Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICALT.2017.54 | 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT) |
Keywords | Field | DocType |
degree curriculum structure,academic analytics,frequent itemsets | Data science,Informatics,World Wide Web,Computer science,Visualization,Curriculum,Higher education | Conference |
ISSN | ISBN | Citations |
2161-3761 | 978-1-5386-3871-2 | 1 |
PageRank | References | Authors |
0.36 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinicius Gottin | 1 | 1 | 1.71 |
| Haydee Jimenez | 2 | 1 | 0.70 |
| Anna Carolina Finamore | 3 | 1 | 0.36 |
| Marco A. Casanova | 4 | 1007 | 979.09 |
| Antonio L. Furtado | 5 | 704 | 917.22 |
| Bernardo Pereira Nunes | 6 | 185 | 30.96 |