Personalized course generation and evolution based on genetic algorithms

Xiao-hong TAN; Rui-min SHEN; Yan WANG

doi:10.1631/jzus.C1200174

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Personalized course generation and evolution based on genetic algorithms

Computer & Automation | Updated：2022-12-09

- Personalized course generation and evolution based on genetic algorithms
- Personalized course generation and evolution based on genetic algorithms
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 12, Pages: 909-917(2012)
- Affiliations：
  
  1.Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
  2.E-learning Lab, Shanghai Jiao Tong University, Shanghai 200030, China
- Author bio：
  
  [ "Xiao-yong LI, E-mail: xhtan@sjtu.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.C1200174
  CLC： TP391.7
- Received：04 June 2012，
  
  Revised：2012-11-12，
  
  Published：2012-12
- Accepted：
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Xiao-hong TAN, Rui-min SHEN, Yan WANG. Personalized course generation and evolution based on genetic algorithms[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(12): 909-917.
DOI：

Xiao-hong TAN, Rui-min SHEN, Yan WANG. Personalized course generation and evolution based on genetic algorithms[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(12): 909-917. DOI： 10.1631/jzus.C1200174.

摘要

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.

关键词

Keywords

references

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