| 講演名 | 2023-01-25 Students Dropout Analytics and Prediction in Higher Education Case Study on Various Campuses of Prince of Songkla University Theerayuth Prasompong(PSU), Suwimon Bureekarn(PSU), Chidchanok Choksuchat(PSU), |
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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. |
| 抄録(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 |
| キーワード(英) | dropout / prediction / machine learning / Dropout Analytics |
| 資料番号 | IA2022-73 |
| 発行日 | 2023-01-18 (IA) |
| 研究会情報 | |
| 研究会 | IA |
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| 開催期間 | 2023/1/25(から1日開催) |
| 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 |
| 委員長氏名（和） | 義久 智樹(阪大) |
| 委員長氏名（英） | Tomoki Yoshihisa(Osaka Univ.) |
| 副委員長氏名（和） | 作元 雄輔(関西学院大) / 屏 雄一郎(KDDI総合研究所) / 山本 寛(立命館大) |
| 副委員長氏名（英） | Yusuke Sakumoto(Kwansei Gakuin Univ.) / Yuichiro Hei(KDDI Research) / Hiroshi Yamamoto(Ritsumeikan Univ.) |
| 幹事氏名（和） | 大平 健司(阪大) / 坂野 遼平(工学院大) / 野林 大起(九工大) |
| 幹事氏名（英） | Kenji Ohira(Osaka Univ.) / Ryohei Banno(Kogakuin Univ.) / Daiki Nobayashi(Kyushu Inst. of Tech.) |
| 幹事補佐氏名（和） | 小谷 大祐(京大) / 中村 遼(福岡大) / 中村 遼(東大) |
| 幹事補佐氏名（英） | Daisuke Kotani(Kyoto Univ.) / Ryo Nakamura(Fukuoka Univ.) / Ryo Nakamura(Univ. of Tokyo) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Internet Architecture |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Students Dropout Analytics and Prediction in Higher Education Case Study on Various Campuses of Prince of Songkla University |
| サブタイトル（和） | |
| 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 |
| 第 1 著者 氏名（和/英） | Theerayuth Prasompong / Theerayuth Prasompong |
| 第 1 著者 所属（和/英） | Prince of Songkla University(略称：PSU) Prince of Songkla University(略称：PSU) |
| 第 2 著者 氏名（和/英） | Suwimon Bureekarn / Suwimon Bureekarn |
| 第 2 著者 所属（和/英） | Prince of Songkla University(略称：PSU) Prince of Songkla University(略称：PSU) |
| 第 3 著者 氏名（和/英） | Chidchanok Choksuchat / Chidchanok Choksuchat |
| 第 3 著者 所属（和/英） | Prince of Songkla University(略称：PSU) Prince of Songkla University(略称：PSU) |
| 発表年月日 | 2023-01-25 |
| 資料番号 | IA2022-73 |
| 巻番号（vol） | vol.122 |
| 号番号（no） | IA-359 |
| ページ範囲 | pp.36-42(IA), |
| ページ数 | 7 |
| 発行日 | 2023-01-18 (IA) |