Personalized course generation and evolution based on genetic algorithms 被引量：2

Personalized course generation and evolution based on genetic algorithms

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摘要 Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance. Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.

作者 Xiao-hong TAN Rui-min SHEN Yan WANG

机构地区 Department of Computer Science and Engineering E-learning Lab

出处《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2012年第12期909-917,共9页 浙江大学学报C辑（计算机与电子（英文版）

基金 Project supported by the National Natural Science Foundation of China (No. 61071154) the project FP7 "Responsive Open Learning Environments" of European Union

关键词 Genetic algorithm Course generation Course evolution Personalized learning Domain ontology

分类号 TP311.12 [自动化与计算机技术—计算机软件与理论] O242.23 [理学—计算数学]

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