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Journal of Shanghai University >

2023 , Vol. 29 >Issue 1: 129 - 139

DOI: https://doi.org/10.12066/j.issn.1007-2861.2342

Research Articles

Personalized learning path recommendation based on improved ant colony algorithm

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  • 1. College of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    2. Key Laboratory of Artificial Intelligence Application, State Administration of Radio and Television, Shanghai 201620, China
    3. College of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received date: 2021-09-13

  Online published: 2023-03-28

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Abstract

At present, most learning path recommendation fields are learning resource recommendation and the application rate of course knowledge graph at a low rate. Therefore, a method which combines knowledge mapping technology, deep knowledge tracking model, and ant colonies, is proposed, to improve the classification of ant colonies in the traditional ant colony algorithm. Initially, taking a course knowledge map as a foundation, deep knowledge tracking is applied to the classification of different levels of learners and combined with knowledge difficulty weights. The corresponding path planning with ant colony algorithm, classifies ants according to different learner categories. The shortest path in considering objective knowledge groups of different learning levels is also studied to make personalized efficient learning path recommendation. Finally, the validity of the proposed method is verified on the open dataset of ASSISTment.

Key words： knowledge graph; deep knowledge tracing; ant colony system; individualized learning

Cite this article

XIA Ruiling, LI Guoping, WANG Guozhong, TENG Guowei . Personalized learning path recommendation based on improved ant colony algorithm[J]. Journal of Shanghai University, 2023 , 29(1) : 129 -139 . DOI: 10.12066/j.issn.1007-2861.2342

References

[1] Yi B, Zhang D, Wang Y, et al. Research on personalized learning model under informatization environment[C]// 2017 International Symposium on Education Technology(ISET). 2017: 48-52.
[2] Shukhman A, Motyleva M, Belonovskaya I. Individual learning path modeling on the basis of generalized competencies system[C]// Global Engineering Education Conference. 2013: 1023-1026.
[3] 胥岩. 基于行为数据分析的学习者学习习惯挖掘及学习路径规划[D]. 哈尔滨: 哈尔滨工业大学, 2020.
[4] 师亚飞, 彭红超, 童名文. 基于学习画像的精准个性化学习路径生成性推荐策略研究[J]. 中国电化教育, 2019(5): 84-91.
[5] Intayoad W, Becker T, Temdee P. Social context-aware recommendation for personalized online learning[J]. Wireless Personal Communications, 2017, 97(1): 1-17.
[6] 任建军, 李柏岩, 赵银龙, 等. 基于思维导图的在线学习交互方法研究[J]. 智能计算机与应用, 2015, 5(1): 111-112.
[7] 王亮. 深度学习视角下基于多模态知识图谱的MOOC 课程重构[J]. 现代教育技术, 2018, 28(10): 100-106.
[8] Shi D, Wang T, Xing H, et al. A learning path recommendation model based on a multi-dimensional knowledge graph framework for e-learning[J]. Knowledge-Based Systems, 2020, 195(5): 105618.
[9] Berg B, Colin T, Janssen J, et al. Swarm-based sequencing recommendations in E-learning[J]. International Journal of Computer Science and Applications, 2006, 3(3): 1-11.
[10] Yao J Y, Wu C. An attribute-based ant colony system for adaptive learning object recommendation[J]. Expert Systems with Applications, 2009, 36(2): 3034-3047.
[11] 孔维梁, 韩淑云, 张昭理. 人工智能支持下子适应学习路径构建[J]. 现代远程教育研究, 2020, 32(3): 94-102.
[12] 申云凤. 基于多重智能算法的个性化学习路径推荐模型[J]. 中国电化教育, 2019(11): 66-72.
[13] 肖仰华, 徐波, 林欣, 等. 知识图谱概念与技术[M]. 北京: 电子工业出版社, 2020: 16-17.
[14] 秦川, 祝恒书, 庄福振, 等. 基于知识图谱的推荐系统研究综述[J]. 中国科学: 信息科学, 2020(7): 937-956.
[15] 朱佳, 张丽君, 梁婉莹. 数据驱动下的个性化自适应系统研究综述[J]. 华南师范大学学报(自然科学版), 2020, 52(4): 17-25.
[16] Chris P, Jonathan B, Jonathan H, et al. Deep knowledge tracing[C]// NIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015: 505-513.
[17] 马骁睿, 徐圆, 朱群雄. 一种结合深度知识追踪的个性化习题推荐方法[J]. 小型微型计算机系统, 2020, 5(5): 990-995.
[18] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 1996, 26(1): 29-41.
[19] 毕行, 徐炜民. 基于特定群体兴趣的混合个性化推荐算法[J]. 上海大学学报(自然科学版), 2010, 16(3): 318-322.
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