Analyzing Convergence in e-Learning Resource Filtering Based on ACO Techniques: A Case Study With Telecommunication Engineering Students
Mario Muñoz-Organero, Gustavo Ramírez-González, Pedro J. Muñoz‐Merino, Carlos Delgado Kloos · IEEE Transactions on Education · 2009
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.