[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121573-en":3,"doc-seo-121573-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121573,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Examining Student Psychology Profile Modeling Methods Based on Machine Learning Programme","Study proposes a global perspective for student profile modeling and examines how computer-aided education (CAE) supports predicting failure, dropout, and academic achievement. Theoretical discussion highlights decision-tree use as a common approach while incorporating personal and online behavioral aspects for student rating. An experiment reinforces survey outcomes by applying up-to-date machine learning techniques to the same datasets, yielding the best results with decision trees. Findings link ICT-driven online learning growth to e-recommendation, recruiting, and tailored student services.","Examining Student Psychology Profile Modeling Methods Based on  \nMachine Learning Programme  \nYusmi Mohd Yunus1, Siti Sarah Maidin2, Norazam Aliman3, Mohd Ab Malek Md Shah4, Zaki Ahmad Dahlan5, Mohd Fazril Mohd Idris6, Izzat Fadhli Hamdan7  \n1PhD in Health Sciences, Faculty of Health Sciences, Universiti Selangor, Selangor, [Malaysia. Email: yusmiyunus@unisel.edu.my](Malaysia. Email: yusmiyunus@unisel.edu.my),  \n2PhD in Information Technology, Faculty of Data Science and Information Technology, INTI International University, Nilai, Negeri Sembilan, Malaysia. Email: [sitisarah.maidin@newinti.edu.my](sitisarah.maidin@newinti.edu.my),  \n3PhD, Mechanical Engineering Department, Politeknik Sultan Azlan Shah, Behrang, Perak, Malaysia. Email: [azam.820731@gmail.com](azam.820731@gmail.com),  \n4PhD in Law, Department of Law, Universiti Teknologi MARA, Melaka Branch, Melaka City Campus, [Malaysia. Email: malek625@melaka.uitm.edu.my](Malaysia. Email: malek625@melaka.uitm.edu.my),  \n5Master of Sciences in Information Technology, Centre of Studies for Surveying Science & Geomatics, College of Built Environment, Universiti Teknologi MARA, Perlis Branch, Arau Campus, Malaysia. Email: [zaki@uitm.edu.my](zaki@uitm.edu.my) ,  \n6Master of Science (Mathematics), School of Mathematical Science, College of Computing, Informatics and Media, Universiti Teknologi MARA, Perlis Branch, Arau Campus, Malaysia. Email: [fazrilizhar@uitm.edu.my](fazrilizhar@uitm.edu.my) ,  \n7Master in Education, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Selangor, Malaysia. Email: [gs61765@student.upm.edu.my](gs61765@student.upm.edu.my) ,  \nReceived: 23-June-2023  \nRevised: 23-July-2023  \nAccepted: 06-August-2023  \nAbstract  \nPropose: This study aims to analyzed global perspective of the student profile has a global perspective model may be created.  \nTheoretical Framework: Computer-aided education (CAE) techniques used for various purposes, such as predicting failure, dropout, and academic achievement, have to be researched, and the prominent features employed. The Decision Tree is the most commonly used method in research studies. Personal and online behavioral aspects are taken into account when students are rated.  \nMethodology: An experiment was conducted to reinforce the survey results using the most recent machine learning techniques applied to the same datasets.  \nFindings: According to the survey results, the best outcomes were achieved using a decision tree.  \nImplications: ICT developments have led to an increase in online learning and the development of erecommendation services, online recruiting, and other educational tools.  \nValue: Advances in Machine Learning make it possible to tailor services for students based on their specific requirements and preferences. Student profile modeling has come a long way in the previous four years thanks to advances in machine learning approaches, which we detail in this work.  \nKeywords: Machine Learning, Student Profile, Artificial Intelligence, Decision Tree  \n1. Introduction  \nTheir profiles must capture their most important traits logically, completely, and practically to model students effectively. It's critical to understand the student's educational history and learning style. Student data, social media, e-learning platforms, and web forms can all be utilized to build student profiles. To recommend a course of study or advise the student on a future career route. Machine learning (ML) is one technology utilized for student profile modeling. Classification, forecasting, and decision-making are all areas in which they are frequently employed (Hamim et al., 2021) . However, as artificial intelligence has progressed, other classifications have emerged, such as \"deep,\" \"transfer,\" and \"reinforcement\" learning, in addition to the more traditional \"supervised,\" \"unsupervised,\" and \"semi-supervised.\" Predicting failure and dropping out of school  \nand providing guidance and making academic decisions w","cbCaigvTdH4AgSnM","https://ap.wps.com/l/cbCaigvTdH4AgSnM","pdf",293452,1,10,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n# Findings\n# Implications","[{\"question\":\"What is the main goal of the study on student profile modeling?\",\"answer\":\"The study aims to examine student profile modeling methods from a global perspective and support creating a global perspective model.\"},{\"question\":\"Which machine learning approach produces the best outcomes in the reported results?\",\"answer\":\"The decision tree approach achieves the best outcomes according to the survey-reinforced experimental findings.\"},{\"question\":\"How do online and personal behavioral aspects affect the modeling process?\",\"answer\":\"Personal and online behavioral aspects are considered when students are rated, supporting more informed profile modeling and related educational decisions.\"}]","Examining Student Psychology Profile Modeling Methods Based on Machine Learning Programme | 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is the main goal of the study on student profile modeling?","Question",{"text":75,"@type":76},"The study aims to examine student profile modeling methods from a global perspective and support creating a global perspective model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approach produces the best outcomes in the reported results?",{"text":80,"@type":76},"The decision tree approach achieves the best outcomes according to the survey-reinforced experimental findings.",{"name":82,"@type":73,"acceptedAnswer":83},"How do online and personal behavioral aspects affect the modeling process?",{"text":84,"@type":76},"Personal and online behavioral aspects are considered when students are rated, supporting more informed profile modeling and related educational 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