Over the years, many people make up their minds to pursue postgraduate studies especially in Doctor of Philosophy (Ph.D) in order to prepare themselves to confront the demands and challenges of 21st century. However, the increment of Ph.D students causes both university and government bodies concern on the capability of the Ph.D students to accomplish the mission of Graduate on Time (GOT) that is stipulated by the university. As a result, this study aims to examine the factors that affect the Ph.D students’ time frame in University Utara Malaysia (UUM) along their learning journey. According to the previous researches, the factors of student, children, supervisor, financial, employment, infrastructure, training, skills, project and peer have been identified as the elements that impact on the ability of the students to attain the GOT mission. A survey form has been distributed to thirty experts from three graduate schools of UUM to collect their opinions on the importance levels of each factor using Analytic Hierarchy Process (AHP) technique. The consistency degree obtained in this study is considered significant as it does not exceed 0.1. The outcome of this study could certainly assist the university to ameliorate the current situation based on the important level of factors hence boost the number of Ph.D students to accomplish GOT in the near future.
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.
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