Students' performance prediction employing Decision Tree
An optimized educational community is a must in this modern era. The intersection of educational activities and the transformative potentials of Educational Data Mining (EDM) should be traversed, highlighting the reasoning behind the importance of EDM. Prior prediction of how a student stands acade...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Can Tho University Publisher
2024-10-01
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| Series: | CTU Journal of Innovation and Sustainable Development |
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| Online Access: | https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/1137 |
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| author | Abtahi Ahmed Farzana Akter Nipa Wasi Uddin Bhuyian Khaled Md Mushfique Kamrul Islam Shahin Huu-Hoa Nguyen Dewan Md. Farid |
| author_facet | Abtahi Ahmed Farzana Akter Nipa Wasi Uddin Bhuyian Khaled Md Mushfique Kamrul Islam Shahin Huu-Hoa Nguyen Dewan Md. Farid |
| author_sort | Abtahi Ahmed |
| collection | DOAJ |
| description |
An optimized educational community is a must in this modern era. The intersection of educational activities and the transformative potentials of Educational Data Mining (EDM) should be traversed, highlighting the reasoning behind the importance of EDM. Prior prediction of how a student stands academically, can facilitate them towards a much safer approach with their life decisions. This study uses the vast power and analytical domain of EDM, combining it with machine learning models, upholding an accurate prediction of students' academic performance. The study consists of a dataset containing academic, demographic and social data of undergraduate students. The paper aims to analyze comprehensively the features that act behind academic performance. Lastly, it compares the impact of non-academic data separately on a student's performance and with academic data as well. Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. The hierarchical tree from Decision Tree visualizes the key features, which include past results, family members' qualification levels and their jobs, hobbies of the student, commute time, and more.
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| format | Article |
| id | doaj-art-4a5b62474cc147e5be3bedb5f9c48424 |
| institution | DOAJ |
| issn | 2588-1418 2815-6412 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Can Tho University Publisher |
| record_format | Article |
| series | CTU Journal of Innovation and Sustainable Development |
| spelling | doaj-art-4a5b62474cc147e5be3bedb5f9c484242025-08-20T03:12:50ZengCan Tho University PublisherCTU Journal of Innovation and Sustainable Development2588-14182815-64122024-10-0116Special issue: ISDS10.22144/ctujoisd.2024.321Students' performance prediction employing Decision TreeAbtahi Ahmed0Farzana Akter Nipa1Wasi Uddin Bhuyian2Khaled Md Mushfique3Kamrul Islam Shahin4Huu-Hoa Nguyen5Dewan Md. Farid6United International UniversityUnited International UniversityUnited International UniversityUnited International UniversityUniversity of Southern DenmarkCan Tho UniversityUnited International University, Dhaka, Bangladesh An optimized educational community is a must in this modern era. The intersection of educational activities and the transformative potentials of Educational Data Mining (EDM) should be traversed, highlighting the reasoning behind the importance of EDM. Prior prediction of how a student stands academically, can facilitate them towards a much safer approach with their life decisions. This study uses the vast power and analytical domain of EDM, combining it with machine learning models, upholding an accurate prediction of students' academic performance. The study consists of a dataset containing academic, demographic and social data of undergraduate students. The paper aims to analyze comprehensively the features that act behind academic performance. Lastly, it compares the impact of non-academic data separately on a student's performance and with academic data as well. Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. The hierarchical tree from Decision Tree visualizes the key features, which include past results, family members' qualification levels and their jobs, hobbies of the student, commute time, and more. https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/1137Classification, educational data mining, machine learning, students' performance prediction |
| spellingShingle | Abtahi Ahmed Farzana Akter Nipa Wasi Uddin Bhuyian Khaled Md Mushfique Kamrul Islam Shahin Huu-Hoa Nguyen Dewan Md. Farid Students' performance prediction employing Decision Tree CTU Journal of Innovation and Sustainable Development Classification, educational data mining, machine learning, students' performance prediction |
| title | Students' performance prediction employing Decision Tree |
| title_full | Students' performance prediction employing Decision Tree |
| title_fullStr | Students' performance prediction employing Decision Tree |
| title_full_unstemmed | Students' performance prediction employing Decision Tree |
| title_short | Students' performance prediction employing Decision Tree |
| title_sort | students performance prediction employing decision tree |
| topic | Classification, educational data mining, machine learning, students' performance prediction |
| url | https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/1137 |
| work_keys_str_mv | AT abtahiahmed studentsperformancepredictionemployingdecisiontree AT farzanaakternipa studentsperformancepredictionemployingdecisiontree AT wasiuddinbhuyian studentsperformancepredictionemployingdecisiontree AT khaledmdmushfique studentsperformancepredictionemployingdecisiontree AT kamrulislamshahin studentsperformancepredictionemployingdecisiontree AT huuhoanguyen studentsperformancepredictionemployingdecisiontree AT dewanmdfarid studentsperformancepredictionemployingdecisiontree |