Post-hoc local explanation-事后可解释性 -解释模型为什么会做出这样的预测或者决策文章来源地址https://www.toymoban.com/news/detail-488135.html
Lu, Y.; Wang, D.; Meng, Q.; and Chen, P. 2020. Towards interpretable deep learning models for knowledge tracing. In International Conference on Artifificial Intelligence in Edu cation , 185–190. Springer.
Lu, Y.; Wang, D.; Chen, P.; Meng, Q.; and Yu, S. 2022. Interpreting Deep Learning Models for Knowledge Tracing. International Journal of Artifificial Intelligence in Education , 1–24.
Global interpretability with explainable structures-将一个可解释的认知模块嵌入到现有的DLKT架构中，以更好地理解知识状态建模过程
Wang, F.; Liu, Q.; Chen, E.; Huang, Z.; Chen, Y.; Yin, Y.; Huang, Z.; and Wang, S. 2020a. Neural cognitive diagnosis for intelligent education systems. In Proceedings of the AAAI Conference on Artifificial Intelligence , 6153–6161.
Zhao, J.; Bhatt, S.; Thille, C.; Zimmaro, D.; and Gattani, N. 2020. Interpretable personalized knowledge tracing and next learning activity recommendation. In Proceedings of the Seventh ACM Conference on Learning@Scale , 325–328.
Pu, Y.; Wu, W.; Peng, T.; Liu, F.; Liang, Y.; Yu, X.; Chen, R.; and Feng, P. 2022. EAKT: Embedding Cognitive Framework with Attention for Interpretable Knowledge Tracing.Scientifific Reports .
Global interpretability with explainable parameters-直接使用认知可解释模型来估计学生正确回答问题的概率。这些模型中的可解释的参数是从DLKT模型的输出中得到的
DKT-IRT-Converse, G.; Pu, S.; and Oliveira, S. 2021. Incorporating item response theory into knowledge tracing. In International Conference on Artifificial Intelligence in Education ,114–118. Springer.
DeepIRT-Yeung, C.-K. 2019. Deep-IRT: Make deep learning based knowledge tracing explainable using item response theory. arXiv preprint arXiv:1904.11738 .
2023_AAAI_Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations_Jiahao Chen1 , Zitao Liu2*, Shuyan Huang1 , Qiongqiong Liu1 , Weiqi Luo2
IEKT-Long, T.; Liu, Y.; Shen, J.; Zhang, W.; and Yu, Y. 2021. Tracing Knowledge State with Individual Cognition and Acquisition Estimation. In Proceedings of the 44th InternationalACM SIGIR Conference on Research and Development in Information Retrieval , 173–182.
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