Name
Abstract | ||
|---|---|---|
Since the nineties, Data Mining (DM) has shown to be a privileged partner in business by providing the organizations a rich set of tools to extract novel and useful knowledge from databases. In this paper, a DM application in the highly competitive market of educational services is presented. A model was built by combining a set of classifiers into a committee machine to predict the likelihood that a student who completed his/her second term will remain in the institution until graduation.The model was applied to undergraduate student records in a higher education institution in Brasília, the capital of Brazil, and has shown to be predictive for evasion in a high accuracy. The unbiased selection of students with elevated evasion risk affords the institution the opportunity to devise mitigation strategies and preempt a decision by the student to evade. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24443-8_16 | MEDI |
Keywords | Field | DocType |
data mining,dm application,elevated evasion risk,rich set,educational service,committee machine,undergraduate student record,higher education institution,competitive market,predicting evasion candidate,high accuracy | Data science,Committee machine,Computer science,Perfect competition,Higher education | Conference |
Volume | ISSN | Citations |
6918 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Remis Balaniuk | 1 | 94 | 15.46 |
| Hercules Antonio do Prado | 2 | 0 | 0.34 |
| Renato da Veiga Guadagnin | 3 | 0 | 0.68 |
| Edilson Ferneda | 4 | 18 | 14.80 |
| Paulo Roberto Cobbe | 5 | 1 | 1.04 |