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Found 5 papers, 4 papers with code
Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing
Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states.
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
The key idea is to first break the rote memories by network pruning to address memorization overfitting in the inner loop, and then the gradients of pruned sub-networks naturally form the high-quality augmentation of the meta-gradient to alleviate learner overfitting in the outer loop.
MetaViewer: Towards A Unified Multi-View Representation
To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner.
DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing
Knowledge tracing aims to trace students' evolving knowledge states by predicting their future performance on concept-related exercises.
Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning.
