research-article

Educational Data Mining: Discovering Principal Factors for Better Academic Performance

Authors:

Yucheng Jin,

Xiaomeng Yang,

Chengting Yu,

Liangjing YangAuthors Info & Claims

BDET '21: Proceedings of the 2021 3rd International Conference on Big Data Engineering and Technology

Pages 1 - 8

https://doi.org/10.1145/3474944.3474945

Published: 15 October 2021 Publication History

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Abstract

The past decades have witnessed the vigorous development of new technologies in the educational field, among which Educational Data Mining (EDM) played an indispensable role in pedagogical improvement, enabling researchers to discover useful knowledge from education-oriented databases. By clustering student-related and parents-related variables into three categories: demographic and family background information (Demographic), self-perceived willingness for education (Willingness), perceived family interaction (Interaction) and utilizing various EDM methodologies such as linear regression, regression tree, random forest, and neural network, this study is the first attempt to conduct a comprehensive and quantitative investigation into the principal factors that influence Chinese junior high school students’ academic performance on a nationally representative survey, the China Education Panel Survey (CEPS) dataset. Additionally, this study further summarizes, explains, and compares different principal factors discovered by different EDM techniques, and proposes two practical strategies for mitigating China's educational inequality.

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Luo YWang Z(2024)Feature Mining Algorithm for Student Academic Prediction Based on Interpretable Deep Neural Network2024 12th International Conference on Information and Education Technology (ICIET)10.1109/ICIET60671.2024.10542709(1-5)Online publication date: 18-Mar-2024
https://doi.org/10.1109/ICIET60671.2024.10542709
Balachandar VVenkatesh K(2023)Predicting and Analysing University Dropout Rates using Machine Learning Methods2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)10.1109/ICSES60034.2023.10465449(1-8)Online publication date: 14-Dec-2023
https://doi.org/10.1109/ICSES60034.2023.10465449
Li CLi MHuang CTseng YKim SYeom S(2023)Educational Data Mining in Prediction of Students’ Learning Performance: A Scoping ReviewTowards a Collaborative Society Through Creative Learning10.1007/978-3-031-43393-1_33(361-372)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-3-031-43393-1_33

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BDET '21: Proceedings of the 2021 3rd International Conference on Big Data Engineering and Technology

January 2021

104 pages

ISBN:9781450389280

DOI:10.1145/3474944

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 October 2021

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BDET 2021: 2021 the 3rd International Conference on Big Data Engineering and Technology

January 16 - 18, 2021

Singapore, Singapore

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Luo YWang Z(2024)Feature Mining Algorithm for Student Academic Prediction Based on Interpretable Deep Neural Network2024 12th International Conference on Information and Education Technology (ICIET)10.1109/ICIET60671.2024.10542709(1-5)Online publication date: 18-Mar-2024
https://doi.org/10.1109/ICIET60671.2024.10542709
Balachandar VVenkatesh K(2023)Predicting and Analysing University Dropout Rates using Machine Learning Methods2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)10.1109/ICSES60034.2023.10465449(1-8)Online publication date: 14-Dec-2023
https://doi.org/10.1109/ICSES60034.2023.10465449
Li CLi MHuang CTseng YKim SYeom S(2023)Educational Data Mining in Prediction of Students’ Learning Performance: A Scoping ReviewTowards a Collaborative Society Through Creative Learning10.1007/978-3-031-43393-1_33(361-372)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-3-031-43393-1_33

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Educational data mining: a review of the state of the art

A Literature Review on Educational Data Mining with Secondary School Data

A Survey and Future Vision of Data Mining in Educational Field

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