A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

Ting Yi Chang, Yan Ru Ke

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality (called GA) in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The forcing legality operation not only reduces the search space size and increases search efficiency but also is more explicit in finding the best e-course composition in a legal solution space. In serial experiments, the forcing legality operation is applied in Chu et al.'s the particle swarm optimization (called PSO) and Dheeban et al.'s the improved particle swarm optimization (called RPSO) to show the forcing legality can speed up the computational time and reduce the computational complexity of algorithm. Furthermore, GA regardless of the number of students or the number of materials in the database, to compose a personalized e-course within a limited time is much more efficient and accurate than PSO and RPSO. For the experiment increasing the number of students to 1200, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, stability, and execution time are above 96%, 79%, 90%, and 10%, respectively. For the experiment increasing the number of materials to 500 and the execution time set to the shortest execution time of RPSO, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, and stability are above 97%, 51%, and 80%, respectively. Therefore, GA is able to enhance the quality of personalized e-course compositions in adaptive learning environments.

Original languageEnglish
Pages (from-to)533-542
Number of pages10
JournalJournal of Network and Computer Applications
Volume36
Issue number1
DOIs
Publication statusPublished - 2013 Jan 1

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Learning systems
Genetic algorithms
Chemical analysis
Particle swarm optimization (PSO)
Students
Experiments
Computational complexity

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Cite this

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title = "A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system",
abstract = "This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality (called GA) in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The forcing legality operation not only reduces the search space size and increases search efficiency but also is more explicit in finding the best e-course composition in a legal solution space. In serial experiments, the forcing legality operation is applied in Chu et al.'s the particle swarm optimization (called PSO) and Dheeban et al.'s the improved particle swarm optimization (called RPSO) to show the forcing legality can speed up the computational time and reduce the computational complexity of algorithm. Furthermore, GA regardless of the number of students or the number of materials in the database, to compose a personalized e-course within a limited time is much more efficient and accurate than PSO and RPSO. For the experiment increasing the number of students to 1200, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, stability, and execution time are above 96{\%}, 79{\%}, 90{\%}, and 10{\%}, respectively. For the experiment increasing the number of materials to 500 and the execution time set to the shortest execution time of RPSO, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, and stability are above 97{\%}, 51{\%}, and 80{\%}, respectively. Therefore, GA is able to enhance the quality of personalized e-course compositions in adaptive learning environments.",
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