Analyzing students records to identify patterns of students' performance
Academic failures among university students have been the subject of interest in higher education community. Students drop out due to poor academic performance as early as in the first year of their university enrolment. Many interested parties' debate and try to find reasons for this poor perf...
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my.uniten.dspace-299562024-04-17T10:35:57Z Analyzing students records to identify patterns of students' performance Hoe A.C.K. Ahmad M.S. Hooi T.C. Shanmugam M. Gunasekaran S.S. Cob Z.C. Ramasamy A. 56105282800 56036880900 55175180600 36195134500 55652730500 25824919900 56106081100 CRISP-DM data mining data modeling clustering data preparation Data mining Information systems Population statistics Academic performance Business understanding CRISP-DM Data preparation Higher education institutions Significant variables Student's performance Undergraduate students Students Academic failures among university students have been the subject of interest in higher education community. Students drop out due to poor academic performance as early as in the first year of their university enrolment. Many interested parties' debate and try to find reasons for this poor performance. Consequently, the ability to predict a student's performance could be useful in many ways to stakeholders of higher education institutions. This paper discusses the data mining technique used to identify the significant variables that affects and influences the performance of undergraduate students. Students' demographic and past academic performance data are then used to study the academic pattern. Early phases of the CRISP-DM methodology is also described in detail consisting business understanding, data understanding and data preparation. The data modeling and mining tool used identifies the most significant correlation of variables associated with academic success based on the past ten years of demographic and students' performance data of the College of Information Technology, Universiti Tenaga Nasional. Finally, the results from the application of the CHAID algorithm aimed at predicting students' academic success is presented. � 2013 IEEE. Final 2023-12-29T07:43:45Z 2023-12-29T07:43:45Z 2013 Conference Paper 10.1109/ICRIIS.2013.6716767 2-s2.0-84897831570 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897831570&doi=10.1109%2fICRIIS.2013.6716767&partnerID=40&md5=0ea43efd14d83b330d0682218847a776 https://irepository.uniten.edu.my/handle/123456789/29956 6716767 544 547 Scopus |
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CRISP-DM data mining data modeling clustering data preparation Data mining Information systems Population statistics Academic performance Business understanding CRISP-DM Data preparation Higher education institutions Significant variables Student's performance Undergraduate students Students |
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CRISP-DM data mining data modeling clustering data preparation Data mining Information systems Population statistics Academic performance Business understanding CRISP-DM Data preparation Higher education institutions Significant variables Student's performance Undergraduate students Students Hoe A.C.K. Ahmad M.S. Hooi T.C. Shanmugam M. Gunasekaran S.S. Cob Z.C. Ramasamy A. Analyzing students records to identify patterns of students' performance |
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Academic failures among university students have been the subject of interest in higher education community. Students drop out due to poor academic performance as early as in the first year of their university enrolment. Many interested parties' debate and try to find reasons for this poor performance. Consequently, the ability to predict a student's performance could be useful in many ways to stakeholders of higher education institutions. This paper discusses the data mining technique used to identify the significant variables that affects and influences the performance of undergraduate students. Students' demographic and past academic performance data are then used to study the academic pattern. Early phases of the CRISP-DM methodology is also described in detail consisting business understanding, data understanding and data preparation. The data modeling and mining tool used identifies the most significant correlation of variables associated with academic success based on the past ten years of demographic and students' performance data of the College of Information Technology, Universiti Tenaga Nasional. Finally, the results from the application of the CHAID algorithm aimed at predicting students' academic success is presented. � 2013 IEEE. |
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56105282800 |
| author_facet |
56105282800 Hoe A.C.K. Ahmad M.S. Hooi T.C. Shanmugam M. Gunasekaran S.S. Cob Z.C. Ramasamy A. |
| format |
Conference Paper |
| author |
Hoe A.C.K. Ahmad M.S. Hooi T.C. Shanmugam M. Gunasekaran S.S. Cob Z.C. Ramasamy A. |
| author_sort |
Hoe A.C.K. |
| title |
Analyzing students records to identify patterns of students' performance |
| title_short |
Analyzing students records to identify patterns of students' performance |
| title_full |
Analyzing students records to identify patterns of students' performance |
| title_fullStr |
Analyzing students records to identify patterns of students' performance |
| title_full_unstemmed |
Analyzing students records to identify patterns of students' performance |
| title_sort |
analyzing students records to identify patterns of students' performance |
| publishDate |
2023 |
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1806427903466930176 |
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13.222552 |