Deep Knowledge Tracing Model with the Integration of Skills Relation and Forgetting Degree

Jia Rui; Dong Yongquan; Liu Yuan; Chen Cheng

doi:10.7544/issn1000-1239.202330697

Journal of Computer Research and Development > 2025 > 62(2): 364-373. > DOI: 10.7544/issn1000-1239.202330697 CSTR: 32373.14.issn1000-1239.202330697

Jia Rui, Dong Yongquan, Liu Yuan, Chen Cheng. Deep Knowledge Tracing Model with the Integration of Skills Relation and Forgetting Degree[J]. Journal of Computer Research and Development, 2025, 62(2): 364-373. DOI: 10.7544/issn1000-1239.202330697

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Deep Knowledge Tracing Model with the Integration of Skills Relation and Forgetting Degree

Jia Rui^1,,
Dong Yongquan^{1, 2, 3, ,},
Liu Yuan¹,
Chen Cheng¹

1.
College of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221116
2.
Jiangsu Education Informatization Engineering Technology Research Center, Xuzhou, Jiangsu 221116
3.
Xuzhou Cloud Computing Engineering Technology Research Center, Xuzhou, Jiangsu 221116

Funds: This work was supported by the National Natural Science Foundation of China (61872168), the Project of Jiangsu Province Education Science “14th Five-Year Plan” (D/2021/01/112), and the Graduate Research and Practice Innovation Program of Jiangsu Normal University (2022XKT1549).

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Author Bio:
Jia Rui: born in 1999. Master candidate. Her main research interests include data mining, knowledge tracing, and machine learning

Dong Yongquan: born in 1979. PhD, professor, master supervisor. His main research interests include deep learning, data mining, and education informatization

Liu Yuan: born in 1997. Master candidate. His main research interests include course recommendation and machine learning

Chen Cheng: born in 1999. Master candidate. His main research interests include data mining, knowledge tracing, and machine learning
Received Date: August 22, 2023
Revised Date: January 07, 2024
Accepted Date: March 05, 2024
Available Online: March 06, 2024

Graphical Abstract

Abstract

Abstract

Knowledge tracing is a pivotal technique for modeling students’ knowledge level, and typically relies on their past learning interactions to predict their future performance on exercises. These interactions represent a student’s process of answering a sequence of questions. Current knowledge tracing methods ignore times a skill has been practiced when modeling student’s forgetting behaviors. Also, few models consider the relation between skills and its influence on performance prediction. To address these questions, we propose a deep knowledge tracing model with the integration of skills relation and forgetting degree. Firstly, a relation matrix is constructed using statistical methods to capture the relation between skills. Secondly, the time intervals between interactions and the times a student practices the same skill are used to compute the forgetting degree of each skill for better modeling of students’ forgetting behaviors. Finally, skills relation and forgetting degrees are integrated into an attention module to obtain the influence of each past interaction on future performance prediction. Based on new attention weights, students’ performance on future exercises and knowledge level can be predicted. Experiments on two real-world online education datasets, algebra2005-2006 and ASSISTment2012, demonstrate that the proposed model achieves better prediction results compared with existing mainstream methods.
- knowledge tracing,
- deep knowledge tracing,
- skills relation,
- forgetting degree,
- intelligent education

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References (18)

References

[1]	刘铁园,陈威,常亮,等. 基于深度学习的知识追踪研究进展[J]. 计算机研究与发展,2022,59(1):81−104 doi: 10.7544/issn1000-1239.20200848 Liu Tieyuan, Chen Wei, Chang Liang, et al. Research advances in the knowledge tracing based on deep learning[J]. Journal of Computer Research and Development, 2022, 59(1): 81−104 (in Chinese) doi: 10.7544/issn1000-1239.20200848
[2]	刘坤佳,李欣奕,唐九阳,等. 可解释深度知识追踪模型[J]. 计算机研究与发展,2021,58(12):2618−2629 doi: 10.7544/issn1000-1239.2021.20211021 Liu Kunjia, Li Xinyi, Tang Jiuyang, et al. Interpretable deep knowledge tracing model[J]. Journal of Computer Research and Development, 2021, 58(12): 2618−2629 (in Chinese) doi: 10.7544/issn1000-1239.2021.20211021
[3]	王宇,朱梦霞,杨尚辉,等. 深度知识追踪模型综述和性能比较[J]. 软件学报,2023,34(3):1365−1395 Wang Yu, Zhu Mengxia, Yang Shanghui, et al. Review and performance comparison of deep knowledge tracing models[J]. Journal of Software, 2023, 34(3): 1365−1395 (in Chinese)
[4]	Yeung C K, Yeung D Y. Incorporating features learned by an enhanced deep knowledge tracing model for STEM/Non-STEM job prediction[J]. International Journal of Artificial Intelligence in Education, 2019, 29(3): 253−278
[5]	Corbett A T, Anderson J R. Knowledge tracing: Modeling the acquisition of procedural knowledge[J]. User Modelling and User-Adapted Interaction, 1995, 4(4): 253−278 doi: 10.1007/BF01099821
[6]	Piech C, Spencer J, Huang J, et al. Deep knowledge tracing[J]. arXiv preprint, arXiv: 1506.05908, 2015
[7]	Zhang Jiani, Shi Xingjia, King I, et al. Dynamic key-value memory networks for knowledge tracing[C]//Proc of the 26th Int Conf on World Wide Web. New York: ACM, 2017: 765−774
[8]	Pandey S, Karypis G. A self-attentive model for knowledge tracing[J]. arXiv preprint, arXiv: 1907.06837, 2019
[9]	Choffin B, Popineau F, Bourda Y, et al. DAS3H: Modeling student learning and forgetting for optimally scheduling distributed practice of skills[J]. arXiv preprint, arXiv: 1905.06873, 2019
[10]	Nagatani K, Zhang Qian, Sato M, et al. Augmenting knowledge tracing by considering forgetting behavior[C]//Proc of the 19th World Wide Web Conf. New York: ACM, 2019: 3101−3107
[11]	Pandey S, Srivastava J. RKT: Relation-aware self-attention for knowledge tracing[C]//Proc of the 29th ACM Int Conf on Information & Knowledge Management. New York: ACM, 2020: 1205−1214
[12]	Liu Qi, Shen Shuanghong, Huang Zhenya, et al. A survey of knowledge tracing[J]. arXiv preprint, arXiv: 2105.15106, 2021
[13]	Yudelson M V, Koedinger K R, Gordon G J. Individualized Bayesian knowledge tracing models[C]//Proc of the 16th Int Conf on Artificial Intelligence in Education. Berlin: Springer, 2013: 171−180
[14]	Baker R S J, Corbett A T, Aleven V. More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing[G]//LNCS 5091: Proc of the 9th Int Conf on Intelligent Tutoring Systems (ITS 2008). Berlin: Springer, 2008: 406−415
[15]	Pardos Z A, Heffernan N T. Modeling individualization in a Bayesian networks implementation of knowledge tracing[G]//LNCS 6075: Proc of the 18th Int Conf on User. Berlin: Springer, 2010: 255−266
[16]	Yeung C K, Yeung D Y. Addressing Two problems in deep knowledge tracing via prediction-consistent regularization[J]. arXiv preprint, arXiv: 1806.02180, 2018
[17]	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proc of the 31st Int Conf on Neural Information Processing Systems. San Dieg, California: Curran Associates, 2017: 5998−6008
[18]	Zhou Yuhao, Li Xihua, Cao Yunbo, et al. LANA: Towards personalized deep knowledge tracing through distinguishable interactive sequences[J]. arXiv preprint, arXiv: 2105.06266, 2021

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