Decision Tree with Light Gradient Boosting Algorithm for University Teacher Performance Evaluation
University teacher performance evaluation analyze educators depending on teaching effectiveness, student engagement, and research contributors. It aims to ensure continuous professional development and quality education. Moreover, it contains technology and different teaching approach for engaging s...
Uloženo v:
| Vydáno v: | 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) s. 1 - 5 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
21.02.2025
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | University teacher performance evaluation analyze educators depending on teaching effectiveness, student engagement, and research contributors. It aims to ensure continuous professional development and quality education. Moreover, it contains technology and different teaching approach for engaging students and rapid deeper learning. However, it has challenges of engaging students with different learning styles by managing efficient content delivery. This results in gap at student participation and comprehension that minimize overall learning outcomes. This research proposes Decision Tree with Light Gradient Boosting Algorithm (DT-LGBA) to evaluate the performance for university teachers. DT-LGBA enable for accurate determination of primary factors to teach performance by effectively managing both non-linear and linear relationship which provides reliable evaluation outcomes. DT makes easy interpretation to influence teacher performance because it presents clear decision rules whereas LGBA provides effective in managing intricate non-linear relationships that makes high model performance in teaching. Therefore, this combination makes both clarity and accurate performance in classroom dynamics. The proposed DT-LGBA achieves a high teaching evaluation effect of 96.38% compared to existing techniques like Random Forest (RF) and Support Vector Machine (SVM). |
|---|---|
| AbstractList | University teacher performance evaluation analyze educators depending on teaching effectiveness, student engagement, and research contributors. It aims to ensure continuous professional development and quality education. Moreover, it contains technology and different teaching approach for engaging students and rapid deeper learning. However, it has challenges of engaging students with different learning styles by managing efficient content delivery. This results in gap at student participation and comprehension that minimize overall learning outcomes. This research proposes Decision Tree with Light Gradient Boosting Algorithm (DT-LGBA) to evaluate the performance for university teachers. DT-LGBA enable for accurate determination of primary factors to teach performance by effectively managing both non-linear and linear relationship which provides reliable evaluation outcomes. DT makes easy interpretation to influence teacher performance because it presents clear decision rules whereas LGBA provides effective in managing intricate non-linear relationships that makes high model performance in teaching. Therefore, this combination makes both clarity and accurate performance in classroom dynamics. The proposed DT-LGBA achieves a high teaching evaluation effect of 96.38% compared to existing techniques like Random Forest (RF) and Support Vector Machine (SVM). |
| Author | Chen, Ming Li, Biao Zheng, Beibei Zhang, Lei Guo, Haolin Guo, Jianxun |
| Author_xml | – sequence: 1 givenname: Jianxun surname: Guo fullname: Guo, Jianxun email: 13821964098@163.com organization: Tianjin University of Technology and Education,Tianjin,China – sequence: 2 givenname: Beibei surname: Zheng fullname: Zheng, Beibei organization: Zhuobei Technology (Wuhan) Co., Ltd,Wuhan,China – sequence: 3 givenname: Lei surname: Zhang fullname: Zhang, Lei organization: Tianjin Data Development Center,Tianjin,China – sequence: 4 givenname: Biao surname: Li fullname: Li, Biao organization: Tianjin University of Technology and Education,Tianjin,China – sequence: 5 givenname: Haolin surname: Guo fullname: Guo, Haolin organization: Tianjin Academy of Fine Arts,Tianjin,China – sequence: 6 givenname: Ming surname: Chen fullname: Chen, Ming organization: Tianjin University of Technology and Education,Tianjin,China |
| BookMark | eNo1j0FLxDAUhCPoQdf9Bx6C961J06TJca3rulBQsJ6X1_alDbSppHFl_70F9TQwM3zM3JBLP3kk5J6zhHNmHg7FodgW70ryXCcpS2WyuEoLzi_I2uRGC8El05lU16R-wsbNbvK0Coj028Welq7rI90HaB36SB-naY7Od3Q7dFNYCiO1U6Af3p0wzC6eaYXQ9BjoG4YlGcE3SHcnGL4gLuRbcmVhmHH9pytSPe-q4mVTvu6XoeXGGRE3mGY2Qw5S101bg8U2Q7QAPMe60SyrLUoQtlEZkyjQgOJWoWjrtNXMcCtW5O4X6xDx-BncCOF8_H8ufgDx_1eC |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICICACS65178.2025.10968311 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Education |
| EISBN | 9798331508456 |
| EndPage | 5 |
| ExternalDocumentID | 10968311 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i93t-e24f4e1a58bcdbafed4eefaa17ebc804bfe5a3fc6405e3e9a61f6e3db2d8091f3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Apr 30 05:50:38 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-e24f4e1a58bcdbafed4eefaa17ebc804bfe5a3fc6405e3e9a61f6e3db2d8091f3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_10968311 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Feb.-21 |
| PublicationDateYYYYMMDD | 2025-02-21 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-Feb.-21 day: 21 |
| PublicationDecade | 2020 |
| PublicationTitle | 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) |
| PublicationTitleAbbrev | ICICACS |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8995433 |
| Snippet | University teacher performance evaluation analyze educators depending on teaching effectiveness, student engagement, and research contributors. It aims to... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Accuracy Boosting decision tree Decision trees Education Integrated circuit modeling learning light gradient boosting algorithm Performance evaluation Radio frequency Random forests Reliability students Support vector machines university teacher performance evaluation |
| Title | Decision Tree with Light Gradient Boosting Algorithm for University Teacher Performance Evaluation |
| URI | https://ieeexplore.ieee.org/document/10968311 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LT8MgGCe6ePDka8Z3OHjtXEtb4DjrpkvMssQddlv44MMs0dV0nX-_wLotHjx4ayiEhOcH_B6E3Ls1EcBqcBMJRZRKlBEIkUVePR8TxVWsTTCb4KORmE7luCGrBy4MIgbwGXb8Z3jLN6Ve-asyN8NlLphn8u5znq_JWo2QqPv1MCyGRa94y7OYe9BWknU2BX5Zp4SdY3D0zzqPSXvHwaPj7e5yQvZwceo9lhs8xhmBp8Yeh04qROovVOmrP2rT5yrguGr6WJZLD2umvY_3snIZPqmLUekOjEEbQWc63vEHaH8rAN4mk0F_UrxEjWNCNJesjjBJbYqxygRoA8qiSRGtUjFH0KKbgsVMMatzF6UhQ6ny2ObIDCRGuLjBsnPSWpQLvCAUuPYPnhYSm6TcGNmFxA06bhhYAcxekrZvq9nXWhNjtmmmqz_Sr8mh75FABo9vSKuuVnhLDvR3PV9Wd6EnfwBxaKSx |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LT8IwGG8MmujJF8a3PXgdsrbbuiNOECISEnfgRvr4akiUmTH8-23LgHjw4G3p2jTp82v7eyB0b9dEKY2SdiIBD1gKaSA5jwKnng9EJCJU2ptNJKMRn0zScU1W91wYAPDgM2i5T_-Wrwu1dFdldoanMaeOybsbMUbaK7pWLSVqfz4MskHWyd7iKEwcbItErXWRX-Ypfu_oHf6z1iPU3LLw8HizvxyjHZifOJflGpFxiuRTbZCD8xIAuytVPHSHbfxceiRXhR-LYuGAzbjz8V6UNsMntlEq3sIxcC3pjMdbBgHubiTAmyjvdfOsH9SeCcEspVUAhBkGoYi4VFoKA5oBGCHCBKTibSYNRIIaFds4DSikIg5NDFRLormNHAw9Q415MYdzhGWi3JOnkcQQlmidtiWxwy7RVBouqblATddW06-VKsZ03UyXf6Tfof1-_jqcDgejlyt04HrHU8PDa9SoyiXcoD31Xc0W5a3v1R8rn6f4 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2025+3rd+International+Conference+on+Integrated+Circuits+and+Communication+Systems+%28ICICACS%29&rft.atitle=Decision+Tree+with+Light+Gradient+Boosting+Algorithm+for+University+Teacher+Performance+Evaluation&rft.au=Guo%2C+Jianxun&rft.au=Zheng%2C+Beibei&rft.au=Zhang%2C+Lei&rft.au=Li%2C+Biao&rft.date=2025-02-21&rft.pub=IEEE&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FICICACS65178.2025.10968311&rft.externalDocID=10968311 |