|Table of Contents|

FKA-DKT:Deep Knowledge Tracing Model Based on the Fusion of Knowledge and Ability(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年02期
Page:
129-139
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
FKA-DKT:Deep Knowledge Tracing Model Based on the Fusion of Knowledge and Ability
Author(s):
Chen Cheng1; Dong Yongquan1; 2; 3; Jia Rui1; Liu Yuan1
(1.College of Computer Science and Technology,Jiangsu Normal University,Xuzhou 221116,China)
(2.Jiangsu Engineering Technology Research Center of ICT in Education,Xuzhou 221116,China)
(3.Xuzhou Cloud Computing Engineering Technology Research Cente
Keywords:
knowledge tracing; deep knowledge tracing; personal ability modeling
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2024.02.015
Abstract:
Knowledge tracing(KT)is an important research problem in intelligent education,which predicts students' future answering behaviors by analyzing their historical interactions. Existing mainstream KT models only model students based on their knowledge mastery,neglecting the role of students' personal abilities in answering questions. Therefore,this paper proposes a deep knowledge tracing model(FKA-DKT)that integrates both knowledge and ability. First,we use the DKT model to construct a Knowledge-based Answer Prediction Network(KAPN),which predicts student answer correctness at the knowledge level. Then,we propose an Ability-based Answer Prediction Network(AAPN)to model students' abilities and predict answer correctness at the ability level. Finally,we linearly combine the predictions from KAPN and AAPN to integrate both knowledge and ability information for answer prediction. Experimental results on four publicly available datasets show that compared to existing mainstream methods,FKA-DKT achieves significant performance improvements in terms of the AUC metric.

References:

[1]CORBETT A T,ANDERSON J R. Knowledge tracing:modeling the acquisition of procedural knowledge[J]. User modeling and user-adapted interaction,1994,4(4):253-278.
[2]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(01):81-104.
[3]PIECH C,BASSEN J,HUANG J,et al. Deep knowledge tracing[J]. Advances in neural information processing systems,2015,28:505-513.
[4]ZHANG J,SHI X,KING I,et al. Dynamic key-value memory networks for knowledge tracing[C/OL]//Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva,CHE:International World Wide Web Conferences Steering Committee,2017:765-774[2022-02-20]. https://doi.org/10.1145/3038912.3052580.
[5]PANDEY S,KARYPIS G. A self-attentive model for knowledge tracing[J/OL]. ArXiv:1907.06837[cs,Stat],2019[2022-02-21]. http://arxiv.org/abs/1907.06837.
[6]GHOSH A,HEFFERNAN N,LAN A S. Context-aware attentive knowledge tracing[C/OL]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York,NY,USA:Association for Computing Machinery,2020:2330-2339[2022-02-21]. https://doi.org/10.1145/3394486.3403282.
[7]BAKER R S J D,CORBETT A T,ALEVEN V. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing[C]//Intelligent Tutoring Systems. Berlin,Heidelberg:Springer,2008:406-415.
[8]PARDOS Z A,HEFFERNAN N T. KT-idem:introducing item difficulty to the knowledge tracing model[C]//User Modeling,Adaption and Personalization. Berlin,Heidelberg:Springer,2011:243-254.
[9]KÄSER T,KLINGLER S,SCHWING A G,et al. Dynamic bayesian networks for student modeling[J]. IEEE transactions on learning technologies,2017,10(4):450-462.
[10]李晓光,魏思齐,张昕,等. LFKT:学习与遗忘融合的深度知识追踪模型[J]. 软件学报,2021,32(03):818-830.
[11]HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural computation,1997,9(8):1735-1780.
[12]CHUNG J,GULCEHRE C,CHO K,et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J/OL]. ArXiv:1412.3555[Cs],2014[2022-01-28]. http://arxiv.org/abs/1412.3555.
[13]MINN S,YU Y,DESMARAIS M C,et al. Deep knowledge tracing and dynamic student classification for knowledge tracing[C]//2018 IEEE International Conference on Data Mining(ICDM). IEEE,2018:1182-1187.
[14]YEUNG C K,YEUNG D Y. Addressing two problems in deep knowledge tracing via prediction-consistent regularization[Z/OL]. London,United Kingdom:Association for Computing Machinery,2018:1-10(2018). https://doi.org/10.1145/3231644.3231647.
[15]CHOI Y,LEE Y,CHO J,et al. Towards an appropriate query,key,and value computation for knowledge tracing[C/OL]//Proceedings of the Seventh ACM Conference on Learning@Scale. New York,NY,USA:Association for Computing Machinery,2020:341-344[2022-06-09]. https://doi.org/10.1145/3386527.3405945.
[16]ZHOU Y,LI X,CAO Y,et al. LANA:towards personalized deep knowledge tracing through distinguishable interactive sequences:arXiv:2105.06266[Z/OL]. arXiv,2021(2021-04-20)[2023-03-20]. http://arxiv.org/abs/2105.06266.
[17]LIU S,YU J,LI Q,et al. Ability boosted knowledge tracing[J]. Information sciences,2022,596:567-587.
[18]GABRIELLA C,GRILLI L,LIMONE P,et al. Deep learning for knowledge tracing in learning analytics:an overview[C]//CEUR Workshop Proceedings. Foggia,Italy:CEUR-WS,2021:1-10.
[19]WANG W,MA H,ZHAO Y,et al. Tracking knowledge proficiency of students with calibrated q-matrix[J]. Expert systems with applications,2022,192:116454.
[20]VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[J/OL]. ArXiv:1706.03762[Cs],2017[2021-11-26]. http://arxiv.org/abs/1706.03762.
[21]LING C X,HUANG J,ZHANG H. AUC:a statistically consistent and more discriminating measure than accuracy[C]//International Joint Conference on Intelligence. San Francisco,CA,USA:Morgan Kaufmann Publishers Inc,2003:519-524.
[22]KINGMA D P,BA J. Adam:a method for stochastic optimization:arXiv:1412.6980[Z/OL]. arXiv,2017(2017-01-29)[2022-06-24]. http://arxiv.org/abs/1412.6980.
[23]LIU Q,SHEN S,HUANG Z,et al. A survey of knowledge tracing[J/OL]. ArXiv:2105.15106[Cs],2021[2022-02-16]. http://arxiv.org/abs/2105.15106.

Memo

Memo:
-
Last Update: 2024-06-15