@inproceedings{0382088bf9b9463e82a54f1ac3ec36ad,
title = "Merging learning objects automatically",
abstract = "Several approaches have been proposed to recommend the most suitable Learning Object (LO) according to student's profile. It gauges the similarity between the student's profile and metadata of stored LOs. A recovery function is used in most of these alternatives; the highest value means a high degree of matching with the user's needs. Usually the user has the option to pick out a LO of the ranked list to look through it. When recovery function does not find a LO, which fulfills user's requirements or it was ranked too low to be considered {"}suitable{"}, it is required to build a more appropriate one. The combination of two or more LOs adds new content to the new LO so it might be closer to the user's needs. This paper shows an approach to do so which is incremental because let us gather knowledge by means of merging LOs.",
keywords = "E-learning, Learning Object, Merging",
author = "Aldo Ram{\'i}rez-Arellano and Elizabeth Acosta-Gonzaga",
year = "2012",
language = "Ingl{\'e}s",
series = "ICSIT 2012 - 3rd International Conference on Society and Information Technologies, Proceedings",
publisher = "International Institute of Informatics and Systemics, IIIS",
pages = "160--165",
editor = "Hsing-Wei Chu and Harald Wahl and Nagib Callaos and Christian Kaufmann and Friedrich Welsch",
booktitle = "ICSIT 2012 - 3rd International Conference on Society and Information Technologies, Proceedings",
note = "3rd International Conference on Society and Information Technologies, ICSIT 2012 ; Conference date: 25-03-2012 Through 28-03-2013",
}