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

计算机与自动化 | Updated：2022-12-09

- Personalized course generation and evolution based on genetic algorithms
- Personalized course generation and evolution based on genetic algorithms
- 信息与电子工程前沿（英文） 2012年13卷第12期页码：909-917
- 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：
  
  Project supported by the National Natural Science Foundation of China (No. 61071154) and the project FP7 "Responsive Open Learn ing Environments" of European Union
- DOI：10.1631/jzus.C1200174
  中图分类号： TP391.7
- 收稿：2012-06-04，
  
  修回：2012-11-12，
  
  纸质出版：2012-12
- Accepted：
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Personalized course generation and evolution based on genetic algorithms[J]. 信息与电子工程前沿（英文）, 2012,13(12):909-917.

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.
Personalized course generation and evolution based on genetic algorithms[J]. 信息与电子工程前沿（英文）, 2012,13(12):909-917. DOI： 10.1631/jzus.C1200174.

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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