Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 343-348.DOI: 10.11772/j.issn.1001-9081.2022010024
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Received:
2022-01-10
Revised:
2022-03-11
Accepted:
2022-03-14
Online:
2022-03-22
Published:
2023-02-10
Contact:
Meng ZHANG
About author:
SHAO Xiaomeng, born in 1997, M. S. candidate. Her research interests include machine learning, educational data mining.
Supported by:
通讯作者:
张猛
作者简介:
邵小萌(1997—),女,河北沧州人,硕士研究生,CCF会员,主要研究方向:机器学习、教育数据挖掘
基金资助:
CLC Number:
Xiaomeng SHAO, Meng ZHANG. Temporal convolutional knowledge tracing model with attention mechanism[J]. Journal of Computer Applications, 2023, 43(2): 343-348.
邵小萌, 张猛. 融合注意力机制的时间卷积知识追踪模型[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 343-348.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022010024
数据集 | 学生数 | 题目数 | 交互记录数 |
---|---|---|---|
ASSISTments2009 | 4 151 | 110 | 325 637 |
ASSISTments2015 | 19 840 | 100 | 683 801 |
Statics2011 | 333 | 1 223 | 189 297 |
Synthetic-5 | 4 000 | 50 | 200 000 |
Tab.1 Dataset introduction
数据集 | 学生数 | 题目数 | 交互记录数 |
---|---|---|---|
ASSISTments2009 | 4 151 | 110 | 325 637 |
ASSISTments2015 | 19 840 | 100 | 683 801 |
Statics2011 | 333 | 1 223 | 189 297 |
Synthetic-5 | 4 000 | 50 | 200 000 |
模型 | ASSISTments2009 | ASSISTments2015 | Statics2011 | Synthetic-5 | ||||
---|---|---|---|---|---|---|---|---|
AUC | Acc | AUC | Acc | AUC | Acc | AUC | Acc | |
DKT | 82.05 | 77.31 | 72.89 | 75.22 | 81.63 | 80.93 | 81.34 | 74.28 |
DKVMN | 81.51 | 76.56 | 72.75 | 75.01 | 81.61 | 81.23 | 82.92 | 75.56 |
CKT | 81.71 | 77.09 | 72.08 | 74.89 | 82.93 | 81.45 | 82.51 | 75.34 |
SAKT | 82.07 | 77.01 | 85.39 | 78.59 | 82.33 | 81.27 | 83.89 | 76.87 |
ATCKT | 84.99 | 85.65 | 92.22 | 86.11 | 83.71 | 81.59 | 87.56 | 79.98 |
Tab.2 Comparison of experimental results of different knowledge tracing models
模型 | ASSISTments2009 | ASSISTments2015 | Statics2011 | Synthetic-5 | ||||
---|---|---|---|---|---|---|---|---|
AUC | Acc | AUC | Acc | AUC | Acc | AUC | Acc | |
DKT | 82.05 | 77.31 | 72.89 | 75.22 | 81.63 | 80.93 | 81.34 | 74.28 |
DKVMN | 81.51 | 76.56 | 72.75 | 75.01 | 81.61 | 81.23 | 82.92 | 75.56 |
CKT | 81.71 | 77.09 | 72.08 | 74.89 | 82.93 | 81.45 | 82.51 | 75.34 |
SAKT | 82.07 | 77.01 | 85.39 | 78.59 | 82.33 | 81.27 | 83.89 | 76.87 |
ATCKT | 84.99 | 85.65 | 92.22 | 86.11 | 83.71 | 81.59 | 87.56 | 79.98 |
数据集 | TCKT-One-Hot | DKT | ||
---|---|---|---|---|
AUC | Acc | AUC | Acc | |
ASSISTments2009 | 83.17 | 78.18 | 82.05 | 77.31 |
ASSISTments2015 | 82.59 | 78.92 | 72.89 | 75.22 |
Statics2011 | 83.44 | 81.26 | 81.63 | 80.93 |
Synthetic-5 | 87.15 | 79.32 | 81.34 | 74.28 |
Tab.3 Prediction results of TCKT-One-Hot and DKT models
数据集 | TCKT-One-Hot | DKT | ||
---|---|---|---|---|
AUC | Acc | AUC | Acc | |
ASSISTments2009 | 83.17 | 78.18 | 82.05 | 77.31 |
ASSISTments2015 | 82.59 | 78.92 | 72.89 | 75.22 |
Statics2011 | 83.44 | 81.26 | 81.63 | 80.93 |
Synthetic-5 | 87.15 | 79.32 | 81.34 | 74.28 |
题目编号 | 知识概念 | 题目编号 | 知识概念 |
---|---|---|---|
86 | 圆柱体积 | 99 | 线性方程组 |
75 | 球体积 | 77 | 数轴 |
85 | 圆柱体表面积 | 22 | 整数加法 |
89 | 用作图法求解线性方程组 |
Tab.4 Exercise problem numbers and corresponding knowledge concepts
题目编号 | 知识概念 | 题目编号 | 知识概念 |
---|---|---|---|
86 | 圆柱体积 | 99 | 线性方程组 |
75 | 球体积 | 77 | 数轴 |
85 | 圆柱体表面积 | 22 | 整数加法 |
89 | 用作图法求解线性方程组 |
数据集 | ATCKT | DKT |
---|---|---|
ASSISTments2009 | 4 | 13 |
ASSISTments2015 | 14 | 19 |
Statics2011 | 1 | 13 |
Synthetic-5 | 3 | 2 |
Tab.5 Comparison of training time between ATCKT and DKT models
数据集 | ATCKT | DKT |
---|---|---|
ASSISTments2009 | 4 | 13 |
ASSISTments2015 | 14 | 19 |
Statics2011 | 1 | 13 |
Synthetic-5 | 3 | 2 |
1 | PIECH C, BASSEN J, HUANG J, et al. Deep knowledge tracing[C]// Proceedings of the 28th International Conference on Neural Information Processing System - Volume 1. Cambridge: MIT Press, 2015:505-513. |
2 | KHAJAH M, LINDSEY R V, MOZER M C. How deep is knowledge tracing?[C]// Proceedings of the 9th International Conference on Educational Data Mining. Massachusetts: International Educational Data Mining Society, 2016:94-101. |
3 | YEUNG C K, YEUNG D Y. Addressing two problems in deep knowledge tracing via prediction-consistent regularization[C]// Proceedings of the 5th Annual ACM Conference on Learning at Scale. New York: ACM, 2018: No.5. 10.1145/3231644.3231647 |
4 | LI X, LIN T W, LIU X, et al. Deep concept-wise temporal convolutional networks for action localization[C]// Proceedings of the 28th ACM International Conference on Multimedia. New York: ACM, 2020:4004-4012. 10.1145/3394171.3413860 |
5 | YOU J X, WANG Y C, PAL A, et al. Hierarchical temporal convolutional networks for dynamic recommender systems[C]// Proceedings of the 2019 World Wide Web Conference. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2019:2236-2246. 10.1145/3308558.3313747 |
6 | 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. 10.1007/bf01099821 |
7 | WILSON K H, KARKLIN Y, HAN B J, et al. Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation[C]// Proceedings of the 9th International Conference on Educational Data Mining. Massachusetts: International Educational Data Mining Society, 2016:539-544. |
8 | VIE J J, KASHIMA H. Knowledge tracing machines: factorization machines for knowledge tracing[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019:750-757. 10.1609/aaai.v33i01.3301750 |
9 | MINN S, YU Y, DESMARAIS M C, et al. Deep knowledge tracing and dynamic student classification for knowledge tracing[C]// Proceedings of the 2018 IEEE International Conference on Data Mining. Piscataway: IEEE, 2018:1182-1187. 10.1109/icdm.2018.00156 |
10 | ZHANG J N, SHI X J, KING I, et al. Dynamic key-value memory networks for knowledge tracing[C]// Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2017:765-774. 10.1145/3038912.3052580 |
11 | PANDEY S, KARYPIS G. A self-attentive model for knowledge tracing[C]// Proceedings of the 12th International Conference on Educational Data Mining. Massachusetts: International Educational Data Mining Society, 2019: 384-389. |
12 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017:6000-6010. |
13 | NAKAGAWA H, IWASAWA Y, MATSUO Y. Graph-based knowledge tracing: modeling student proficiency using graph neural network[C]// Proceedings of the 2019 IEEE/CVF International Conference on Web Intelligence. Piscataway: IEEE, 2019:156-163. 10.1145/3350546.3352513 |
14 | 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. 10.7544/issn1000-1239.2021.20211021 |
LIU K J, LI X Y, TANG J Y, et al. Interpretable deep knowledge tracing[J]. Journal of Computer Research and Development, 2021, 58(12): 2618-2629. 10.7544/issn1000-1239.2021.20211021 | |
15 | 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(3):818-830. |
LI X G, WEI S Q, ZHANG X, et al. LFKT: deep knowledge tracing model with learning and forgetting behavior merging[J]. Journal of Software, 2021, 32(3): 818-830. | |
16 | YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[EB/OL]. (2016-04-30) [2021-10-20].. 10.1109/cvpr.2017.75 |
17 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016:770-778. 10.1109/cvpr.2016.90 |
18 | FENG M Y, HEFFERNAN N, KOEDINGER K. Addressing the assessment challenge with an online system that tutors as it assesses[J]. User Modeling and User-Adapted Interaction, 2009, 19(3):243-266. 10.1007/s11257-009-9063-7 |
19 | SHEN S H, LIU Q, CHEN E H, et al. Convolutional knowledge tracing: modeling individualization in student learning process[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020:1857-1860. 10.1145/3397271.3401288 |
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