@inproceedings{a763a4b170d24afc80de73f4415da32f,
title = "An Analysis of Degree Curricula through Mining Student Records",
abstract = "Higher Education Institutions store a sizable amount of data, including student records and the structure of a degree curriculum. This paper focuses on the problem of identifying how closely students follow the recommended order of the courses in a degree curriculum, and to what extent their performance is affected by the order they actually adopt. It addresses this problem by applying techniques to mine frequent itemsets to student records. The paper illustrates the application of the techniques for a case study involving over 60,000 student records in two undergraduate degrees at a Brazilian University.",
keywords = "academic analytics, degree curriculum structure, frequent itemsets",
author = "Vinicius Gottin and Haydee Jimenez and Finamore, {Anna Carolina} and Casanova, {Marco A.} and Furtado, {Antonio L.} and Nunes, {Bernardo P.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017 ; Conference date: 03-07-2017 Through 07-07-2017",
year = "2017",
month = aug,
day = "3",
doi = "10.1109/ICALT.2017.54",
language = "English",
series = "Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "276--280",
editor = "Ronghuai Huang and Radu Vasiu and Kinshuk and Sampson, {Demetrios G} and Nian-Shing Chen and Maiga Chang",
booktitle = "Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017",
address = "United States",
}