations.
  arXiv  Detail & Related papers  (2024-05-17T03:32:15Z)
• Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning [82.79371269942146]
  We propose to tackle FewShot Class-Incremental Learning (FSCIL) from a new perspective, i.e., relation disentanglement. The challenge of disentangling spurious correlations lies in the poor controllability of FSCIL. We propose a new simple-yeteffective method, called ConTrollable Relation-disentang FewShot Class-Incremental Learning (CTRL-FSCIL)
  arXiv  Detail & Related papers  (2024-03-17T03:16:59Z)
• Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection [0.0]
  Unsupervised out-of-distribution (OOD) Detection aims to separate the samples falling outside the distribution of training data without label information. We propose Cluster-aware Contrastive Learning (CCL) framework for unsupervised OOD detection, which considers both instance-level and semantic-level information.
  arXiv  Detail & Related papers  (2023-02-06T07:21:03Z)
• Efficient Modelling Across Time of Human Actions and Interactions [92.39082696657874]
  We argue that current fixed-sized-temporal kernels in 3 convolutional neural networks (CNNDs) can be improved to better deal with temporal variations in the input. We study how we can better handle between classes of actions, by enhancing their feature differences over different layers of the architecture. The proposed approaches are evaluated on several benchmark action recognition datasets and show competitive results.
  arXiv  Detail & Related papers  (2021-10-05T15:39:11Z)
• ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection [102.9428507180728]
  A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples. We observe that there exist natural correlations and anti-correlations among human-object interactions. We present techniques to learn these priors and leverage them for more effective training, especially on rare classes.
  arXiv  Detail & Related papers  (2021-09-09T06:02:50Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Impact of Attention on Adversarial Robustness of Image Classification Models [0.9176056742068814]
  Adrial attacks against deep learning models have gained significant attention. Recent works have proposed explanations for the existence of adversarial examples and techniques to defend the models against these attacks. This work aims at a general understanding of the impact of attention on adversarial robustness.
  arXiv  Detail & Related papers  (2021-09-02T13:26:32Z)
• Spectrum-Guided Adversarial Disparity Learning [52.293230153385124]
  We propose a novel end-to-end knowledge directed adversarial learning framework. It portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art.
  arXiv  Detail & Related papers  (2020-07-14T05:46:27Z)

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.