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Cited by 141

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck·2021·LAK21: 11th International Learning Analytics and Knowledge Conference
DOI 10.1145/3448139.3448188
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Crossref · 10.1145/3448139.3448188

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  • Domain-specific features may be just as important as model structure for improving KT performance.

    “suggesting that domain-specific features may be just as important as model structure.”

    From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [44]

    Section 5.4 reports that adding temporal features to SAINT improved knowledge-tracing accuracy and AUC, supporting the importance of these features for performance. The paper does not establish that features are as important as model structure; its comparison with SAINT does not measure their relative importance. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The improvement of Code-DKT over DKT is comparable to that of other modern KT models over DKT (2-4% AUC).

    “This improvement is comparable to that of other modern KT models over DKT (2-4%) [31, 44]”

    From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [44]

  • Recent models have outperformed DKT by only about 2-4% AUC, suggesting DKT is still representative of modern deep KT models.

    “Some recent models have outperformed DKT, but only by about 2-4% AUC [31, 44], suggesting that DKT is still representative of modern deep KT models.”

    From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [44]

  • AUC is a standard evaluation metric for KT models and is more appropriate than accuracy for imbalanced datasets like ours.

    “Our primary performance metric is AUC, a standard evaluation metric for KT models [34, 44, 31], as it uses the predicted probability of success, rather than a binary correctness prediction, and is more appropriate than accuracy for imbalanced datasets like ours (23% positive).”

    From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [44]

  • DKT is a commonly used baseline in recent KT papers.

    “as it is a commonly used baseline in other more recent KT papers [31, 44].”

    From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [44]

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Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 22

  1. Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450(2016). Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450(2016). · arXiv
  2. Youngduck Choi Youngnam Lee Junghyun Cho Jineon Baek Byungsoo Kim Yeongmin Cha Dongmin Shin Chan Bae and Jaewe Heo. 2020. Towards an Appropriate Query Key and Value Computation for Knowledge Tracing. arXiv preprint arXiv:2002.07033(2020). Youngduck Choi Youngnam Lee Junghyun Cho Jineon Baek Byungsoo Kim Yeongmin Cha Dongmin Shin Chan Bae and Jaewe Heo. 2020. Towards an Appropriate Query Key and Value Computation for Knowledge Tracing. arXiv preprint arXiv:2002.07033(2020). · arXiv
  3. 10.1007/978-3-030-52240-7_13 · DOI · Paper page
  4. 10.1007/978-3-540-69132-7_44 · DOI
  5. 10.1145/3394486.3403282 · DOI
  6. Zhenya Huang , Yu Yin , Enhong Chen , Hui Xiong , Yu Su , Guoping Hu , 2019 . EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction . IEEE Transactions on Knowledge and Data Engineering ( 2019). Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, Guoping Hu, 2019. EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction. IEEE Transactions on Knowledge and Data Engineering (2019).
  7. 10.1145/1595496.1562972 · DOI
  8. Kangwook Lee , Jichan Chung , Yeongmin Cha , and Changho Suh . 2016 . Machine Learning Approaches for Learning Analytics: Collaborative Filtering Or Regression With Experts? . In NIPS Workshop, Dec. 1–11 . Kangwook Lee, Jichan Chung, Yeongmin Cha, and Changho Suh. 2016. Machine Learning Approaches for Learning Analytics: Collaborative Filtering Or Regression With Experts?. In NIPS Workshop, Dec. 1–11.
  9. Youngnam Lee Youngduck Choi Junghyun Cho Alexander R Fabbri Hyunbin Loh Chanyou Hwang Yongku Lee Sang-Wook Kim and Dragomir Radev. 2019. Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess. arXiv preprint arXiv:1906.10910(2019). Youngnam Lee Youngduck Choi Junghyun Cho Alexander R Fabbri Hyunbin Loh Chanyou Hwang Yongku Lee Sang-Wook Kim and Dragomir Radev. 2019. Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess. arXiv preprint arXiv:1906.10910(2019). · arXiv
  10. Hiromi Nakagawa , Yusuke Iwasawa , and Yutaka Matsuo . 2019 . Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. In 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 156–163 . Hiromi Nakagawa, Yusuke Iwasawa, and Yutaka Matsuo. 2019. Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. In 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 156–163.
  11. Shalini Pandey and George Karypis. 2019. A Self-Attentive model for Knowledge Tracing. arXiv preprint arXiv:1907.06837(2019). Shalini Pandey and George Karypis. 2019. A Self-Attentive model for Knowledge Tracing. arXiv preprint arXiv:1907.06837(2019). · arXiv
  12. Zachary Pardos and Neil Heffernan . 2010. Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm . In Educational Data Mining 2010 . Zachary Pardos and Neil Heffernan. 2010. Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm. In Educational Data Mining 2010.
  13. 10.1007/978-3-642-13470-8_24 · DOI
  14. 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. 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.
  15. Michael Sao Pedro , Ryan Baker , and Janice Gobert . 2013. Incorporating scaffolding and tutor context into bayesian knowledge tracing to predict inquiry skill acquisition . In Educational Data Mining 2013 . Michael Sao Pedro, Ryan Baker, and Janice Gobert. 2013. Incorporating scaffolding and tutor context into bayesian knowledge tracing to predict inquiry skill acquisition. In Educational Data Mining 2013.
  16. 10.1145/3397271.3401288 · DOI
  17. 10.1016/j.procs.2010.08.006 · DOI
  18. Hanshuang Tong , Yun Zhou , and Zhen Wang . 2020 . HGKT: Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing. arXiv preprint arXiv:2006.16915(2020). Hanshuang Tong, Yun Zhou, and Zhen Wang. 2020. HGKT: Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing. arXiv preprint arXiv:2006.16915(2020). · arXiv
  19. Brett van De Sande . 2013 . Properties of the bayesian knowledge tracing model . Journal of Educational Data Mining 5 , 2 (2013), 1 . Brett van De Sande. 2013. Properties of the bayesian knowledge tracing model. Journal of Educational Data Mining 5, 2 (2013), 1.
  20. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Łukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Łukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.
  21. 10.1007/978-3-642-39112-5_18 · DOI
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Metadata

Authors
Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., & Choi, Y.
Affiliations
Riiid! AI Research, South Korea
Venue
LAK21: 11th International Learning Analytics and Knowledge Conference
Publisher
ACM
Published
2021-04-12
Pages
490-496
DOI
10.1145/3448139.3448188
Cited by
141
References
5
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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