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Paper Abstract and Keywords
Presentation 2023-01-25 15:45
Students Dropout Analytics and Prediction in Higher Education Case Study on Various Campuses of Prince of Songkla University
Theerayuth Prasompong, Suwimon Bureekarn, Chidchanok Choksuchat (PSU) IA2022-73
Abstract (in Japanese) (See Japanese page) 
(in English) In point of students, ‘dropout’ problem in higher education wastes their time and tuition fees. In contrast, universities lost their resources in various aspect as well. That is also a critical issue in Prince of Songkla University (PSU), Thailand. The Data Strategy division of the office of Digital Innovation and Intelligent Systems (DIIS) analyzed the factors and predict the dropout of students in PSU which is the biggest university in southern. By using secondary data from the database at 4 campuses, namely Hat Yai Campus, Trang Campus, Surat Thani Campus. And Phuket Campus from the academic year 2016 to 2021. There was a total of 12 independent variables, and the dependent variable was the PSU's student dropout related. By using the student dropout and graduation student data for the train model, and the resulting model to predict students who are studying the results of the analysis showed that the 3 techniques with the highest accuracy were the Light Gradient Boosting Machine technique with a predicting accuracy of 90.78% and the second, the Random Forest Classifier technique, with a predicting accuracy of 90.78%, 90.29% and the Extra Trees Classifier had predicting accuracy of 89.60% respectively, which were the very good levels.
Keyword (in Japanese) (See Japanese page) 
(in English) dropout / prediction / machine learning / Dropout Analytics / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 359, IA2022-73, pp. 36-42, Jan. 2023.
Paper # IA2022-73 
Date of Issue 2023-01-18 (IA) 
ISSN Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IA  
Conference Date 2023-01-25 - 2023-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Umeda Campus, Kwansei Gakuin University (Osaka) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Sensor Network, IoT, M2M, etc., and IA2022 - Workshop on Internet Architecture and Applications 2022 
Paper Information
Registration To IA 
Conference Code 2023-01-IA 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Students Dropout Analytics and Prediction in Higher Education Case Study on Various Campuses of Prince of Songkla University 
Sub Title (in English)  
Keyword(1) dropout  
Keyword(2) prediction  
Keyword(3) machine learning  
Keyword(4) Dropout Analytics  
1st Author's Name Theerayuth Prasompong  
1st Author's Affiliation Prince of Songkla University (PSU)
2nd Author's Name Suwimon Bureekarn  
2nd Author's Affiliation Prince of Songkla University (PSU)
3rd Author's Name Chidchanok Choksuchat  
3rd Author's Affiliation Prince of Songkla University (PSU)
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Speaker Author-1 
Date Time 2023-01-25 15:45:00 
Presentation Time 20 minutes 
Registration for IA 
Paper # IA2022-73 
Volume (vol) vol.122 
Number (no) no.359 
Page pp.36-42 
Date of Issue 2023-01-18 (IA) 

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