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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (12): 909-917    DOI: 10.1631/jzus.C1200174
    
Personalized course generation and evolution based on genetic algorithms
Xiao-hong Tan, Rui-min Shen, Yan Wang
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; E-learning Lab, Shanghai Jiao Tong University, Shanghai 200030, China
Personalized course generation and evolution based on genetic algorithms
Xiao-hong Tan, Rui-min Shen, Yan Wang
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; E-learning Lab, Shanghai Jiao Tong University, Shanghai 200030, China
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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.
关键词： Genetic algorithm;  Course generation;  Course evolution;  Personalized learning;  Domain ontology    
Abstract: 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.
Key words: Genetic algorithm    Course generation    Course evolution    Personalized learning    Domain ontology
收稿日期: 2012-06-04 出版日期: 2012-12-09
CLC:  TP391.7  
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Xiao-hong Tan
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Xiao-hong Tan, Rui-min Shen, Yan Wang. Personalized course generation and evolution based on genetic algorithms. Front. Inform. Technol. Electron. Eng., 2012, 13(12): 909-917.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200174    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I12/909

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