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.
%0 Journal Article
%1 5288562
%A Munoz-Organero, Mario
%A Ramirez-Gonzalez, Gustavo
%A Muñoz-Merino, Pedro
%A Delgado-Kloos, Carlos
%D 2010
%J Education, IEEE Transactions on
%K "education ACO Ant Colony IOT NFC RFID internet learning mobile myown of physical space telecommunication things ubiquitous
%N 4
%P 542 -546
%R 10.1109/TE.2009.2032168
%T Analyzing Convergence in e-Learning Resource Filtering Based on ACO Techniques: A Case Study With Telecommunication Engineering Students
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5288562
%V 53
%X 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.
@article{5288562,
abstract = {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.},
added-at = {2012-11-17T20:10:38.000+0100},
author = {Munoz-Organero, Mario and Ramirez-Gonzalez, Gustavo and Muñoz-Merino, Pedro and Delgado-Kloos, Carlos},
biburl = {https://www.bibsonomy.org/bibtex/27a0729188fb22a9dfa798332e8fb4746/gusramir},
doi = {10.1109/TE.2009.2032168},
interhash = {e12d531af092fc10a71f34dfbabe83db},
intrahash = {7a0729188fb22a9dfa798332e8fb4746},
issn = {0018-9359},
journal = {Education, IEEE Transactions on},
keywords = {"education ACO Ant Colony IOT NFC RFID internet learning mobile myown of physical space telecommunication things ubiquitous},
month = {nov. },
number = 4,
pages = {542 -546},
timestamp = {2012-11-19T23:31:42.000+0100},
title = {Analyzing Convergence in e-Learning Resource Filtering Based on ACO Techniques: A Case Study With Telecommunication Engineering Students},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5288562},
volume = 53,
year = 2010
}