Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2747-2752.DOI: 10.11772/j.issn.1001-9081.2022081184
• Artificial intelligence • Previous Articles Next Articles
Haodong ZHENG, Hua MA, Yingchao XIE, Wensheng TANG()
Received:
2022-08-09
Revised:
2022-12-19
Accepted:
2022-12-26
Online:
2023-01-18
Published:
2023-09-10
Contact:
Wensheng TANG
About author:
ZHENG Haodong, born in 1996, M. S. candidate. His research interests include smart education, recommender system.Supported by:
通讯作者:
唐文胜
作者简介:
郑浩东(1996—),男,山西运城人,硕士研究生,主要研究方向:智慧教育、推荐系统基金资助:
CLC Number:
Haodong ZHENG, Hua MA, Yingchao XIE, Wensheng TANG. Knowledge tracing model based on graph neural network blending with forgetting factors and memory gate[J]. Journal of Computer Applications, 2023, 43(9): 2747-2752.
郑浩东, 马华, 谢颖超, 唐文胜. 融合遗忘因素与记忆门的图神经网络知识追踪模型[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2747-2752.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022081184
遗忘因素 | ASSIST | KDD | ||
---|---|---|---|---|
数据来源字段 | 预处理方法 | 数据来源字段 | 预处理方法 | |
重复学习知识点的次数 | skill_name | 在答题记录中统计各个学生完成的同一skill的习题个数,并归一化 | problem_name | 在答题记录中统计各个学生完成的同一problem的习题个数,并归一化 |
学生重复学习知识点 的间隔时间 | problem_start_time | 两个相邻相同知识点的problem_start_time作差,并归一化 | step_start_time | 两个相邻相同知识点的step_start_time作差,并归一化 |
顺序学习间隔时间 | problem_start_time, problem_end_time | 当前知识点的problem_start_time减去上一个知识点的problem_end_time,并归一化 | step_start_time, step_end_time | 当前知识点的step_start_time减去上一个知识点的step_end_time,并归一化 |
学生要求提示的次数 | hint_total | 直接对该字段数值进行归一化 | hints | 直接对该字段数值进行归一化 |
学生答题前的行为 | bottom_hint | 直接对该字段数值归一化 | correct_first_attempt | 直接对该字段数值归一化 |
答题正确率 | correct | 统计答题记录中学生correct值为1和0的个数,用r和e表示,计算学生历史答题正确率: | corrects, incorrects | 计算学生历史答题正确率:corrects/(corrects+incorrects),并将结果归一化 |
知识点原始掌握程度 | 历史学习记录 | 计算前一时刻模型预测的知识点掌握程度,将预测值归一化 | 历史学习记录 | 计算前一时刻模型预测的知识点掌握程度,将预测值归一化 |
Tab. 1 Data acquisition methods of forgetting factors
遗忘因素 | ASSIST | KDD | ||
---|---|---|---|---|
数据来源字段 | 预处理方法 | 数据来源字段 | 预处理方法 | |
重复学习知识点的次数 | skill_name | 在答题记录中统计各个学生完成的同一skill的习题个数,并归一化 | problem_name | 在答题记录中统计各个学生完成的同一problem的习题个数,并归一化 |
学生重复学习知识点 的间隔时间 | problem_start_time | 两个相邻相同知识点的problem_start_time作差,并归一化 | step_start_time | 两个相邻相同知识点的step_start_time作差,并归一化 |
顺序学习间隔时间 | problem_start_time, problem_end_time | 当前知识点的problem_start_time减去上一个知识点的problem_end_time,并归一化 | step_start_time, step_end_time | 当前知识点的step_start_time减去上一个知识点的step_end_time,并归一化 |
学生要求提示的次数 | hint_total | 直接对该字段数值进行归一化 | hints | 直接对该字段数值进行归一化 |
学生答题前的行为 | bottom_hint | 直接对该字段数值归一化 | correct_first_attempt | 直接对该字段数值归一化 |
答题正确率 | correct | 统计答题记录中学生correct值为1和0的个数,用r和e表示,计算学生历史答题正确率: | corrects, incorrects | 计算学生历史答题正确率:corrects/(corrects+incorrects),并将结果归一化 |
知识点原始掌握程度 | 历史学习记录 | 计算前一时刻模型预测的知识点掌握程度,将预测值归一化 | 历史学习记录 | 计算前一时刻模型预测的知识点掌握程度,将预测值归一化 |
序号 | d | ASSIST | KDD |
---|---|---|---|
1 | 8 | 0.807 | 0.815 |
2 | 16 | 0.825 | 0.831 |
3 | 32 | 0.849 | 0.822 |
4 | 64 | 0.814 | 0.819 |
Tab. 2 Comparison results of AUC value of different embedded dimensions
序号 | d | ASSIST | KDD |
---|---|---|---|
1 | 8 | 0.807 | 0.815 |
2 | 16 | 0.825 | 0.831 |
3 | 32 | 0.849 | 0.822 |
4 | 64 | 0.814 | 0.819 |
编号 | LT | RT | ST | CR | FA | HC | OM | ACC |
---|---|---|---|---|---|---|---|---|
a | × | × | × | × | × | × | × | 0.810 0 |
b | × | √ | √ | √ | √ | √ | √ | 0.813 3 |
c | √ | × | √ | √ | √ | √ | √ | 0.814 8 |
d | √ | √ | × | √ | √ | √ | √ | 0.812 5 |
e | √ | √ | √ | × | √ | √ | √ | 0.812 3 |
f | √ | √ | √ | √ | × | √ | √ | 0.812 0 |
g | √ | √ | √ | √ | √ | × | √ | 0.812 5 |
h | √ | √ | √ | √ | √ | √ | × | 0.817 8 |
i | √ | √ | √ | √ | √ | √ | √ | 0.835 0 |
Tab. 3 Effect analysis of forgetting factors on average ACC
编号 | LT | RT | ST | CR | FA | HC | OM | ACC |
---|---|---|---|---|---|---|---|---|
a | × | × | × | × | × | × | × | 0.810 0 |
b | × | √ | √ | √ | √ | √ | √ | 0.813 3 |
c | √ | × | √ | √ | √ | √ | √ | 0.814 8 |
d | √ | √ | × | √ | √ | √ | √ | 0.812 5 |
e | √ | √ | √ | × | √ | √ | √ | 0.812 3 |
f | √ | √ | √ | √ | × | √ | √ | 0.812 0 |
g | √ | √ | √ | √ | √ | × | √ | 0.812 5 |
h | √ | √ | √ | √ | √ | √ | × | 0.817 8 |
i | √ | √ | √ | √ | √ | √ | √ | 0.835 0 |
模型 | AUC | ACC | ||
---|---|---|---|---|
ASSIST | KDD | ASSIST | KDD | |
DKT | 0.773 | 0.764 | 0.761 | 0.774 |
DKVMN | 0.785 | 0.761 | 0.749 | 0.761 |
GKT | 0.794 | 0.759 | 0.793 | 0.772 |
LFKT | 0.807 | 0.769 | 0.785 | 0.801 |
GKT-FM | 0.849 | 0.831 | 0.835 | 0.824 |
GKT-FM-WF | 0.823 | 0.817 | 0.810 | 0.801 |
GKT-FM-WM | 0.809 | 0.811 | 0.817 | 0.803 |
Tab. 4 Experimental results of different models’ prediction performance comparison
模型 | AUC | ACC | ||
---|---|---|---|---|
ASSIST | KDD | ASSIST | KDD | |
DKT | 0.773 | 0.764 | 0.761 | 0.774 |
DKVMN | 0.785 | 0.761 | 0.749 | 0.761 |
GKT | 0.794 | 0.759 | 0.793 | 0.772 |
LFKT | 0.807 | 0.769 | 0.785 | 0.801 |
GKT-FM | 0.849 | 0.831 | 0.835 | 0.824 |
GKT-FM-WF | 0.823 | 0.817 | 0.810 | 0.801 |
GKT-FM-WM | 0.809 | 0.811 | 0.817 | 0.803 |
1 | 吴正洋,汤庸,刘海. 个性化学习推荐研究综述[J]. 计算机科学与探索, 2022, 16(1):21-40. 10.3778/j.issn.1673-9418.2105111 |
WU Z Y, TANG Y, LIU H. Survey of personalized learning recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 21-40. 10.3778/j.issn.1673-9418.2105111 | |
2 | 胡学钢,刘菲,卜晨阳. 教育大数据中认知跟踪模型研究进展[J]. 计算机研究与发展, 2020, 57(12):2523-2546. 10.7544/issn1000-1239.2020.20190767 |
HU X G, LIU F, BU C Y. Research advances on knowledge tracing models in educational big data[J]. Journal of Computer Research and Development, 2020, 57(12): 2523-2546. 10.7544/issn1000-1239.2020.20190767 | |
3 | 李宇帆,张会福,刘上力,等. 教育数据挖掘研究进展[J]. 计算机工程与应用, 2019, 55(14):15-23. |
LI Y F, ZHANG H F, LIU S L, et al. Research progress on educational data mining[J]. Computer Engineering and Applications, 2019, 55(14): 15-23. | |
4 | 陈恩红,刘淇,王士进,等. 面向智能教育的自适应学习关键技术与应用[J]. 智能系统学报, 2021, 16(5):886-898. 10.11992/tis.202105036 |
CHEN E H, LIU Q, WANG S J, et al. Key techniques and application of intelligent education oriented adaptive learning[J]. CAAI Transactions on Intelligent Systems, 2021, 16(5): 886-898. 10.11992/tis.202105036 | |
5 | 张暖,江波. 学习者知识追踪研究进展综述[J]. 计算机科学, 2021, 48(4):213-222. 10.11896/jsjkx.200600044 |
ZHANG N, JIANG B. Review progress of learner knowledge tracing[J]. Computer Science, 2021, 48(4): 213-222. 10.11896/jsjkx.200600044 | |
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 | YUDELSON M V, KOEDINGER K R, GORDON G J. Individualized Bayesian knowledge tracing models[C]// Proceedings of the 2013 International Conference on Artificial Intelligence in Education, LNCS 7926. Berlin: Springer, 2013: 171-180. |
8 | 黄诗雯,刘朝晖,罗凌云,等. 融合行为和遗忘因素的贝叶斯知识追踪模型研究[J]. 计算机应用研究, 2021, 38(7):1993-1997. |
HUANG S W, LIU Z H, LUO L Y, et al. Research on Bayesian knowledge tracking model integrating behavior and forgetting factors[J]. Application Research of Computers, 2021, 38(7): 1993-1997. | |
9 | PIECH C, BASSEN J, HUANG J. Deep knowledge tracing[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. Cambridge: MIT Press, 2015: 505-513. |
10 | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. 10.1162/neco.1997.9.8.1735 |
11 | 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, 2023, 43(2):343-348. 10.11772/j.issn.1001-9081.2022010024 |
SHAO X M, ZHANG M. Temporal convolutional knowledge tracing model with attention mechanism[J]. Journal of Computer Applications, 2023, 43(2):343-348. 10.11772/j.issn.1001-9081.2022010024 | |
12 | ZHANG J N, SHI X J, KING I, et al. Dynamic key-value memory network 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 |
13 | ABDELRAHMAN G, WANG Q. Knowledge tracing with sequential key-value memory networks[C]// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York: ACM, 2019: 175-184. 10.1145/3331184.3331195 |
14 | 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. 10.13328/j.cnki.jos.006185 |
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. 10.13328/j.cnki.jos.006185 | |
15 | MURRE J M J, DROS J. Replication and analysis of Ebbinghaus’ forgetting curve[J]. PLoS ONE, 2015, 10(7): No.e0120644. 10.1371/journal.pone.0120644 |
16 | EBBINGHAUS H. Memory: a contribution to experimental psychology[J]. Annals of Neurosciences, 2013, 20(4): 155-156. 10.5214/ans.0972.7531.200408 |
17 | NAKAGAWA H, IWASAWA Y, MATSUO Y. Graph-based knowledge tracing: modeling student proficiency using graph neural network[C]// Proceedings of the 2019 IEEE/WIC/ACM International Conference on Web Intelligence. Piscataway: IEEE, 2019: 156-163. 10.1145/3350546.3352513 |
18 | DAVID P. Educational psychology: a cognitive view[J]. American Educational Research Journal, 1969, 6(2): 287-290. 10.2307/1161899 |
19 | WANG Q Y, ZHENG S, FARAHAT A, et al. Multilayer perceptron for sparse functional data[C]// Proceedings of the 2019 International Joint Conference on Neural Networks. Piscataway: IEEE, 2019: 1-10. 10.1109/ijcnn.2019.8851700 |
20 | ASSISTments. ASSISTments2009 [DS/OL]. [2022-11-30]. . |
21 | ACM KDD. KDD Cup 2010: student performance evaluation[DS/OL]. [2022-11-30].. 10.1145/2517288.2517297 |
[1] | Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718. |
[2] | Xinrui LIN, Xiaofei WANG, Yan ZHU. Academic anomaly citation group detection based on local extended community detection [J]. Journal of Computer Applications, 2024, 44(6): 1855-1861. |
[3] | Yajuan ZHAO, Fanjun MENG, Xingjian XU. Review of online education learner knowledge tracing [J]. Journal of Computer Applications, 2024, 44(6): 1683-1698. |
[4] | Jie GUO, Jiayu LIN, Zuhong LIANG, Xiaobo LUO, Haitao SUN. Recommendation method based on knowledge‑awareness and cross-level contrastive learning [J]. Journal of Computer Applications, 2024, 44(4): 1121-1127. |
[5] | Dapeng XU, Xinmin HOU. Feature selection method for graph neural network based on network architecture design [J]. Journal of Computer Applications, 2024, 44(3): 663-670. |
[6] | Beijing ZHOU, Hairong WANG, Yimeng WANG, Lisi ZHANG, He MA. Recommendation method using knowledge graph embedding propagation [J]. Journal of Computer Applications, 2024, 44(10): 3252-3259. |
[7] | Linhao LI, Xiaoqian ZHANG, Yao DONG, Xu WANG, Yongfeng DONG. Knowledge tracing based on personalized learning and deep refinement [J]. Journal of Computer Applications, 2024, 44(10): 3039-3046. |
[8] | Runchao PAN, Qishan YU, Hongfei XIONG, Zhihui LIU. Collaborative recommendation algorithm based on deep graph neural network [J]. Journal of Computer Applications, 2023, 43(9): 2741-2746. |
[9] | Hongjun HENG, Dingcheng YANG. Knowledge enhanced aspect word interactive graph neural network [J]. Journal of Computer Applications, 2023, 43(8): 2412-2419. |
[10] | Kun ZHANG, Fengyu YANG, Fa ZHONG, Guangdong ZENG, Shijian ZHOU. Source code vulnerability detection based on hybrid code representation [J]. Journal of Computer Applications, 2023, 43(8): 2517-2526. |
[11] | Zifang XIA, Yaxin YU, Ziteng WANG, Jiaqi QIAO. Explainable recommendation mechanism by fusion collaborative knowledge graph and counterfactual inference [J]. Journal of Computer Applications, 2023, 43(7): 2001-2009. |
[12] | Kai ZHANG, Zhengchu QIN, Yue LIU, Xinyi QIN. Multi-learning behavior collaborated knowledge tracing model [J]. Journal of Computer Applications, 2023, 43(5): 1422-1429. |
[13] | Kejun JIN, Hongtao YU, Yiteng WU, Shaomei LI, Jianpeng ZHANG, Honghao ZHENG. Improved defense method for graph convolutional network based on singular value decomposition [J]. Journal of Computer Applications, 2023, 43(5): 1511-1517. |
[14] | Hao SUN, Jian CAO, Haisheng LI, Dianhui MAO. Session-based recommendation model based on enhanced capsule network [J]. Journal of Computer Applications, 2023, 43(4): 1043-1049. |
[15] | Lubao LI, Tian CHEN, Fuji REN, Beibei LUO. Bimodal emotion recognition method based on graph neural network and attention [J]. Journal of Computer Applications, 2023, 43(3): 700-705. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||