Predicting the “graduate on time (GOT)” of PhD students using binary logistics regression model

Shariff, S. Sarifah Radiah; Rodzi, Nur Atiqah Mohd; Rahman, Kahartini Abdul; Zahari, Siti Meriam; Deni, Sayang Mohd

doi:10.1063/1.4966105

Cited by 7 publications

(7 citation statements)

References 2 publications

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“…The model gained 89.84% of accuracy. Likewise, [24] also used single classifier to predict postgraduate doctoral degree students that will complete their study on time by using Binary Linear Regression. The outcome revealed that only 6.8% of the students in the year 2014 were able to graduate on time.…”

Section: Literature Reviewmentioning

confidence: 99%

Drop-Out Prediction in Higher Education Among B40 Students

Sani¹,

Nafuri²,

Othman³

et al. 2020

IJACSA

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Malaysia citizens are categorized into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. In 2018, it was estimated that 4.1 million households belong to this group. The government of Malaysia has widened access to higher education for the B40 group in an effort to reduce the gaps in socioeconomics and to improve their living standards. Statistical data shows that since 2013, a yearly intake of students in bachelor's degree programs in Malaysia's public universities amounts to more than 85,000. Despite this huge number of enrolments, not all were able to graduate, including students from low-income family background. Data mining approach with machine learning techniques has been widely used effectively and accurately to predict students at risk of dropping out in general education. However, machine learning related works on student attrition in Malaysia's higher education is generally lacking. Therefore, in this research, three machine learning models were developed using Decision Tree, Random Forest and Artificial Neural Network algorithm in order to classify attrition among B40 students in bachelor's degree programs in Malaysia's public universities. Comparative performance analysis between the three models indicates that the Random Forest model is the best model in predicting student attrition in this study. Random Forest model outperforms the other two models in terms of accuracy, precision, recall and Fmeasure with the value of 95.93%, 97.10%, 81.26% and 88.50%, respectively. Nevertheless, there is a statistically significant difference in performance between the Random Forest model and Decision Tree model but no statistically significant difference between Random Forest models and Artificial Neural Network model.

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Section: Literature Reviewmentioning

confidence: 99%

Drop-Out Prediction in Higher Education Among B40 Students

Sani¹,

Nafuri²,

Othman³

et al. 2020

IJACSA

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“…Regression requires tedious, iterative model building and selection procedure to ensure the transformation is made on predictors and their interaction. The classifier for regression is logistic regression and it was used to make a prediction on the outcome of predictor variables and to measure the correlation between the categorical variable and dependent variable to change the probability score [57,63]. Probability score can be considered a success when the value is greater than a given numerical cutoff but it can be considered a failure when the value is lower than a given numerical cutoff [48].…”

Section: A Supervised Data Miningmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

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Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students' information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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“…The concept of graduate on time (GOT) can be best explained as a condition where students finish or complete their studies within the stipulated time. Another definition by [15], GOT is a state where students accomplished their studies in respective time that has been set by the university. Furthermore, according to Texas Tech University, timely graduation is defined as: "When the students graduated within the university expected timeline".…”

Section: A An Overview Of "Graduate On Time" In the Higher Educationmentioning

confidence: 99%

“…The main reason why female works better than male is because the female has better self-discipline and more focus in their study or work compared to men [45]. Above all, this statement can be strengthened by research done by [15]. They proposed a predictive model to predict the graduation status of PHD students.…”

Section: Related Workmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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Predicting the “graduate on time (GOT)” of PhD students using binary logistics regression model

Cited by 7 publications

References 2 publications

Drop-Out Prediction in Higher Education Among B40 Students

Drop-Out Prediction in Higher Education Among B40 Students

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

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