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| 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 |
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| 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 | |
| Copyright and reproduction |
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) | |
| Download PDF | IA2022-73 |
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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 |
| Keyword(5) | |
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| 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 |
| #Pages | 7 |
| Date of Issue | 2023-01-18 (IA) |
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