National Key R&D Program of China [2022ZD0117103]; National Natural Science Foundation of China [62077021, 62107017, 62207017, 62293554]; China Postdoctoral Science Foundation [2020M682454, 2022M711282]; Hubei Provincial Natural Science Foundation of China [2022CFB414]; Knowledge Innovation Program of Wuhan-Shuguang Project [2022010801020287]; Fundamental Research Funds for the Central Universities [CCNU22LJ005, CCNU22XJ033]
机构署名：
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摘要：
Knowledge tracing (KT), which estimates and traces the degree of learners' mastery of concepts based on students' responses to learning resources, has become an increasingly relevant problem in intelligent education. The accuracy of predictions greatly depends on the quality of question representations. While contrastive learning has been commonly used to generate high-quality representations, the selection of positive and negative samples for knowledge tracing remains a challenge. To address this issue, we propose an adversarial bootstrapped question representation (ABQR) model, which can gen...
