可解释性+knowledge tracing_interpreting deep learning models for knowledge tr

作者：代码探险家 | 2024-07-13 19:17:42

踩

interpreting deep learning models for knowledge tracing

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

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 International

ACM SIGIR Conference on Research and Development in Information Retrieval , 173–182.

声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/代码探险家/article/detail/821136