@inproceedings{4f667ca09c5b4137ad26b1e4d99b4ec1,
title = "A case study: Data mining applied to student enrollment",
abstract = "One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students' past performance. The objective is to improve students' performance in general, by reducing the rate of misguided enrollment decisions.",
author = "C{\'e}sar Vialardi and Jorge Chue and Alfredo Barrientos and Daniel Victoria and Jhonny Estrella and Peche, \{Juan Pablo\} and {\'A}lvaro Ortigosa",
year = "2010",
language = "Ingl{\'e}s",
isbn = "9780615375298",
series = "Educational Data Mining 2010 - 3rd International Conference on Educational Data Mining",
publisher = "International Educational Data Mining Society",
pages = "333--334",
booktitle = "Educational Data Mining 2010 - 3rd International Conference on Educational Data Mining",
note = "3rd International Conference on Educational Data Mining, EDM 2010 ; Conference date: 11-06-2010 Through 13-06-2010",
}