Predicting students' graduation outcomes through support vector machines
| Autor: | Joseph O'Brien, Michael Ben-Avie, Yulei Pang, Nicolas Judd |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Higher education
Recall business.industry Computer science 05 social sciences 050301 education Statistical model 02 engineering and technology Machine learning computer.software_genre Support vector machine Kernel (linear algebra) ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Set (psychology) Construct (philosophy) 0503 education computer Graduation |
| Zdroj: | FIE |
| DOI: | 10.1109/fie.2017.8190666 |
| Popis: | Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but also on the level of the individual student. In this paper we propose an ensemble support vector machines based model for predicting students' graduation. Up to about 100 features, including a set of psychological-educational factors, were employed to construct the predicting model. We evaluated the proposed model using data taken from a state university's longitudinal, cohort data sets from the incoming classes of students from 2011–2012 (n=350). The experimental results demonstrated the effectiveness of the model, with considerable accuracy, precision, and recall. This paper presents the results of analysis that were conducted in order to gauge the predictive capability of a machine learning algorithm to predict on-time graduation that took into consideration students' learning and development. |
| Databáze: | OpenAIRE |
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