Prediction of graduation delay based on student performance

Ojha, Tushar; Heileman, Gregory L.; Martínez‐Ramón, Manel; Slim, Ahmad

doi:10.1109/ijcnn.2017.7966290

Cited by 24 publications

(18 citation statements)

References 9 publications

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“…A total of 33 papers on student performance predictions were studied and analyzed to gather all the factors that influenced the performance of students. Our research bears a close resemblance to a study by [1], as they utilized student's performance to predict the students' graduation delay. They believed that the graduation time of a student depends highly on the performance of that student itself.…”

Section: Related Workmentioning

confidence: 76%

“…This serious issue impacts the universities as good graduation rate is at stake that also the main key when measuring the position of a university in the education industry. Another key point from [1], the writers emphasized that student graduation rates were often used as an objective metric to measure institutional effectiveness. This challenging scenario is eye-opening that worries many parties, especially the management team of UiTM.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

View full text Add to dashboard Cite

show abstract

“…Pang et al (2017) predict students' graduation based on their historical grades over multiple semesters. Ojha et al (2017) and Tampakas et al (2018) use demographic features and historical academic features to predict students' graduation time. Andreswari et al (2019) introduce more detailed background information.…”

Section: Features For Predicting Difficulty In Graduationmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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“…Educational Data Mining has a variety of purposes. Some studies use EDM to predict academic patterns by reviewing the accuracy of the study period of students in their education such as research conducted by [4], [5], [2], [6], [7], [8], [9]. In addition there are studies with the aim of predicting academic patterns by reviewing student performance such as research conducted by [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

Wibowo

2020

Logic

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There is data of students who experience Drop Out which raises the curiosity in IST AKPRIND's industrial engineering study program on students' graduation patterns. It is necessary to have research on how to classify the data held by industrial engineering study programs in order to obtain students' graduation patterns as evaluation material in the administration of study programs. This study also produced a design to set the goals of Educational Data Mining, this case as a student modeling that would be achieved by predicting using the Decision Tree method. The final results showed a mismatch between the general information data passed and the drop out of the rule obtained using the decision tree algorithm in the Rapidminer software which is shown by an accuracy of 95.83%. This value indicates that there is a match between the prediction of student identity data with the rule obtained using the decision tree algorithm.

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Prediction of graduation delay based on student performance

Cited by 24 publications

References 9 publications

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Educational Anomaly Analytics: Features, Methods, and Challenges

Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

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