基于改进蚁群算法的个性化学习路径推荐

doi:10.12066/j.issn.1007-2861.2342

上海大学学报(自然科学版) ›› 2023, Vol. 29 ›› Issue (1): 129-139.doi: 10.12066/j.issn.1007-2861.2342

基于改进蚁群算法的个性化学习路径推荐

夏瑞玲¹, 李国平², 王国中², 滕国伟³

1.上海工程技术大学电子电气工程学院, 上海 201620
2.人工智能应用国家广播电视总局重点实验室, 上海 201620
3.上海大学通信与信息工程学院, 上海 200444

收稿日期:2021-09-13 出版日期:2023-02-28 发布日期:2023-03-28
基金资助:
国家重点研发计划资助项目(2019YFB1802700)

Personalized learning path recommendation based on improved ant colony algorithm

XIA Ruiling¹, LI Guoping², WANG Guozhong², TENG Guowei³

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:2021-09-13 Online:2023-02-28 Published:2023-03-28

摘要/Abstract

摘要：

目前已有的学习路径推荐领域多为学习资源推荐, 而课程知识图谱应用率较低, 与蚁群算法的结合普遍缺乏对学习者知识水平的精确建模. 因此, 提出将知识图谱技术、深度知识追踪模型以及蚁群算法三者相结合, 同时分类蚁群改进传统的蚁群算法: 首先, 抽象出课程知识点图谱作为路径基础, 将深度知识追踪应用于不同水平学习者的分类, 并与知识点难度权重相结合; 然后, 采用蚁群算法进行相应的路径规划, 将蚁群按照不同的学习者类别进行划分, 在保障相对最短学习路径的同时考虑不同学习群体客观知识水平情况, 从而得到个性化的高效率学习路径推荐; 最后, 在ASSISTment 数据集上验证了本方法的有效性.

关键词: 知识图谱, 深度知识追踪, 蚁群算法, 个性化学习

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

中图分类号:

TP 391

夏瑞玲, 李国平, 王国中, 滕国伟. 基于改进蚁群算法的个性化学习路径推荐[J]. 上海大学学报(自然科学版), 2023, 29(1): 129-139.

XIA Ruiling, LI Guoping, WANG Guozhong, TENG Guowei. Personalized learning path recommendation based on improved ant colony algorithm[J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 129-139.

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参考文献 19

[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.

基于改进蚁群算法的个性化学习路径推荐

Personalized learning path recommendation based on improved ant colony algorithm

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