Learning object recommendation for teahcers creating lesson plans

LEANDRO CASTRO; SILVANA ACIAR; ELISEO REATEGUI

Congreso; XX Congreso Argentino de Ciencias de la Computación (Buenos Aires, 2014); 2014

E-learning is one of the most popular and beneficial uses of the Internet today. However, it presents a problem known as the "information overload". Current systems of e-learning stored too many contents: on various topics, for different ages, etc..; therefore users often are not sure how to find what they are needing between all the contents available. And this problem is even greater when considering that most current e-learning systems have simple search engines as tools to find resources. In this paper a new algorithm that incorporates recommendations of learning objects from a repository on an e-learning system is presented. At the end, a case study is performed to evaluate the proposal. Techniques of artificial intelligence are used to filter and customize information and recommended items.