@inproceedings{c31510a9e5f84849b9be20a649a7b15c,
title = "On the predictive power of university curricula",
abstract = "In this study we analyzed the curricula of 65 university students to investigate the impact of activities progression on student performances. Clustering curricula based on activity order and type we discovered a significant incidence on performance, validating the predictive power of curricula. Nevertheless, we discovered that the characterization of clusters is mainly due to non mandatory activities, selected by a student to personalize his curriculum, while activity order is very less relevant. This observation rejects the idea that activities progression has impact on performance, resulting rather as a consequence of student choices.",
keywords = "educational data mining, experimental study, students performances, university curricula",
author = "Antonia Azzini and Paolo Ceravolo and Nello Scarabottolo and Ernesto Damiani",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Global Engineering Education Conference, EDUCON 2016 ; Conference date: 10-04-2016 Through 13-04-2016",
year = "2016",
month = may,
day = "19",
doi = "10.1109/EDUCON.2016.7474663",
language = "British English",
series = "IEEE Global Engineering Education Conference, EDUCON",
publisher = "IEEE Computer Society",
pages = "929--932",
booktitle = "Proceedings of 2016 IEEE Global Engineering Education Conference, EDUCON 2016",
address = "United States",
}