Student Performance Prediction Approach Based on Educational Data Mining
Predicting student performance is crucial for improving students’ future academic achievements. Within student groups, common characteristics can reveal trends in overall student learning. Most studies tend to focus on the common characteristics of students but ignore their individual cha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10327720/ |
| _version_ | 1827356048961306624 |
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| author | Ziling Chen Gang Cen Ying Wei Zifei Li |
| author_facet | Ziling Chen Gang Cen Ying Wei Zifei Li |
| author_sort | Ziling Chen |
| collection | DOAJ |
| description | Predicting student performance is crucial for improving students’ future academic achievements. Within student groups, common characteristics can reveal trends in overall student learning. Most studies tend to focus on the common characteristics of students but ignore their individual characteristics. However, individual characteristics are important in promoting student academic performance because they allow us to understand the unique learning performances of each student. To address this issue, this paper proposes a student performance prediction approach. First, addressing the problem of difficulty in effectively dividing the student samples under multi-dimensional discrete data, we propose a method that combines the relationship matrix-based bipartite network approach (RMBN) with Louvain clustering. Second, the hybrid neural network model based on a relationship matrix (RMHNN) is proposed to address the problem that discrete types of features are difficult to fit by algorithms. The results show that the implementation of the model on real student data can effectively predict student performance with an accuracy of 93.1% and an F1-score of 90.45%. With the model’s predicted student performance, educators can provide individualized support and assistance to each student. |
| first_indexed | 2024-03-08T04:53:03Z |
| format | Article |
| id | doaj.art-ed36f401fddd4fc0bcea67b0444b0ba6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:53:03Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ed36f401fddd4fc0bcea67b0444b0ba62024-02-08T00:00:44ZengIEEEIEEE Access2169-35362023-01-011113126013127210.1109/ACCESS.2023.333598510327720Student Performance Prediction Approach Based on Educational Data MiningZiling Chen0https://orcid.org/0009-0000-4579-4914Gang Cen1https://orcid.org/0009-0002-0719-6862Ying Wei2Zifei Li3https://orcid.org/0009-0007-1476-6356School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaPredicting student performance is crucial for improving students’ future academic achievements. Within student groups, common characteristics can reveal trends in overall student learning. Most studies tend to focus on the common characteristics of students but ignore their individual characteristics. However, individual characteristics are important in promoting student academic performance because they allow us to understand the unique learning performances of each student. To address this issue, this paper proposes a student performance prediction approach. First, addressing the problem of difficulty in effectively dividing the student samples under multi-dimensional discrete data, we propose a method that combines the relationship matrix-based bipartite network approach (RMBN) with Louvain clustering. Second, the hybrid neural network model based on a relationship matrix (RMHNN) is proposed to address the problem that discrete types of features are difficult to fit by algorithms. The results show that the implementation of the model on real student data can effectively predict student performance with an accuracy of 93.1% and an F1-score of 90.45%. With the model’s predicted student performance, educators can provide individualized support and assistance to each student.https://ieeexplore.ieee.org/document/10327720/Performance predictiondata miningcommon characteristicsindividual characteristicsrelation network |
| spellingShingle | Ziling Chen Gang Cen Ying Wei Zifei Li Student Performance Prediction Approach Based on Educational Data Mining IEEE Access Performance prediction data mining common characteristics individual characteristics relation network |
| title | Student Performance Prediction Approach Based on Educational Data Mining |
| title_full | Student Performance Prediction Approach Based on Educational Data Mining |
| title_fullStr | Student Performance Prediction Approach Based on Educational Data Mining |
| title_full_unstemmed | Student Performance Prediction Approach Based on Educational Data Mining |
| title_short | Student Performance Prediction Approach Based on Educational Data Mining |
| title_sort | student performance prediction approach based on educational data mining |
| topic | Performance prediction data mining common characteristics individual characteristics relation network |
| url | https://ieeexplore.ieee.org/document/10327720/ |
| work_keys_str_mv | AT zilingchen studentperformancepredictionapproachbasedoneducationaldatamining AT gangcen studentperformancepredictionapproachbasedoneducationaldatamining AT yingwei studentperformancepredictionapproachbasedoneducationaldatamining AT zifeili studentperformancepredictionapproachbasedoneducationaldatamining |