Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model

Shuai Zhang; Jie Chen; Wenyu Zhang; Qiwei Xu; Jiaxuan Shi

doi:doi:10.11648/j.sjedu.20210902.16

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Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model

Shuai Zhang, Jie Chen, Wenyu Zhang, Qiwei Xu, Jiaxuan Shi

Published in Science Journal of Education (Volume 9, Issue 2)

Received: 13 March 2021 Accepted: Published: 26 April 2021

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Abstract

With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.

Published in	Science Journal of Education (Volume 9, Issue 2)
DOI	10.11648/j.sjedu.20210902.16
Page(s)	58-62
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Ensemble Model, Education Data Mining, Prediction, Students’ Achievements

References

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APA Style

Shuai Zhang, Jie Chen, Wenyu Zhang, Qiwei Xu, Jiaxuan Shi. (2021). Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model. Science Journal of Education, 9(2), 58-62. https://doi.org/10.11648/j.sjedu.20210902.16

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ACS Style

Shuai Zhang; Jie Chen; Wenyu Zhang; Qiwei Xu; Jiaxuan Shi. Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model. Sci. J. Educ. 2021, 9(2), 58-62. doi: 10.11648/j.sjedu.20210902.16

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AMA Style

Shuai Zhang, Jie Chen, Wenyu Zhang, Qiwei Xu, Jiaxuan Shi. Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model. Sci J Educ. 2021;9(2):58-62. doi: 10.11648/j.sjedu.20210902.16

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@article{10.11648/j.sjedu.20210902.16,
  author = {Shuai Zhang and Jie Chen and Wenyu Zhang and Qiwei Xu and Jiaxuan Shi},
  title = {Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model},
  journal = {Science Journal of Education},
  volume = {9},
  number = {2},
  pages = {58-62},
  doi = {10.11648/j.sjedu.20210902.16},
  url = {https://doi.org/10.11648/j.sjedu.20210902.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjedu.20210902.16},
  abstract = {With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.},
 year = {2021}
}

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TY  - JOUR
T1  - Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model
AU  - Shuai Zhang
AU  - Jie Chen
AU  - Wenyu Zhang
AU  - Qiwei Xu
AU  - Jiaxuan Shi
Y1  - 2021/04/26
PY  - 2021
N1  - https://doi.org/10.11648/j.sjedu.20210902.16
DO  - 10.11648/j.sjedu.20210902.16
T2  - Science Journal of Education
JF  - Science Journal of Education
JO  - Science Journal of Education
SP  - 58
EP  - 62
PB  - Science Publishing Group
SN  - 2329-0897
UR  - https://doi.org/10.11648/j.sjedu.20210902.16
AB  - With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.
VL  - 9
IS  - 2
ER  -

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Author Information

Shuai Zhang

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
Jie Chen

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
Qiwei Xu

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
Jiaxuan Shi

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Shuai Zhang, Jie Chen, Wenyu Zhang, Qiwei Xu, Jiaxuan Shi. (2021). Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model. Science Journal of Education, 9(2), 58-62. https://doi.org/10.11648/j.sjedu.20210902.16

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ACS Style

Shuai Zhang; Jie Chen; Wenyu Zhang; Qiwei Xu; Jiaxuan Shi. Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model. Sci. J. Educ. 2021, 9(2), 58-62. doi: 10.11648/j.sjedu.20210902.16

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AMA Style

Shuai Zhang, Jie Chen, Wenyu Zhang, Qiwei Xu, Jiaxuan Shi. Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model. Sci J Educ. 2021;9(2):58-62. doi: 10.11648/j.sjedu.20210902.16

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@article{10.11648/j.sjedu.20210902.16,
  author = {Shuai Zhang and Jie Chen and Wenyu Zhang and Qiwei Xu and Jiaxuan Shi},
  title = {Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model},
  journal = {Science Journal of Education},
  volume = {9},
  number = {2},
  pages = {58-62},
  doi = {10.11648/j.sjedu.20210902.16},
  url = {https://doi.org/10.11648/j.sjedu.20210902.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjedu.20210902.16},
  abstract = {With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Education Data Mining Application for Predicting Students’ Achievements of Portuguese Using Ensemble Model
AU  - Shuai Zhang
AU  - Jie Chen
AU  - Wenyu Zhang
AU  - Qiwei Xu
AU  - Jiaxuan Shi
Y1  - 2021/04/26
PY  - 2021
N1  - https://doi.org/10.11648/j.sjedu.20210902.16
DO  - 10.11648/j.sjedu.20210902.16
T2  - Science Journal of Education
JF  - Science Journal of Education
JO  - Science Journal of Education
SP  - 58
EP  - 62
PB  - Science Publishing Group
SN  - 2329-0897
UR  - https://doi.org/10.11648/j.sjedu.20210902.16
AB  - With the emergence of the massive educational data, education data mining techniques have extensively drawn considerable interest from scholars to explore the relationship between students’ achievements and other factors. In this study, the data set about the students’ achievements of Portuguese in two secondary education schools in Portugal is selected for education data mining, which involves the personal information, social and school related factors. To analyze the relationship between the students' achievements and other factors, this study proposed an ensemble model based on weighted voting for predicting the students’ achievements of Portuguese in the final period. First, the raw data is preprocessed using some basic methods, including dummy coding, correlation analysis, standardization, and normalization. Second, the isolation forest algorithm-based outlier adaption is applied to deal with the data set to enhance the robustness of the ensemble model. Finally, two base classifiers, i.e. gradient boosting decision tree and extreme gradient boosting, are integrated to form the ensemble model. The experiments are presented for verifying the superiority of the proposed model by comparing with five base classifiers, including gradient boosting decision tree, adaptive boosting, extreme gradient boosting, random forest, and decision tree. The experimental results demonstrate that the ensemble model performs better than other base classifiers in classification, and prove the validity of the outlier adaption based on isolation forest algorithm.
VL  - 9
IS  - 2
ER  -

Copy | Download