Academic Analytics: Applying C4.5 Decision Tree Algorithm in Predicting Success in the Licensure Examination of Graduates
This study predicted success in The Licensure Examinations for Teachers (LET) applying C4.5 decision tree algorithm. This system was eyed to help the students and the academia improve their success rates in the LET. The following were the academic areas considered: Entrance examination raw scores; g...
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Published in | 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) pp. 193 - 197 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.02.2019
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Subjects | |
Online Access | Get full text |
DOI | 10.1109/CCOMS.2019.8821710 |
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Abstract | This study predicted success in The Licensure Examinations for Teachers (LET) applying C4.5 decision tree algorithm. This system was eyed to help the students and the academia improve their success rates in the LET. The following were the academic areas considered: Entrance examination raw scores; general weighted average (GWA) in the major field of specialization and in professional education and general education subjects; and, whether pass or fail in LET review results as well as LET Board results. There were a total of 348 instances studied, spread over a 5-year period, from 2012 to 2017. The size of the C4.5 pruned tree totaled to 16 with 10 leaves. The predictive capacity of the system was found to be almost perfect, evidenced by the generated Kappa value of 0.8195. Therefore, should there be any predicted failure, the teachers and deans could promptly provide intervention programs to improve the scores and GWA of students, which would eventually redound to the success in the LET. This study was conducted at Aklan State University (ASU), (Philippines), because at current times, there is still no academic analytics system installed for this purpose. Consequently, ASU was considered as the test case and the datasets utilized in this study were from ASU. In essence, this study could help in accreditation matters, particularly in tracking down the LET success rates of the teacher graduates of HEIs, ASU included. |
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AbstractList | This study predicted success in The Licensure Examinations for Teachers (LET) applying C4.5 decision tree algorithm. This system was eyed to help the students and the academia improve their success rates in the LET. The following were the academic areas considered: Entrance examination raw scores; general weighted average (GWA) in the major field of specialization and in professional education and general education subjects; and, whether pass or fail in LET review results as well as LET Board results. There were a total of 348 instances studied, spread over a 5-year period, from 2012 to 2017. The size of the C4.5 pruned tree totaled to 16 with 10 leaves. The predictive capacity of the system was found to be almost perfect, evidenced by the generated Kappa value of 0.8195. Therefore, should there be any predicted failure, the teachers and deans could promptly provide intervention programs to improve the scores and GWA of students, which would eventually redound to the success in the LET. This study was conducted at Aklan State University (ASU), (Philippines), because at current times, there is still no academic analytics system installed for this purpose. Consequently, ASU was considered as the test case and the datasets utilized in this study were from ASU. In essence, this study could help in accreditation matters, particularly in tracking down the LET success rates of the teacher graduates of HEIs, ASU included. |
Author | Clarin, Jeffrey A. Romana, Cherry Lyn C. Sta Feliscuzo, Larmie S. |
Author_xml | – sequence: 1 givenname: Jeffrey A. surname: Clarin fullname: Clarin, Jeffrey A. organization: Cebu Institute of Technology University, Cebu City, Philippines – sequence: 2 givenname: Cherry Lyn C. Sta surname: Romana fullname: Romana, Cherry Lyn C. Sta organization: Cebu Institute of Technology University, Cebu City, Philippines – sequence: 3 givenname: Larmie S. surname: Feliscuzo fullname: Feliscuzo, Larmie S. organization: Cebu Institute of Technology University, Cebu City, Philippines |
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Snippet | This study predicted success in The Licensure Examinations for Teachers (LET) applying C4.5 decision tree algorithm. This system was eyed to help the students... |
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SubjectTerms | academic analytics C4.5 algorithm Classification algorithms Data mining decision tree Decision trees kDD performance prediction Prediction algorithms Training Urban areas |
Title | Academic Analytics: Applying C4.5 Decision Tree Algorithm in Predicting Success in the Licensure Examination of Graduates |
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