[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119699-en":3,"doc-seo-119699-105":30,"detail-sidebar-cat-0-en-105":92},{"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},119699,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","The Role of Machine Learning in Identifying Students At-Risk and Minimizing Failure","Education planning relies on timely support for students whose performance is trending downward. This research addresses the challenge that at-risk learners are often identified too late, limiting teachers’ ability to intervene. Using a hybrid ensemble stacking approach, the study predicts at-risk students with multiple machine learning algorithms and evaluates them through several metrics. Stratified k-fold cross validation and hyperparameter optimization improve performance. Results reach 94.8% accuracy with demographic plus academic features and 98.4% with academic features alone, enabling earlier assistance.","Received 8 December 2022, accepted 24 December 2022, date of publication 28 December 2022, date of current version 5 January 2023. Digital Object Identifier 10.1109/ACCESS.2022.3232984  \nThe Role of Machine Learning in Identifying Students At-Risk and Minimizing Failure  \nREYHAN ZEYNEP PEK1, SIBEL TARIYAN ÖZYER2, TAREK ELHAGE3, TANSEL ÖZYER2, AND REDA ALHAJJ1,4,5,(Senior Member, IEEE)  \n1Department of Computer Engineering, Istanbul Medipol University, 34810 Istanbul, Turkey  \n2Department of Computer Engineering, Ankara Medipol University, 06050 Ankara, Turkey  \n3ABC Private School, Abu Dhabi, United Arab Emirates  \n4Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada  \n5Department of Heath Informatics, University of Southern Denmark, 5230 Odense, Denmark Corresponding author: Reda Alhajj (alhajj@ucalgary.ca)  \nThis work was supported in part by The Scienti􀀜c and Technological Research Institution of Turkey (TUBITAK) under Program Grant 2209-A.  \nABSTRACT Education is very important for students' future success. The performance of students can be supported by the extra assignments and projects given by the instructors for students with low performance. However, a major problem is that students at-risk cannot be identi􀀜ed early. This situation is being investigated by various researchers using Machine Learning techniques. Machine learning is used in a variety of areas and has also begun to be used to identify students at-risk early and to provide support by instructors. This research paper discusses the performance results found using Machine learning algorithms to identify at-risk students and minimize student failure. The main purpose of this project is to create a hybrid model using the ensemble stacking method and to predict at-risk students using this model. We used machine learning algorithms such as Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, AdaBoost Classi􀀜er and Logistic Regression in this project. The performance of each machine learning algorithm presented in the project was measured with various metrics. Thus, the hybrid model by combining algorithms that give the best prediction results is presented in this study. The data set containing the demographic and academic information of the students was used to train and test the model. In addition, a web application developed for the effective use of the hybrid model and for obtaining prediction results is presented in the report. In the proposed method, it has been realized that strati􀀜ed k-fold cross validation and hyperparameter optimization techniques increased the performance of the models. The hybrid ensemble model was tested with a combination of two different datasets to understand the importance of the data features. In 􀀜rst combination, the accuracy of the hybrid model was obtained as 94.8% by using both demographic and academic data. In the second combination, when only academic data was used, the accuracy of the hybrid model increased to 98.4% . This study focuses on predicting the performance of at-risk students early. Thus, teachers will be able to provide extra assistance to students with low performance.  \nINDEX TERMS At-risk students, classi􀀜cation, dropout prediction, hybrid model, machine learning techniques, stacking ensemble model, student performance prediction.  \nI. INTRODUCTION  \nSome students may fail their courses during the semester due to various problems such as psychological reasons, family situation, friend environment or not getting enough support from the teachers. The school success of such students is  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Alberto Cano  .  \nat risk. Early intervention is required by teachers to identify students at risk and to support the educational status of these students. Early prediction of students' achievement performance can help instructors identify those students who nee","cbCaieaVG1cTEqA6","https://ap.wps.com/l/cbCaieaVG1cTEqA6","pdf",3914971,1,20,"English","en",105,"# Abstract\n# Introduction\n## Problem of late identification and need for early intervention\n# Proposed Hybrid Model\n## Ensemble stacking approach\n## Algorithms and evaluation metrics\n# Experimental Setup\n## Dataset and training/testing\n## Stratified k-fold cross validation and hyperparameter optimization\n# Results and Discussion\n## Two dataset feature settings and accuracy outcomes\n# Web Application for Prediction","[{\"question\":\"What problem does the study address regarding student risk?\",\"answer\":\"At-risk students are difficult to identify early, which delays teacher intervention. The research focuses on enabling earlier prediction to support students with low performance.\"},{\"question\":\"How does the proposed approach predict at-risk students?\",\"answer\":\"It builds a hybrid ensemble model using an ensemble stacking method and combines predictions from multiple machine learning algorithms.\"},{\"question\":\"What accuracy results are reported for different feature settings?\",\"answer\":\"With demographic plus academic data, the hybrid model achieves 94.8% accuracy. With academic data only, accuracy increases to 98.4%.\"}]","The Role of Machine Learning in Identifying Students At-Risk and Minimizing Failure | PDF",1785725833,50,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-role-of-machine-learning-in-identifying-students-at-risk-and-minimizing-failure","",{"@graph":36,"@context":86},[37,54,69],{"@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/the-role-of-machine-learning-in-identifying-students-at-risk-and-minimizing-failure/119699/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address regarding student risk?","Question",{"text":76,"@type":77},"At-risk students are difficult to identify early, which delays teacher intervention. The research focuses on enabling earlier prediction to support students with low performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed approach predict at-risk students?",{"text":81,"@type":77},"It builds a hybrid ensemble model using an ensemble stacking method and combines predictions from multiple machine learning algorithms.",{"name":83,"@type":74,"acceptedAnswer":84},"What accuracy results are reported for different feature settings?",{"text":85,"@type":77},"With demographic plus academic data, the hybrid model achieves 94.8% accuracy. 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