Foundation model enhanced derivative-free cognitive diagnosis

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References

[1] Liu Y J, Zhang T C, Wang X C, Yu G, Li T. New development of cognitive diagnosis models. Frontiers of Computer Science, 2023, 17(1): 171604

[2] Haberman S J. Identifiability of parameters in item response models with unconstrained ability distributions. ETS Research Report Series, 2005, 2005(2): i−22

[3] De La Torre J.. Dina model and parameter estimation: a didactic. Journal of Educational and Behavioral Statistics, 2009, 34(1): 115−130

[4] Wang F, Liu Q, Chen E H, Huang Z Y, Chen Y Y, Yin Y, Huang Z, Wang S J. Neural cognitive diagnosis for intelligent education systems. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 6153–6161

[5] Wang F, Liu Q, Chen E H, Huang Z Y, Yin Y, Wang S J, Su Y. NeuralCD: a general framework for cognitive diagnosis. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(8): 8312−8327

[6] Chen X Z, Wu L, Liu F, Chen L, Zhang K, Hong R C, Wang M. Disentangling cognitive diagnosis with limited exercise labels. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 792

[7] Shen J H, Qian H, Zhang W, Zhou A M. Symbolic cognitive diagnosis via hybrid optimization for intelligent education systems. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 14928–14936

[8] Wang Z, Lamb A, Saveliev E, Cameron P, Zaykov Y, Hernández-Lobato J M, Turner R E, Baraniuk R G, Barton C, Jones S P, Woodhead S, Zhang C. Diagnostic questions: the neurips 2020 education challenge. 2020, arXiv preprint arXiv: 2007.12061

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62106076).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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