ABSTRACT
Empirical results have shown that deep neural networks achieve superior performance in the application of Knowledge Tracing. However, the design of recurrent cells like long short term memory (LSTM) cells or gated recurrent units (GRU) is influenced largely by applications in natural language processing. They were proposed and evaluated in the context of sequence to sequence modeling, like machine translation. Even though the LSTM cell works well for knowledge tracing, it is unknown if its architecture is ideally suited for knowledge tracing. Despite the fact that there are several recurrent neural network based architectures proposed for knowledge tracing, the methodologies rely on empirical observations and trial and error, which may not be efficient or scalable. In this study, we investigate using reinforcement learning for the automatic design of recurrent neural network cells for knowledge tracing, showing improved performance compared to the LSTM cell. We also discuss a potential method for model regularization using neural architecture search.
Supplemental Material
- Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4 (1994), 253--278.Google Scholar
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.Google ScholarDigital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Xinyi Ding and Eric C Larson. 2019. Why Deep Knowledge Tracing has less Depth than Anticipated. International Educational Data Mining Society (2019).Google Scholar
- Fritz Drasgow and Charles L Hulin. 1990. Item response theory. (1990).Google Scholar
- Mohammad M Khajah, Yun Huang, José P González-Brenes, Michael C Mozer, and Peter Brusilovsky. 2014. Integrating knowledge tracing and item response theory: A tale of two frameworks. In CEUR Workshop Proceedings, Vol. 1181. University of Pittsburgh, 7--15.Google Scholar
- Quinn McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12, 2 (1947), 153--157.Google Scholar
- Philip I Pavlik Jr, Hao Cen, and Kenneth R Koedinger. 2009. Performance Factors Analysis--A New Alternative to Knowledge Tracing. Online Submission (2009).Google Scholar
- Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean. 2018. Efficient neural architecture search via parameter sharing. arXiv preprint arXiv:1802.03268 (2018).Google Scholar
- Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in neural information processing systems. 505--513.Google Scholar
- J. Stamper, A. Niculescu-Mizil, S. Ritter, G.J. Gordon, and K. R. Koedinger. 2010. Algebra I 2005--2006. Challenge data set from KDD Cup 2010 Educational Data Mining Challenge. Find it at http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp. (2010).Google Scholar
- Xiaolu Xiong, Siyuan Zhao, Eric G Van Inwegen, and Joseph E Beck. 2016. Going deeper with deep knowledge tracing. International Educational Data Mining Society (2016).Google Scholar
- Chun-Kit Yeung. 2019. Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory. arXiv preprint arXiv:1904.11738 (2019).Google Scholar
- Michael V Yudelson, Kenneth R Koedinger, and Geoffrey J Gordon. 2013. Individualized bayesian knowledge tracing models. In International conference on artificial intelligence in education. Springer, 171--180.Google ScholarCross Ref
- Jiani Zhang, Xingjian Shi, Irwin King, and Dit-Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th international conference on World Wide Web. 765--774.Google ScholarDigital Library
- Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016).Google Scholar
Index Terms
- Automatic RNN Cell Design for Knowledge Tracing using Reinforcement Learning
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