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It targets computer engineering undergraduate students and uses Educational Data Mining, a subset of Machine Learning, to extract patterns from large educational datasets. Performance is evaluated using three aspects: final grades, study duration, and next term course grade. Results indicate that SVM and decision tree are the most effective algorithms, while final course grades emerge as the most valuable predictive factor.",{"@graph":69,"@context":123},[70,84,106],{"@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/predicting-students-academic-performance-using-machine-learning-techniques-master-of-science-thesis/128839/",{"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/predicting-students-academic-performance-using-machine-learning-techniques-master-of-science-thesis/128839.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-20","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the study?","Question",{"text":113,"@type":114},"To predict students’ academic performance using the most accurate data mining and machine learning algorithms, and to identify factors influencing performance for undergraduate computer engineering students.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which algorithms and performance aspects are evaluated?",{"text":118,"@type":114},"SVM and decision tree are evaluated, and student performance is analyzed through final grades, study duration, and next term course grade.",{"name":120,"@type":111,"acceptedAnswer":121},"What factor is found to be most valuable for prediction?",{"text":122,"@type":114},"Final course grades are determined to be the most valuable factor for predicting academic performance.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128839,1786003808,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","M . DOO ST ATILIM UNIVERSITY 2023  \nPREDICTING STUDENTS’ ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES  \nTHE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES  \nOF  \nATILIM UNIVERSITY  \nMIRWAIS DOOST  \nA MASTER OF SCIENCE THESIS IN  \nTHE DEPARTMENT OF SOFTWARE ENGINEERING  \nPREDICTING STUDENTS’ ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES  \nA THESIS SUBMITTED TO  \nTHE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES  \nOF  \nATILIM UNIVERSITY  \nBY  \nMIRWAIS DOOST  \nIN PARTIAL FULFILLMENT OF THE REQUIREMENTS  \nFOR  \nTHE DEGREE OF MASTER OF SCIENCE IN  \nTHE DEPARTMENT OF SOFTWARE ENGINEERING  \nApproval of the Graduate School of Natural and Applied Sciences, Atilim University.  \nProf. Dr. Ender KESKİNKILIÇ Director  \nI certify that this thesis satisfies all the requirements as a thesis for the degree of  \nMaster of Science in Software Engineering, Atılım University.  \nProf. Dr. Ali Yazıcı Head of Department  \nThis is to certify that we have read the thesis PREDICTING STUDENTS’ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES submitted by MIRWAIS DOOST and that in our opinion it is fully adequate, in scope and quality, as a thesis for the degree of Master of Science.  \nAssoc. Prof. Dr. Cansu Çiğdem EKİN Supervisor  \nExamining Committee Members:  \nAsst. Prof. Dr. Güzin TÜRKMEN  \nComputer Eng. Department, Atilim University    \nAssoc. Prof. Dr. Cansu Çiğdem EKİN  \nComputer Eng. Department, Atilim University    \nAssoc. Prof. Dr. Elif POLAT HOPCAN Department of Computer Education and  \nInstructional Technologies, Istanbul University    \nDate: 11.09.2023  \nI hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.  \nName, Last Name: Mirwais Doost  \nSignature:  \nABSTRACT  \nPREDICTING STUDENT ACADEMIC PERFORANCE USING MACHINE LEARNING TECHNIQUES  \nDoost, Mirwais  \nMSc., Department of Software Engineering Supervisor: Assoc. Prof. Dr. Cansu Çiğdem EKİN  \nSeptember 2023, 94 pages  \nRecently education sectors have the most attraction from people all around the world and this make it more valuable for whom wants to invest in this sector and make income. Therefore, there are too much efforts to make this domain more stable. Students are the largest stakeholders in this area, in this reason they need more attention in educational settings. All universities are trying to make their better quality for achieving their students’ satisfaction. Because the quality of education is depended on success rate of students and capability of the institute for retaining its students, predicting student’s performance is a way to identify the students who are at risk of failure, so management can make decision for improving students’performance. These analyzes can be done EDM (Educational Data Mining) a subset of ML (Machine Learning) that is able to discover very large datasets for producing valuable results. The main purpose of this study is to predict students’ academic performance using most accurate data mining algorithms and determine the factors which influence the performance of computer engineering students in undergraduate level. The student academic performances were analyzed in three different aspects as Final Grades, Study Duration, and Next Term Course Grade. Our results shows that SVM (Support Vector Machine) and DT (Decision Tree) are the two best ML algorithms and also, we determined that only Final Course grades are the most valuable factors in prediction.  \nKeywords: Machine Learning, Data Mining, Educational Data Mining, Students’Success Rate.  \nÖZ  \nMAKİNE ÖĞRENME TEKNİKLERİNİ KULLANARAK ÖĞRENCİNİNAKADEMİK PERFORMANSININ TAHMİN EDİLMESİ  \nDoost, Mirwais  \nYüksek Lisans, Yazılım Mühendisliği Bölümü Danışman: Assoc. Prof. Dr. Cansu Çiğdem EKİN  \nEylül 2023, 94 sayfa  \nSon dönemde eğitim sek","cbCaiccEdJIuHU7d","https://ap.wps.com/l/cbCaiccEdJIuHU7d","pdf",4093817,112,"English","# Abstract\n## Keywords\n## Purpose and approach\n## Data mining and evaluation aspects\n## Results and predictive factors\n## Ethics and declarations","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To predict students’ academic performance using the most accurate data mining and machine learning algorithms, and to identify factors influencing performance for undergraduate computer engineering students.\"},{\"question\":\"Which algorithms and performance aspects are evaluated?\",\"answer\":\"SVM and decision tree are evaluated, and student performance is analyzed through final grades, study duration, and next term course grade.\"},{\"question\":\"What factor is found to be most valuable for prediction?\",\"answer\":\"Final course grades are determined to be the most valuable factor for predicting academic performance.\"}]","Predicting Students’ Academic Performance Using Machine Learning Techniques - Master of Science Thesis | PDF",282]