東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Predicting Student Attrition in High...

Castelino, Dezzil M.,

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning // Dezzil M Castelino.
作者:	Castelino, Dezzil M.,
面頁冊數:	1 electronic resource (60 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
標題:	Educational administration. -
電子資源:	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 /
LDR:03074nmm a22004453i 4500 001 2432961
005 20250818063858.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798382770550
035 $a (MiAaPQD)AAI31300179
035 $a AAI31300179
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Castelino, Dezzil M., $e author. $3 3822386
245 1 0 $a Predicting Student Attrition in Higher Education Institutions in the UAE Using Machine Learning / $c Dezzil M Castelino.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (60 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-11.
500 $a Advisors: Modak, Sanjay Committee members: Messatfa, Hammou.
502 $b M.S. $c Rochester Institute of Technology $d 2024.
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.
546 $a English
590 $a School code: 0465
650 4 $a Educational administration. $3 2122799
650 4 $a Middle Eastern studies. $3 3168421
653 $a Algorithms
653 $a Data mining
653 $a Machine learning
653 $a Outliers
653 $a Performance metrics
653 $a Student attrition
690 $a 0514
690 $a 0555
690 $a 0800
710 2 $a Rochester Institute of Technology. $b Data Science. $e degree granting institution. $3 3817159
720 1 $a Modak, Sanjay $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-11.
790 $a 0465
791 $a M.S.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31300179