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Predicting Student Attrition in High...

Castelino, Dezzil M.,

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Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning // Dezzil M Castelino.
Author:	Castelino, Dezzil M.,
Description:	1 electronic resource (60 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Educational administration. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31300179
ISBN:	9798382770550

Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning /
Castelino, Dezzil M.,

Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning /Dezzil M Castelino. - 1 electronic resource (60 pages)

Source: Masters Abstracts International, Volume: 85-11.

UAE has made significant progress in the field of education, including Higher Education, by attracting students from all around the world to various colleges and universities. Student dropout or attrition is a major issue that is faced by Higher Education Institutions (HEI). Hence, we need effective techniques to identify student data that affects attrition. This research aims to explore the use of machine learning algorithms to predict student attrition at Rochester Institute of Technology- Dubai (RIT Dubai). In this research, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was used as it is widely used in projects based on data mining and machine learning. Using IBM SPSS Modeler and SPSS Statistics, various machine learning models including Random Trees, Logistic Regression, Linear Support Vector Machines (LSVM), and Neural Networks were explored to find the most suitable predictive model that could accurately find student attrition and support in providing early intervention to mitigate this problem at the HEI. The logistic regression model provided the best results with the highest accuracy, AUC, precision, and recall at 1.0. The research involves analyzing student data such as demographics, results of language tests, data on academic performance, acceptance and start dates, socioeconomic factors, and other contextual data from RIT Dubai. The results of the study provide a clear understanding of the most significant predictors of student attrition that were found through machine learning. This will prove to be an important instrument for RIT Dubai to improve student retention rates.

English

ISBN: 9798382770550Subjects--Topical Terms:

2122799
Educational administration.
Subjects--Index Terms:

Algorithms

Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning /
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520 $a UAE has made significant progress in the field of education, including Higher Education, by attracting students from all around the world to various colleges and universities. Student dropout or attrition is a major issue that is faced by Higher Education Institutions (HEI). Hence, we need effective techniques to identify student data that affects attrition. This research aims to explore the use of machine learning algorithms to predict student attrition at Rochester Institute of Technology- Dubai (RIT Dubai). In this research, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was used as it is widely used in projects based on data mining and machine learning. Using IBM SPSS Modeler and SPSS Statistics, various machine learning models including Random Trees, Logistic Regression, Linear Support Vector Machines (LSVM), and Neural Networks were explored to find the most suitable predictive model that could accurately find student attrition and support in providing early intervention to mitigate this problem at the HEI. The logistic regression model provided the best results with the highest accuracy, AUC, precision, and recall at 1.0. The research involves analyzing student data such as demographics, results of language tests, data on academic performance, acceptance and start dates, socioeconomic factors, and other contextual data from RIT Dubai. The results of the study provide a clear understanding of the most significant predictors of student attrition that were found through machine learning. This will prove to be an important instrument for RIT Dubai to improve student retention rates.
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650 4 $a Educational administration. $3 2122799
650 4 $a Middle Eastern studies. $3 3168421
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653 $a Machine learning
653 $a Outliers
653 $a Performance metrics
653 $a Student attrition
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