[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122555-en":3,"doc-seo-122555-105":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},122555,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods - research paper","The study addresses the growing need for data-supported guidance in IT education by predicting specialization preferences among IT students in Kazakhstan. A predictive model is developed to improve academic advising using academic performance, personality traits, qualifications, and extracurricular involvement. Using 692 anonymized student profiles, five machine learning algorithms are compared with stratified 10-fold cross-validation. Gradient Boosting achieves the best validation accuracy, though its test performance indicates overfitting risk. Statistical analysis highlights significant effects of academic performance and extracurricular activities on specialization selection, suggesting that ML-based recommendations can improve decision accuracy and career alignment.","Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine  \nLearning Methods  \nBauyrzhan Berlikozha 1, Azamat Serek 2*, Tamara Zhukabayeva 3, Azamat Zhamanov 4, Oliver Dias 5  \n1 Department of Information Systems, SDU University, Kaskelen, Kazakhstan  \n2 School of Information Technology and Engineering, Kazakh-British Technical University (KBTU), Almaty, Kazakhstan  \n3 Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan  \n4 North American University, USA, Houston Texas  \n5 Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain [Email:](Email:1 bauirzhan.berlikozha@sdu.edu.kz)[1](Email:1 bauirzhan.berlikozha@sdu.edu.kz)[ bauirzhan.berlikozha@sdu.edu.kz](Email:1 bauirzhan.berlikozha@sdu.edu.kz), [2](2 a.serek@kbtu.kz)[ a.serek@kbtu.kz](2 a.serek@kbtu.kz), [3](3 tamara_kokenovna@mail.ru)[ tamara_kokenovna@mail.ru](3 tamara_kokenovna@mail.ru), [4](4 azhamanov@na.edu)[ azhamanov@na.edu](4 azhamanov@na.edu),  \n[5](5 oliver.diaz@ub.edu)[ oliver.diaz@ub.edu](5 oliver.diaz@ub.edu)  \n*Corresponding Author  \nAbstract—The growing intricacy of IT education requires resources to aid students in choosing specialized pathways. This study investigates the prediction of specialization preferences among IT students at SDU University in Kazakhstan through the application of machine learning techniques. The research contribution is the development of a predictive model that enhances academic advising by incorporating multiple factors, including academic performance, personality traits, qualifications, and extracurricular involvement. The research examined 692 anonymized student profiles and evaluated the efficacy of five machine learning algorithms: Random Forest, KNearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes. Stratified 10-fold cross-validation was utilized to reduce the risk of overfitting. Gradient Boosting attained a peak accuracy of 99.10% in validation; however, its performance decreased to 92.16% on an independent test set, suggesting overfitting. Naive Bayes exhibited the lowest accuracy, recorded at 35.26%. Logistic regression analysis indicated a statistically significant correlation (p \u003C 0.05) among academic performance, extracurricular involvement, and specialization selection. Personality traits and certifications significantly influenced the prediction process. The findings suggest that although Gradient Boosting demonstrates high effectiveness, the associated risk of overfitting requires additional refinement for practical application. The notable impact of academic performance and extracurricular activities indicates that educational institutions ought to prioritize these elements in student guidance. The incorporation of machine learning-based recommendations into advising frameworks enhances the precision of specialization predictions, thereby improving student decision-making and career alignment.  \nKeywords—Educational Prediction; Machine Learning in Education; Artificial Intelligence in Education; Prediction Systems in Education.  \nI. INTRODUCTION  \nRapid growth in the information technology (IT) sector has increased the need for specialist education in software engineering, data science, cybersecurity, and IT management [1]-[3] . Universities help students choose an academic focus as these subjects expand and diversify [4]-[6] . With so many specialized options, educational institutions must give focused support to help students navigate this process and  \nchoose pathways that match their skills, interests, and professional aspirations. This counsel is crucial since students' school choices might affect their career success, employment contentment, and overall fulfillment [7]-[9] . The correct educational pathway gives students the skills they need and a sense of purpose, helping them succeed professionally and personally [10]-[12] .  \nThere exists a significant gap in the litera","cbCaipLJg06zwHl5","https://ap.wps.com/l/cbCaipLJg06zwHl5","pdf",691957,1,11,"English","en",105,"# Introduction\n## Rationale for ML-based student guidance\n## Literature gap and ethical considerations\n# Abstract and Keywords\n## Study contribution and evaluation setup","[{\"question\":\"What problem does the study address in IT education?\",\"answer\":\"It targets the challenge of providing students with effective, data-driven support when choosing IT specialization pathways, where existing tools and analyses are insufficient.\"},{\"question\":\"Which features and algorithms are used in the prediction model?\",\"answer\":\"The model incorporates academic performance, personality traits, qualifications, and extracurricular involvement, and it evaluates Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes.\"},{\"question\":\"How do the models perform and what does the study conclude?\",\"answer\":\"Gradient Boosting reaches 99.10% peak validation accuracy but drops to 92.16% on an independent test set, indicating overfitting. Naive Bayes performs worst at 35.26%, and the findings suggest additional refinement and stronger advising focus on academic performance and extracurricular activities.\"}]","Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods - research paper | PDF",1785811264,28,{"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},"development-of-method-to-predict-career-choice-of-it-students-in-kazakhstan-by-applying-machine-learning-methods-research-paper","",{"@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/development-of-method-to-predict-career-choice-of-it-students-in-kazakhstan-by-applying-machine-learning-methods-research-paper/122555/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"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 IT education?","Question",{"text":75,"@type":76},"It targets the challenge of providing students with effective, data-driven support when choosing IT specialization pathways, where existing tools and analyses are insufficient.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which features and algorithms are used in the prediction model?",{"text":80,"@type":76},"The model incorporates academic performance, personality traits, qualifications, and extracurricular involvement, and it evaluates Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the models perform and what does the study conclude?",{"text":84,"@type":76},"Gradient Boosting reaches 99.10% peak validation accuracy but drops to 92.16% on an independent test set, indicating overfitting. Naive Bayes performs worst at 35.26%, and the findings suggest additional refinement and stronger advising focus on academic performance and extracurricular activities.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]