[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121293-en":3,"doc-seo-121293-105":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":20,"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},121293,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning-Based Teacher Education Student Placement Model - Via Interest Profile and Diagnostic Test","Traditional college placement based on academic performance, interviews, and student choice may produce suboptimal matches for teacher education majors. This study introduces a machine learning-based placement model that combines students’ interest profile data and diagnostic test outcomes across specializations. Data from 208 freshmen collected in AY 2024–2025 were analyzed using validated interest questionnaires and diagnostic tests. Multiple classifiers were compared, and the final model used ANN, SVM, gradient boosting, and adaptive boosting, each reaching at least 80% accuracy and strong F1 scores.","Machine Learning-Based Teacher Education Student Placement Model Via Interest Profile and Diagnostic Test  \nJohn Ben S. Temones 1􀀍 , Edwin M. Codecio Jr. 2, Jay Christian A. De La Cruz 3, Lalaine R. Domanais 4, Michael Gerald R. Llonado 5, & Anthony Jay D. Timado 6  \n(1) College of Arts and Sciences, Central Bicol State University of Agriculture, Philippines (2,3,4,5,6) College of Development Education, Central Bicol State University of Agriculture,  \nPhilippines  \n􀀍 Corresponding author  \n[[johnben.temones@cbsua.edu.ph](johnben.temones@cbsua.edu.ph)]  \nAbstract  \nTraditional student placement in college programs based on academic performance, interviews, and student choice may not always yield optimal results. This study proposes a machine learningbased model for teacher education program placement, integrating student interests and diagnostic test results across various specializations. Data from 208 freshmen in a teacher education institution (AY 2024-2025) were collected using a validated interest profile questionnaire and diagnostic test. Various machine learning methods were evaluated for classification performance. Results showed that most students exhibited strong interest in their chosen specialization, highlighting interest as a key placement factor. Diagnostic test performance trends further indicated that students tend to excel in their respective fields. The final placement model employed artificial neural networks, support vector machines, gradient boosting, and adaptive boosting, each achieving at least 80% classification accuracy and F1 score. This model offers a systematic and data-driven approach to optimizing teacher education student placement.  \nKeywords: Student placement, teacher education, machine learning.  \nINTRODUCTION  \nChoosing a major in college is an important step in progressing one’s career plans. College programs determine most students’ lives and what they become in the near future (Cuy & Salinas, 2019) . In higher education institution, most students answer the question “what program should Itake?” solely based on their preference, which is sometimes affected by peers, family, career opportunities, financial stability and other factors (Del Rosario, et al., 2024) . This student-based placement system for college admission presents issues that may affect both the students and the higher education institution they plan to attend to. Placement systems for students taking majorsin college institutions mostly rely on static criteria such as academic performance in high school, college admission test scores, college interviews, and student choice. This traditional method of placement in college is inflexible in guiding a student towards the best college pathway for them, as it overlooks the dynamics of student interest profiles and a more specific overview of student qualification.  \nPersonal and academic interests of students are one of the key factors in choosing their desired program and specialization. Parsons (1909) asserted in his theory that students assess themselves with factors like interests, characteristics, and capabilities in mind. Hence, when students declare that they take interest in taking a certain program or specialization, they tend tobe led to an in-depth assessment of themselves. As stated in the Social Cognitive Career Theory of Hackett et al. (1981) & Lent et al. (1994), the academic interests of students reflect their beliefs in their self-efficacy and how they perceive outcomes that they come up on their own. This suggests that students who pin their interest on an academic pathway, at some point reflect their own capabilities.  \nDespite having personal and academic interest as necessary determinants in choosing a program and/or specialization, the students’ academic competencies, specifically in their preferred specialization, are matters that the current system of admission fails to consider. Some students may take interest in taking a particular specialization whilst ","cbCaicnTuZGps2BB","https://ap.wps.com/l/cbCaicnTuZGps2BB","pdf",1017928,1,10,"English","en",105,"# Introduction\n## Background of student placement systems\n## Role of interests in choosing majors\n## Limitations of current admission based on competencies\n## AI and machine learning in education","[{\"question\":\"What problem does the study address in traditional teacher education student placement?\",\"answer\":\"Traditional placement relying on academic performance, interviews, and choice may not consistently produce optimal program matches because it overlooks student interest dynamics and more specific qualification information.\"},{\"question\":\"How does the proposed model improve placement decisions?\",\"answer\":\"The model integrates interest profile results and diagnostic test outcomes, using machine learning classifiers to predict the appropriate specialization based on observed patterns.\"},{\"question\":\"Which machine learning methods were used and how accurate was the model?\",\"answer\":\"The final placement model used artificial neural networks, support vector machines, gradient boosting, and adaptive boosting, with each achieving at least 80% classification accuracy and F1 score.\"}]","Machine Learning-Based Teacher Education Student Placement Model - Via Interest Profile and Diagnostic Test | PDF",1785734943,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-teacher-education-student-placement-model-via-interest-profile-and-diagnostic-test","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-teacher-education-student-placement-model-via-interest-profile-and-diagnostic-test/121293/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in traditional teacher education student placement?","Question",{"text":75,"@type":76},"Traditional placement relying on academic performance, interviews, and choice may not consistently produce optimal program matches because it overlooks student interest dynamics and more specific qualification information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed model improve placement decisions?",{"text":80,"@type":76},"The model integrates interest profile results and diagnostic test outcomes, using machine learning classifiers to predict the appropriate specialization based on observed patterns.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods were used and how accurate was the model?",{"text":84,"@type":76},"The final placement model used artificial neural networks, support vector machines, gradient boosting, and adaptive boosting, with each achieving at least 80% classification accuracy and F1 score.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]