基于粒子群优化算法的学习资源推荐方法

doi:10.11772/j.issn.1001-9081.2014.05.1350

计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1350-1353.DOI: 10.11772/j.issn.1001-9081.2014.05.1350

基于粒子群优化算法的学习资源推荐方法

杨超

合肥学院基础教学与实验中心，合肥 230601

收稿日期:2013-11-06 修回日期:2013-12-17 出版日期:2014-05-01 发布日期:2014-05-30
通讯作者: 杨超
作者简介:杨超（1979-），男，安徽六安人，讲师，硕士，主要研究方向：计算机辅助教学、算法
基金资助:
安徽高校省级自然科学项目

Learning resource recommendation method based on particle swarm optimization algorithm

YANG Chao

Basic Teaching and Experimental Department, Hefei University, Hefei Anhui 230601, China

Received:2013-11-06 Revised:2013-12-17 Online:2014-05-01 Published:2014-05-30
Contact: YANG Chao

摘要/Abstract

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。

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.

中图分类号:

TP18

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.

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

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基于粒子群优化算法的学习资源推荐方法

Learning resource recommendation method based on particle swarm optimization algorithm

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