Learning resource recommendation method based on particle swarm optimization algorithm

doi:10.11772/j.issn.1001-9081.2014.05.1350

Abstract

Abstract:

Due to individual differences in the ability, learning objectives and learning time of the learners,a learning resource recommendation method based on Particle Swarm Optimization (PSO) algorithm was proposed to provide the learner with a personalized digital curriculum. The knowledge structure chart was constructed applying concept map and knowledge structure. The learning objectives and the ability of the learner were analyzed based on item response theory. The PSO algorithm was adopted to select the appropriate e-learning materials from a mass of candidate materials and the adaptive e-course was composed and recommended. The learning time limit was considered while initializing particles, and some unnecessary particles were filtered out to improve the efficiency of the algorithm. When determining the optimal solution, the Sigmoid function was used to fix the particle update velocity to ensure it effective. The experimental results show that with the increase of the number of iterations, the difference between the recommended content and the learner goal is 0. The difference between the recommended curriculum and the learner ability is 0.6, and the overall difference is 0.25.It shows that the convergency and efficiency of the proposed method and the selected learning materials can meet the demands of different learners.

摘要：

针对学习者的能力、学习目标、学习时间的个别差异，提出以粒子群优化（PSO）算法为基础的学习资源推荐方法，提供每位学习者个性化的数字课程。综合概念图和知识结构相关理论构建知识点网络结构图，运用项目反应理论（IRT）分析不同学习者的学习目标和能力程度，再应用PSO算法从多样性的学习资源中挑选学习内容，形成个性化的课程推荐给学生。初始化粒子时考虑学习者的学习时间上下限，过滤掉一些不必要的粒子来提高算法效率，在确定最优解位置时，使用Sigmoid函数修正粒子更新速度，保证其在有效范围内。实验结果表明，随着迭代次数增加，所推荐的内容与学习者预定目标差异为0，挑选出的课程与学习者能力差异为0.6，整体差异为0.25，说明所使用的方法具有较好的收敛性，推荐的学习资源能够满足学习者要求。

CLC Number:

TP18

YANG Chao. Learning resource recommendation method based on particle swarm optimization algorithm[J]. Journal of Computer Applications, 2014, 34(5): 1350-1353.

杨超. 基于粒子群优化算法的学习资源推荐方法[J]. 计算机应用, 2014, 34(5): 1350-1353.

References

［1］CHEN S, ZHANG J. Research hotspot and development trend in intelligent teaching system ［J］. E-education Research, 2007,28(10):41-46.(陈仕品,张剑平.智能教学系统的研究热点与发展趋势［J］.电化教育研究,2007,28(10):41-46.)
［2］YANG L, LIU K, YAN Z.Research on personalized recommendation of learning resources for virtual learning community［J］. E-education Research, 2010,31(4):67-71.(杨丽娜,刘科成,颜志军.面向虚拟学习社区的学习资源个性化推荐研究［J］.电化教育研究, 2010,31(4):67-71.)
［3］XU G, ZENG W, HUANG C.Research on intelligent tutoring system ［J］. Application Research of Computers, 2009,26(11):4019-4022.(许高攀,曾文华,黄翠兰.智能教学系统研究综述［J］.计算机应用研究, 2009,26(11):4019-4022.)
［4］MANOUSELIS N, VUORIKARI R, van ASSCHE F. Collaborative recommendation of e-learning resources: an experimental investigation ［J］. Journal of Computer Assisted Learning, 2010,26(4):227-242.
［5］TANG T, McCALLA G. Smart recommendation for an evolving e-learning system: a simulation-based study ［C］// Proceedings of the 17th Conference of the Canadian Society for Computational Studies of Intelligence. Berlin: Springer, 2004:439-443.
［6］GORDON M, FAN W G, RAFAELI S, et al. The architecture of commKnowledge: combining link structure and user actions to support an online community ［J］. International Journal of Electronic Business, 2003,1(1):69-82.
［7］DING L, WU C. Application of data mining in personalized services for distance education［J］. E-education Research, 2002,23(9): 43-46.(丁琳,吴长永.数据挖掘在远程教育个性化服务中的应用［J］.电化教育研究,2002,23(9):43-46.)
［8］WANG Y. Research on e-learning system to support personalized learning［J］. China Educational Technology, 2008,29(3):102-107.(王艳芳.支持个性化学习的E-learning系统研究［J］.中国电化教育,2008,29(3):102-107.)
［9］HE L, GAO L. Personalized recommendation of learning resources in the network environment ［J］. Distance Education in China, 2009,29(2):67-69.(何玲,高琳琦.网络环境中学习资料的个性化推荐方法［J］.中国远程教育,2009,29(2):67-69.)
［10］YANG L, LIU K, YAN Z. Research on personalized recommendation of learning resources based on case reasoning Agent under the cooperation framework ［J］. China Educational Technology, 2009,30(12):105-109.(杨丽娜,刘科成,颜志军.案例推理Agent合作框架下的个性化学习资源推荐研究［J］.中国电化教育,2009,30(12): 105-109.)
［11］ZHAO W, YU Y, ZHANG S. Open e-learning solutions for personalized recommendation service: an idea of exploration facing to digital learning service for lifelong learning mode ［J］. China Educational Technology, 2010,30(11):110-116.(赵蔚,余延冬,张赛男.开放式e-Learning解决方案个性化推荐服务:一种面向终身学习的数字化学习服务模式的探索思路［J］.中国电化教育,2010,30(11):110-116.)
［12］WANG L. Research on some key technologies of educational resource recommendation service ［D］. Changchun: Jilin University, 2013.(王龙.教育资源推荐服务中若干关键技术的研究［D］.长春:吉林大学,2013.)
［13］EBERHART R, KENNED Y. A new optimizer using particle swarm theory ［C］// MHS '95: Proceedings of the 6th International Symposium on Micro Machine and Human Science. Piscataway: IEEE Press, 1995:39-43.
［14］KENNEDY J, EBERHART R. Particle swarm optimization ［C］// Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway: IEEE Press, 1995,4:1942-1948.
［15］YAN F, AN X. Research on the tactic of intelligent test-paper generation based on PSO algorithm ［J］. Journal of North University of China: Natural Science Edition, 2008,29(4):333-337.(阎峰,安晓东.基于粒子群优化算法的智能抽题策略研究［J］.中北大学学报:自然科学版,2008,29(4):333-337.)
［16］YU Q, YANG Z, LEI J, et al. Applied binary particle swarm optimize algorithm basedon probability to test-sheet composition ［J］. Computer Simulation, 2012,29(9):387-391.(于庆梅,杨争争,雷景生,等.概率表示的二进制粒子群算法在组卷中的应用［J］.计算机仿真,2012,29(9):387-391.)
［17］CHEN Q, LING P, XIAO Y. Control method for learning path optimization problem based on improved particle swarm optimization ［J］. Computer Engineering, 2008,34(4):190-195.(陈其晖,凌培亮,萧蕴诗.基于改进微粒群优化的学习路径优化控制方法［J］.计算机工程,2008,34(4):190-195.)
［18］SUN Z, GU Q. Intelligent matching algorithm of concept maps to support personalized learning ［J］. Computer Engineering and Design, 2012,33(10):3954-3958.(孙珠婷, 顾倩颐.概念图智能匹配机制用于支持个性化网络学习［J］.计算机工程与设计,2012,33(10):3954-3958.)
［19］LI Y. Application of item response theory in constructing the standardized examination question bank system ［J］. Journal of Shaoyang University: Social Science Edition, 2012,11(5):91-95.(李映红.项目反应理论在构建标准化考试题库系统中的应用［J］. 邵阳学院学报:社会科学版,2012,11(5):91-95.)

[1]	LYU Le, ZHANG Bohan, JING Junchang, LIU Dong. Multiple target nodes hiding method based on permanence [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Federated class-incremental learning method with multi-head self-attention for label semantic embedding [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	Jianli DING, Hui HUANG, Weidong CAO. Dynamic monitoring method of flight chain operation status [J]. Journal of Computer Applications, 2024, 44(12): 3941-3948.
[5]	Jingxin LIU, Wenjing HUANG, Liangsheng XU, Chong HUANG, Jiansheng WU. Unsupervised feature selection model with dictionary learning and sample correlation preservation [J]. Journal of Computer Applications, 2024, 44(12): 3766-3775.
[6]	Yifei SONG, Yi LIU. Fast adversarial training method based on data augmentation and label noise [J]. Journal of Computer Applications, 2024, 44(12): 3798-3807.
[7]	Yanran SHEN, Xin WEN, Jinhao ZHANG, Shuai ZHANG, Rui CAO, Baolu GAO. fMRI brain age prediction model with lightweight multi-scale convolutional network [J]. Journal of Computer Applications, 2024, 44(12): 3949-3957.
[8]	Zucuan ZHANG, Xuebin CHEN, Rui GAO, Yuanhuai ZOU. Federated learning client selection method based on label classification [J]. Journal of Computer Applications, 2024, 44(12): 3759-3765.
[9]	. Equivariant graph neural network-based simulator for lagrangian particle flow [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Multi-scale Information Fusion Time Series Long-term Forecasting Model Based on Neural Network [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Multi-target detection algorithm for traffic intersection images based on YOLOv9 [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	. Personalized learning recommendation in collaboration with knowledge graph and large language model [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	. Review of interpretable deep knowledge tracing methods [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Multi-scale Decorrelation Graph Convolutional Network [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Object detection uncertainty measurement scheme based on guide to the expression of uncertainty in measurement [J]. Journal of Computer Applications, 0, (): 0-0.