Analyzing the factors that influencing the success of post graduates in achieving graduate on time (GOT) using analytic hierarchy process (AHP)

Chin, Wan Yung; Ch’ng, Chee Keong; Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

doi:10.1063/1.5012197

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…However, the thesis is a challenging endeavour that requires skills, aptitude, and determination for successful, timely completion [5,[7][8][9][10]. Thus, it is reasonable to assume that non-completion of higher education degrees should be viewed as a substantial problem that requires serious attention and proactive planning [2,4,5,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning

Nouri¹,

Larsson²,

Saqr³

2019

Lecture Notes in Computer Science

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The master thesis is the last formal step in most universities around the world. However, all students do not finish their master thesis. Thus, it is reasonable to assume that the non-completion of the master thesis should be viewed as a substantial problem that requires serious attention and proactive planning. This learning analytics study aims to understand better factors that influence completion and non-completion of master thesis projects. More specifically, we ask: which student and supervisor factors influence completion and non-completion of master thesis? Can we predict completion and noncompletion of master thesis using such variables in order to optimise the matching of supervisors and students? To answer the research questions, we extracted data about supervisors and students from two thesis management systems which record large amounts of data related to the thesis process. The sample used was 755 master thesis projects supervised by 109 teachers. By applying traditional statistical methods (descriptive statistics, correlation tests and independent sample t-tests), as well as machine learning algorithms, we identify five central factors that can accurately predict master thesis completion and non-completion. Besides the identified predictors that explain master thesis completion and non-completion, this study contributes to demonstrating how educational data and learning analytics can produce actionable data-driven insights. In this case, insights that can be utilised to inform and optimise how supervisors and students are matched and to stimulate targeted training and capacity building of supervisors.

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Section: Introductionmentioning

confidence: 99%

Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning

Nouri¹,

Larsson²,

Saqr³

2019

Lecture Notes in Computer Science

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“…Failure to resolve a candidate's negative feelings, the hesitation to approach their supervisors, are the causes for many candidates to be stuck in the middle of the path [2]. House and Johnson's findings point to the applicants' average entry grade as a decisive predictive factor of successful, timely completion [14], a finding that was corroborated by Jiranek [15] and Wright and Cochrane [30].…”

Section: 2mentioning

confidence: 96%

“…Non-completion results in a vast waste of faculty time and institutional resources, a devastating personal experience for students that costs precious time, loss of money and energy, and a societal loss of high-skilled workers [1,3,11]. Thus, it is reasonable to assume that non-completion of higher education degrees should be viewed as a substantial problem that requires serious attention and proactive planning [1,3,11,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research related to thesis projects has identified some variables that influence the performance of students undertaking thesis projects; variables that, in particular, point out the relation between the student candidate and the supervisor [1,11,16]. The specific student variables that have been indicated as influencing thesis completion are students' attitudes and motivation [2], the students' average entry grade [14], and the students' communication and language skills [15]. Among the supervisor variables, it has been shown that the supervisor's experience, research output and workload constitute factors of thesis success [15,17].…”

Section: Introductionmentioning

confidence: 99%

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Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

Nouri

Larsson

Saqr

2019

iJAI

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<p class="0abstractCxSpLast">The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research. On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.</p>

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“…UUM begins Ph.D program since year 1992 and there were 6 candidates in the first enrolment. In year 2014, doctoral students has increased to 506 candidates and the ability of the Ph.D students in attaining GOT has been a concern of students, lecturers, supervisors, faculty, school and university [3]. Although Ph.D students who are "GOT achiever" had increased to 449 in year 2017, there are still 130 postgraduates are "non-GOT achiever".…”

Section: Introductionmentioning

confidence: 99%

A study of graduate on time (GOT) for Ph.D students using decision tree model

Chin

Ch’ng

Jamil

2019

The 4TH INNOVATION AND ANALYTICS CONFERENCE &Amp; EXHIBITION (IACE 2019)

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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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Analyzing the factors that influencing the success of post graduates in achieving graduate on time (GOT) using analytic hierarchy process (AHP)

Cited by 6 publications

References 4 publications

Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning

Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning

Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

A study of graduate on time (GOT) for Ph.D students using decision tree model

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