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Students were recruited via simple random sampling from secondary educational service areas across Nakhon Sawan, Uthai Thani, Chainat, and Pichit, Thailand, yielding 510 questionnaire participants and 117 evaluation-platform users. Three assessments supported platform development: SPST-20, 9Q depression form, and an 8Q death form. Comparative analytics across five techniques identifies the most accurate results and supports future school monitoring systems.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-machine-learning-techniques-to-study-of-stress-depression-and-academic-performance-of-grade-12th-students/593297/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-machine-learning-techniques-to-study-of-stress-depression-and-academic-performance-of-grade-12th-students/593297.png","ImageObject",300,407,{"name":92,"@type":93},"Elsa","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What were the main objectives of the study?","Question",{"text":112,"@type":113},"The study aimed to examine stress and depression problems among Grade 12 students, apply machine learning techniques to analyze and predict stress, depression, and academic performance, and evaluate a stress and depression prediction platform.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were participants recruited and what were the sample sizes?",{"text":117,"@type":113},"Students from schools in several Thai secondary educational service areas were recruited using simple random sampling. 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[Email:](Email: wudhijaya@nsru.ac.th)[ ](Email: wudhijaya@nsru.ac.th)[wudhijaya@nsru.ac.th](Email: wudhijaya@nsru.ac.th)  \nAbstract  \nThe objectives of this research were 1) to study the problems of stress and depression among Grade 12 students; 2) to investigate the machine learning technique in analyzing and predicting stress, depression, and academic performance among Grade 12 students; and 3) to evaluate the stress and depression prediction platform. Students from schools in the Secondary Educational Service Areas of Nakhon Sawan, Uthai Thani, Chainat, and Pichit in Thailand comprised the sampling group, which the researchers recruited through simple random sampling. A total of 510 students volunteered to participate in the questionnaire, while 117 students served as the group for the evaluation platform. The researcher employed a total of three assessments: 1) the Stress Assessment Scale (SPST-20), 2) the Depression Assessment Form 9 Questions (9Q), and 3) the Death Assessment Form (8Q) to gather data and develop the platform. The data analytics process incorporates all assessment results, comparing five techniques to yield the most accurate results. The article presents the research results, which will guide the development of a system and tools for future student monitoring in schools.  \nKeywords: Stress, Depression, Data analytics, Machine learning techniques, Platform, Grade 12th students, Thailand.  \nCitation | Philuek, W. (2024). Using machine learning techniques to study of stress, depression, and academic performance of grade 12th students. Asian Journal of Education and Training, 10(3), 146–153. 10.20448/edu.v10i3.5885  \nHistory:  \nReceived: 12 December 2023  \nRevised: 26 June 2024  \nAccepted: 17 July 2024  \nPublished: 15 August 2024  \nLicensed: This work is licensed under a Creative Commons Attribution 4.0 License   \nPublisher: Asian Online Journal Publishing Group  \nFunding: This research is supported by Nakhon Sawan Rajabhat University  \n(Grant number: Edu66-Com03) .  \nInstitutional Review Board Statement: The Ethical Committee of the Nakhon Ratchasima Rajabhat University, Thailand has granted approval for this study on 30 January 2023 (Ref. No. HE-014-2566).  \nTransparency: The author confirms that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing.  \nCompeting Interests: The author declares that there are no conflicts of interests regarding the publication of this paper.  \nContents  \n1. Introduction .................................................................................................................................................................................... 147  \n2. Methodology ................................................................................................................................................................................... 147  \n3. Results .............................................................................................................................................................................................. 150  \n4. Discussions ...................................................................................................................................................................................... 152  \nReferences ................................................................................................","cbCaipQxmplLnr5d","https://ap.wps.com/l/cbCaipQxmplLnr5d","pdf",490740,"English","# Introduction\n# Methodology\n# Results\n# Discussions\n# References","[{\"question\":\"What were the main objectives of the study?\",\"answer\":\"The study aimed to examine stress and depression problems among Grade 12 students, apply machine learning techniques to analyze and predict stress, depression, and academic performance, and evaluate a stress and depression prediction platform.\"},{\"question\":\"How were participants recruited and what were the sample sizes?\",\"answer\":\"Students from schools in several Thai secondary educational service areas were recruited using simple random sampling. A total of 510 students completed the questionnaire, and 117 students were used for evaluating the platform.\"},{\"question\":\"Which assessment tools were used to collect the research data?\",\"answer\":\"The study used three assessments: the Stress Assessment Scale (SPST-20), the Depression Assessment Form 9 Questions (9Q), and the Death Assessment Form (8Q) to gather data and develop the platform.\"}]","Using machine learning techniques to study of stress, depression, and academic performance of grade 12th students | PDF",1792077003]