Consistency and Monotonicity Regularization for Neural Knowledge Tracing

• URL: http://arxiv.org/abs/2105.00607v1
• Date: Mon, 3 May 2021 02:36:29 GMT
• Title: Consistency and Monotonicity Regularization for Neural Knowledge Tracing
• Authors: Seewoo Lee, Youngduck Choi, Juneyoung Park, Byungsoo Kim and Jinwoo Shin
• Abstract summary: Knowledge Tracing (KT) tracking a human's knowledge acquisition is a central component in online learning and AI in Education. We propose three types of novel data augmentation, coined replacement, insertion, and deletion, along with corresponding regularization losses. Extensive experiments on various KT benchmarks show that our regularization scheme consistently improves the model performances.
• Score: 50.92661409499299
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a central component in online learning and AI in Education. In this paper, we present a simple, yet effective strategy to improve the generalization ability of KT models: we propose three types of novel data augmentation, coined replacement, insertion, and deletion, along with corresponding regularization losses that impose certain consistency or monotonicity biases on the model's predictions for the original and augmented sequence. Extensive experiments on various KT benchmarks show that our regularization scheme consistently improves the model performances, under 3 widely-used neural networks and 4 public benchmarks, e.g., it yields 6.3% improvement in AUC under the DKT model and the ASSISTmentsChall dataset.

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