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| 講演名 | 2023-01-25 15:45 Predicting the drop out of Prince of Songkla University students using machine learning methods ○Theerayuth Prasompong・Suwimon Bureekarn・Chidchanok Choksuchat（PSU）  IA2022-73 |
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| 抄録 | （和） | 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. |
| （英） | 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. | |
| キーワード | （和） | dropout / prediction / machine learning / Dropout Analytics / / / / |
| （英） | dropout / prediction / machine learning / Dropout Analytics / / / / | |
| 文献情報 | 信学技報, vol. 122, no. 359, IA2022-73, pp. 36-42, 2023年1月. | |
| 資料番号 | IA2022-73 | |
| 発行日 | 2023-01-18 (IA) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | IA2022-73 |
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| 研究会情報 | |
| 研究会 | IA |
| 開催期間 | 2023-01-25 - 2023-01-25 |
| 開催地（和） | 関西学院大学 大阪梅田キャンパス (大阪府) |
| 開催地（英） | Osaka Umeda Campus, Kwansei Gakuin University (Osaka) |
| テーマ（和） | センサー ネットワーク, IoT, M2M, 一般, 及び IA2022 - Workshop on Internet Architecture and Applications 2022 |
| テーマ（英） | Sensor Network, IoT, M2M, etc., and IA2022 - Workshop on Internet Architecture and Applications 2022 |
| 講演論文情報の詳細 | |
| 申込み研究会 | IA |
| 会議コード | 2023-01-IA |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Predicting the drop out of Prince of Songkla University students using machine learning methods |
| サブタイトル（英） | |
| キーワード(1)（和/英） | dropout / dropout |
| キーワード(2)（和/英） | prediction / prediction |
| キーワード(3)（和/英） | machine learning / machine learning |
| キーワード(4)（和/英） | Dropout Analytics / Dropout Analytics |
| キーワード(5)（和/英） | / |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | Theerayuth Prasompong / Theerayuth Prasompong / |
| 第1著者 所属（和/英） | Prince of Songkla University (略称： プリンス・オブ・ソンクラー大) Prince of Songkla University (略称： PSU) |
| 第2著者 氏名（和/英/ヨミ） | Suwimon Bureekarn / Suwimon Bureekarn / |
| 第2著者 所属（和/英） | Prince of Songkla University (略称： プリンス・オブ・ソンクラー大) Prince of Songkla University (略称： PSU) |
| 第3著者 氏名（和/英/ヨミ） | Chidchanok Choksuchat / Chidchanok Choksuchat / |
| 第3著者 所属（和/英） | Prince of Songkla University (略称： プリンス・オブ・ソンクラー大) Prince of Songkla University (略称： PSU) |
| 講演者 | 第1著者 |
| 発表日時 | 2023-01-25 15:45:00 |
| 発表時間 | 20分 |
| 申込先研究会 | IA |
| 資料番号 | IA2022-73 |
| 巻番号（vol） | vol.122 |
| 号番号（no） | no.359 |
| ページ範囲 | pp.36-42 |
| ページ数 | 7 |
| 発行日 | 2023-01-18 (IA) |
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