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1109. Classification of Students Based on the Risk of Dropping out of University Degrees
Invited abstract in session MA-47: MCDA applications in Engineering and Management 1, stream Multiple Criteria Decision Analysis.
Monday, 8:30-10:00Room: 50 (building: 324)
Authors (first author is the speaker)
| 1. | Marina Segura
|
| DEPARTMENT OF FINANCIAL AND ACTUARIAL ECONOMICS AND STATISTICS, Universidad Complutense de Madrid | |
| 2. | ANA MARIA SANCHEZ SANCHEZ
|
| EconomÃa Financiera y Actuarial y EstadÃstica, Universidad Complutense de Madrid | |
| 3. | Adolfo Hernandez
|
| Financial & Actuarial Economics & Statistics, Complutense University Madrid |
Abstract
Dropping out of university degrees is a problem with a considerable negative impact not only academically but also economically. The objective of this work is to design a multicriteria model that allows classifying students based on the risk of dropping out of the degree they are pursuing after the first year. Ordered categories of higher to lower risk are generated using the Global Local Net Flow Sorting (GLNF Sorting) algorithm, and the quality of student assignments to a risk group is evaluated using the Silhouette for Sorting (SILS) quality indicator. To quantify the risk of dropping out, criteria both before access to higher education and related to the context and results of students in the first semester have been used. The model is validated with real data from students in various degrees at Complutense University of Madrid. The obtained classification is compared with those obtained using traditional classification techniques and machine learning.
Keywords
- Multi-Objective Decision Making
- Risk Analysis and Management
- Machine Learning
Status: accepted
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