An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning
Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student pe...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-05-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/12/5/728 |
id |
doaj-00a00559da6c4a30976069588b5f2d69 |
---|---|
record_format |
Article |
spelling |
doaj-00a00559da6c4a30976069588b5f2d692020-11-25T03:35:27ZengMDPI AGSymmetry2073-89942020-05-011272872810.3390/sym12050728An Ensemble Prediction Model for Potential Student Recommendation Using Machine LearningLijuan Yan0Yanshen Liu1National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, ChinaNational Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, ChinaStudent performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and area under the receiver operating characteristic curve (AUC) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.https://www.mdpi.com/2073-8994/12/5/728ensembleprediction modelstudent performancemachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lijuan Yan Yanshen Liu |
spellingShingle |
Lijuan Yan Yanshen Liu An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning Symmetry ensemble prediction model student performance machine learning |
author_facet |
Lijuan Yan Yanshen Liu |
author_sort |
Lijuan Yan |
title |
An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning |
title_short |
An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning |
title_full |
An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning |
title_fullStr |
An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning |
title_full_unstemmed |
An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning |
title_sort |
ensemble prediction model for potential student recommendation using machine learning |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2020-05-01 |
description |
Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and area under the receiver operating characteristic curve (AUC) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students. |
topic |
ensemble prediction model student performance machine learning |
url |
https://www.mdpi.com/2073-8994/12/5/728 |
work_keys_str_mv |
AT lijuanyan anensemblepredictionmodelforpotentialstudentrecommendationusingmachinelearning AT yanshenliu anensemblepredictionmodelforpotentialstudentrecommendationusingmachinelearning AT lijuanyan ensemblepredictionmodelforpotentialstudentrecommendationusingmachinelearning AT yanshenliu ensemblepredictionmodelforpotentialstudentrecommendationusingmachinelearning |
_version_ |
1724554345408626688 |