Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance.
The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispen...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Kabale University
2024
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| Online Access: | http://hdl.handle.net/20.500.12493/2389 |
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| _version_ | 1815869488502931456 |
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| author | Hussein, Alkattan Alhumaima, Ali Subhi Oluwaseun, Adelaja A. Abotaleb, Mostafa Mijwil, Maad M. Pradeep, Mishra Sekiwu, Denis Bamwerinde, Wilson Turyasingura, Benson |
| author_facet | Hussein, Alkattan Alhumaima, Ali Subhi Oluwaseun, Adelaja A. Abotaleb, Mostafa Mijwil, Maad M. Pradeep, Mishra Sekiwu, Denis Bamwerinde, Wilson Turyasingura, Benson |
| author_sort | Hussein, Alkattan |
| collection | KAB-DR |
| description | The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. Special emphasis was placed on the use of algorithms such as decision trees. The study includes an analysis of factors that affect students' academic performance such as previous academic achievement in educational activities, as well as social and psychological factors. Classification models were applied using the KNIME platform and the WEKA tool to analyze students' performance in three courses: database technology, artificial intelligence, and image processing in the ICT degree program. The results showed that the use of decision trees can effectively classify students' performance and determine the success and failure rates. The cruel outright mistakes, RMS errors, and relative supreme mistakes all showed 0% whereas the kappa esteem obtained from the analysis extended between 0.991 and 1.00 which significantly concurs with most statistical values. |
| format | Article |
| id | oai:idr.kab.ac.ug:20.500.12493-2389 |
| institution | KAB-DR |
| language | English |
| publishDate | 2024 |
| publisher | Kabale University |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-23892024-11-12T00:01:22Z Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. Hussein, Alkattan Alhumaima, Ali Subhi Oluwaseun, Adelaja A. Abotaleb, Mostafa Mijwil, Maad M. Pradeep, Mishra Sekiwu, Denis Bamwerinde, Wilson Turyasingura, Benson Data Mining Machine Learning Academic Performance Decision Trees Education Analysis The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. Special emphasis was placed on the use of algorithms such as decision trees. The study includes an analysis of factors that affect students' academic performance such as previous academic achievement in educational activities, as well as social and psychological factors. Classification models were applied using the KNIME platform and the WEKA tool to analyze students' performance in three courses: database technology, artificial intelligence, and image processing in the ICT degree program. The results showed that the use of decision trees can effectively classify students' performance and determine the success and failure rates. The cruel outright mistakes, RMS errors, and relative supreme mistakes all showed 0% whereas the kappa esteem obtained from the analysis extended between 0.991 and 1.00 which significantly concurs with most statistical values. 2024-11-11T06:47:26Z 2024-11-11T06:47:26Z 2024 Article Alkattan, H. et al. (2024). Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. Kabale: Kabale University. http://hdl.handle.net/20.500.12493/2389 en application/pdf Kabale University |
| spellingShingle | Data Mining Machine Learning Academic Performance Decision Trees Education Analysis Hussein, Alkattan Alhumaima, Ali Subhi Oluwaseun, Adelaja A. Abotaleb, Mostafa Mijwil, Maad M. Pradeep, Mishra Sekiwu, Denis Bamwerinde, Wilson Turyasingura, Benson Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. |
| title | Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. |
| title_full | Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. |
| title_fullStr | Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. |
| title_full_unstemmed | Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. |
| title_short | Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. |
| title_sort | employing data mining techniques and machine learning models in classification of students academic performance |
| topic | Data Mining Machine Learning Academic Performance Decision Trees Education Analysis |
| url | http://hdl.handle.net/20.500.12493/2389 |
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