Analyzing Convergence in e-Learning Resource Filtering Based on ACO Techniques: A Case Study With Telecommunication Engineering Students Articles uri icon

publication date

  • November 2010

start page

  • 542

end page

  • 546


  • 4


  • 53

International Standard Serial Number (ISSN)

  • 0018-9359

Electronic International Standard Serial Number (EISSN)

  • 1557-9638


  • The use of swarm intelligence techniques in e-learning scenarios provides a way to combine simple interactions of individual students to solve a more complex problem. After getting some data from the
    interactions of the first students with a central system, the use of
    these techniques converges to a solution that the rest of the students
    can successfully use. This paper uses a case study to analyze how fast
    swarm intelligence techniques converge when applied to solve the problem
    of e-learning resource filtering. Some modifications to traditional ant
    colony optimization (ACO) algorithms based on student filtering are
    also introduced in order to improve convergence.