Predictive Models for Undergraduate Student Retention Using Machine Learning Algorithms
| Autor: | Manohar Mareboyana, Ji-Wu Jia |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Signal processing
Artificial neural network business.industry Computer science Decision tree Machine learning computer.software_genre Regression Support vector machine Statistical classification Software ComputingMilieux_COMPUTERSANDEDUCATION Undergraduate student Artificial intelligence business Algorithm computer |
| Zdroj: | Transactions on Engineering Technologies ISBN: 9789401791144 |
| Popis: | In this paper, we have presented some results of undergraduate student retention using machine learning and wavelet decomposition algorithms for classifying the student data. We have also made some improvements to the classification algorithms such as Decision tree, Support Vector Machines (SVM), and neural networks supported by Weka software toolkit. The experiments revealed that the main factors that influence student retention in the Historically Black Colleges and Universities (HBCU) are the cumulative grade point average (GPA) and total credit hours (TCH) taken. The target functions derived from the bare minimum decision tree and SVM algorithms were further revised to create a two-layer neural network and a regression to predict the retention. These new models improved the classification accuracy. Furthermore, we utilized wavelet decomposition and achieved better results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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