引用本文:李浩君,张鹏威,张征,等.基于多维信息特征映射模型的在线学习路径优化方法[J].控制与决策,2019,34(6):1132-1140
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基于多维信息特征映射模型的在线学习路径优化方法
李浩君1, 张鹏威1, 张征1, 王万良2
(1. 浙江工业大学教育科学与技术学院,杭州310023;2. 浙江工业大学计算机科学与技术学院,杭州310023)
摘要:
针对目前在线学习路径优化方法存在学习路径与学习者匹配度不高的问题,首先构建在线学习路径的多维信息特征映射模型(MIFMM),该模型根据学习者与学习资源的多维信息特征建立,融合了kolb学习风格和学习资源类型信息;然后设计双映射二进制粒子群优化算法(DMBPSO),DMBPSO算法根据进化因子ef将学习路径推荐过程分为收敛和跳出局部最优两种进化状态,采用与进化状态特征相匹配的映射函数选择策略,并对惯性权重进行动态非线性调整,提高学习路径推荐性能;接着将MIFMM模型与DMBPSO算法相融合提出基于多维信息特征映射模型的在线学习路径优化方法(MIFMM-POA);最后将MIFMM-POA方法与其他4种粒子群算法为核心的学习路径优化方法相比较,从寻优精度、寻优过程与寻优时间3个角度进行分析,实验表明MIFMM-POA方法是优化学习路径的有效方法.
关键词:  在线学习路径优化  映射模型  多维信息特征  二进制粒子群  进化状态  kolb学习风格
DOI:10.13195/j.kzyjc.2017.1579
分类号:TP18
基金项目:
Method of online learning path optimization based on multi-dimensional information feature mapping model
LI Hao-jun1,ZHANG Peng-wei1, ZHANG Zheng1,WANG Wan-liang2
(1. College of Education Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China;2. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
Abstract:
For the problem that online learning path optimization methods don't have a high degree of matching between learning path and learners, this paper firstly constructs a multi-dimensional information feature mapping model(MIFMM) of online learning path, which is based on the multi-dimensional information characteristics of learners and learning resources, and integrates kolb learning style and learning resource type information. Then, a dual mapping binary particle swarm optimization(DMBPSO) algorithm is designed. According to the evolution factor ef, the DMBPSO algorithm divides the learning path recommendation process into two evolutionary states: convergent and out of local optimism, adopts a mapping function selection strategy which matches the evolutionary state features, and dynamically adjusts the inertia weight to improve the learning path recommendation performance. Futhermore, this paper combines the MIFMM with the DMBPSO algorithm to propose an online learning path optimization method based on the MIFMM model (MIFMM-POA). Finally, the MIFMM-POA method is compared with the learning path optimization methods based on the other four particle swarm algorithms, and the analysis is carried out from the three perspectives of optimization accuracy, optimization process and optimization time. The experimental results show that the MIFMM-POA method is an effective method to optimize the learning path.
Key words:  online learning path optimization  mapping model  multi-dimensional information feature  binary particle swarm optimization  evolution state  kolb learning style

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