A study of graduate on time (GOT) for Ph.D students using decision tree model
Over the years, there has been exponential growth in the number of Doctor of Philosophy (Ph.D) graduates in most of the universities all around the world. The increment of Ph.D students causes both university and government bodies concern about the capability of the Ph.D students to accomplish the m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IP Publishing LLC
2019
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| Online Access: | https://repo.uum.edu.my/id/eprint/26926/1/AIPCP%20%202138%202019%20%201%206.pdf |
| _version_ | 1825805561147424768 |
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| author | Chin, Wan Yung Ch’ng, Chee Keong Mohd Jamil, Jastini |
| author_facet | Chin, Wan Yung Ch’ng, Chee Keong Mohd Jamil, Jastini |
| author_sort | Chin, Wan Yung |
| collection | UUM |
| description | Over the years, there has been exponential growth in the number of Doctor of Philosophy (Ph.D) graduates in most of the universities all around the world. The increment of Ph.D students causes both university and government bodies concern about the capability of the Ph.D students to accomplish the mission of Graduate on Time (GOT) that is stipulated by the university. Therefore, this study aims to classify the Ph.D students into the group of “GOT achiever” and “non-GOT achiever” by using decision tree models. Historical data that related to all Ph.D students in a public university in Malaysia has been obtained directly from the database of Graduate Academic Information System (GAIS) in order to develop and compare the performance of decision tree models (Chi-square algorithm, Gini index algorithm, Entropy algorithm and an interactive decision tree). The result gained in four decision tree models illustrated that the attributes of English background, gender and the Ph.D students’entry Cumulative Grade Point Average (CGPA) result are the core in impacting the students’ success. Among all models, decision tree model with Entropy algorithm perform the best by scoring the highest accuracy rate (72%) and sensitivity rate (95%). Therefore, it has been selected as the best model for predicting the ability of the Ph.D students in achieving GOT. The outcome can certainly ease the burden of universities in handling and controlling the GOT issue. Also, the model can be used by the university to uncover the restriction in this issue so that better plans can be carried out to boost the number of GOT achiever in future. |
| first_indexed | 2024-07-04T06:34:19Z |
| format | Article |
| id | uum-26926 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:34:19Z |
| publishDate | 2019 |
| publisher | IP Publishing LLC |
| record_format | eprints |
| spelling | uum-269262020-03-19T06:36:58Z https://repo.uum.edu.my/id/eprint/26926/ A study of graduate on time (GOT) for Ph.D students using decision tree model Chin, Wan Yung Ch’ng, Chee Keong Mohd Jamil, Jastini QA75 Electronic computers. Computer science Over the years, there has been exponential growth in the number of Doctor of Philosophy (Ph.D) graduates in most of the universities all around the world. The increment of Ph.D students causes both university and government bodies concern about the capability of the Ph.D students to accomplish the mission of Graduate on Time (GOT) that is stipulated by the university. Therefore, this study aims to classify the Ph.D students into the group of “GOT achiever” and “non-GOT achiever” by using decision tree models. Historical data that related to all Ph.D students in a public university in Malaysia has been obtained directly from the database of Graduate Academic Information System (GAIS) in order to develop and compare the performance of decision tree models (Chi-square algorithm, Gini index algorithm, Entropy algorithm and an interactive decision tree). The result gained in four decision tree models illustrated that the attributes of English background, gender and the Ph.D students’entry Cumulative Grade Point Average (CGPA) result are the core in impacting the students’ success. Among all models, decision tree model with Entropy algorithm perform the best by scoring the highest accuracy rate (72%) and sensitivity rate (95%). Therefore, it has been selected as the best model for predicting the ability of the Ph.D students in achieving GOT. The outcome can certainly ease the burden of universities in handling and controlling the GOT issue. Also, the model can be used by the university to uncover the restriction in this issue so that better plans can be carried out to boost the number of GOT achiever in future. IP Publishing LLC 2019 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/26926/1/AIPCP%20%202138%202019%20%201%206.pdf Chin, Wan Yung and Ch’ng, Chee Keong and Mohd Jamil, Jastini (2019) A study of graduate on time (GOT) for Ph.D students using decision tree model. AIP Conference Proceedings, 2138 (1). 040006-1-040006-6. ISSN 0094-243X http://doi.org/10.1063/1.5121085 doi:10.1063/1.5121085 doi:10.1063/1.5121085 |
| spellingShingle | QA75 Electronic computers. Computer science Chin, Wan Yung Ch’ng, Chee Keong Mohd Jamil, Jastini A study of graduate on time (GOT) for Ph.D students using decision tree model |
| title | A study of graduate on time (GOT) for Ph.D students using decision tree model |
| title_full | A study of graduate on time (GOT) for Ph.D students using decision tree model |
| title_fullStr | A study of graduate on time (GOT) for Ph.D students using decision tree model |
| title_full_unstemmed | A study of graduate on time (GOT) for Ph.D students using decision tree model |
| title_short | A study of graduate on time (GOT) for Ph.D students using decision tree model |
| title_sort | study of graduate on time got for ph d students using decision tree model |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/26926/1/AIPCP%20%202138%202019%20%201%206.pdf |
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