[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117927-en":3,"doc-seo-117927-105":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},117927,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Student Attrition Prediction Using Machine Learning Techniques","Educational systems rely on course enrollment as a key indicator of academic and financial sustainability, yet student attrition persists due to demographic and personal factors including age, gender, academic background, financial capacity, and chosen degree. This study applies machine learning to build prediction models for attrition in a computer science program and to flag students at high risk of dropping out before graduation. Data from the Federal University Lokoja (FUL) are preprocessed with Weka, converted to ARFF, and evaluated using resampling with training/testing splits, correlation-based feature selection, and random forest and random tree algorithms.","Student Attrition Prediction Using Machine Learning  \nTechniques  \nDoris Chinedu Asogwaa*, Emmanuel Chibuogu Asogwab, Emmanuel Chinedu Mbonuc, Joshua Makuochukwu Nwankpad, Tochukwu Sunday Belonwue  \na,b,c,d,e Nnamdi Azikiwe University, Awka, Anambra State, 234, Nigeria [a](aEmail: dc.asogwa@unizik.edu.ng)[Email: dc.asogwa@unizik.edu.ng](aEmail: dc.asogwa@unizik.edu.ng), bEmail: [ec.asogwa@unizik.edu.ng](ec.asogwa@unizik.edu.ng), cEmail: [ec.mbonu@unizik.edu.ng](ec.mbonu@unizik.edu.ng)  \n[d](dEmail: jm.nwankpa@unizik.edu.ng)[Email: jm.nwankpa@unizik.edu.ng](dEmail: jm.nwankpa@unizik.edu.ng), eEmail: [ts.belonwu@unizik.edu.ng](ts.belonwu@unizik.edu.ng)  \nAbstract  \nIn educational systems, students’ course enrollment is fundamental performance metrics to academic and financial sustainability. In many higher institutions today, students’ attrition rates are caused by a variety of circumstances, including demographic and personal factors such as age, gender, academic background, financial abilities, and academic degree of choice. In this study, machine learning approaches was used to develop prediction models that predicted students’ attrition rate in pursuing computer science degree, as well as students who have a high risk of dropping out before graduation. This can help higher education institutes to develop proper intervention plans to reduce attrition rates and increase the probability of student academic success. Student’s data were collected from the Federal University Lokoja (FUL), Nigeria. The data were preprocessed using existing weka machine learning libraries where the data was converted into attribute related file form (arff) and resampling techniques was used to partition the data into training set and testing set. The correlationbased feature selection was extracted and used to develop the students’ attrition model and to identify the students’ risk of dropping out. Random forest and random tree machine learning algorithms were used to predict students' attrition. The results showed that the random forest had an accuracy of 79.45%, while the random tree's accuracy was 78.09% . This is an improvement over previous results where 66.14% and 57.48% accuracy was recorded for random forest and random tree respectively. This improvement was as a result of the techniques used. It is therefore recommended that applying techniques to the classification model can improve the performance of the model.  \nKeywords: Machine learning; Predictive model; Random Forest; Random Tree algorithm; Student Attrition; Feature selection method; (Java Virtual Machine (JVM); Netbeans Integrated Software Development Environment (IDE); Weka Tool;Weka Plugin.  \nReceived: 7/17/2023  \nAccepted: 8/22/2023  \nPublished: 9/2/2023  \n* Corresponding author.  \n1. Introduction  \nThe consequences of attrition in final year student today in our educational system are considerable, both for the individuals as well as the affected institutions. Indeed, attrition imposes costs on all parties involved, be it resources, time or money [1, 2] . Consequently, preventing educational attrition poses a major challenge to institutions of higher education [3] .  \nThe predicting of student attrition has generally been subjected to a machine leaning model as a way to make abetter decision. Machine learning model is a computer program or a specific of field of computer system, used to analyze data without explicit program. This approach can help an institution to reduce student attrition, by identifying and maintaining student relationships with the assistance of predictive data mining techniques proposed by [4] .  \nThe identification of risk cases constitutes the first step to improving retention policies such as learning assistance or mentorship program. Quantifying attrition risks may prove to be helpful in allocating pedagogical, psychological or administrative resources in an efficient way. This study serves as a pilot to assess the feasibility to make attrition prediction","cbCaiasTGO12qOG9","https://ap.wps.com/l/cbCaiasTGO12qOG9","pdf",544544,1,14,"English","en",105,"# Introduction\n## Background and challenge of attrition\n## Aim and dataset overview\n# Methods\n## Data preparation and preprocessing\n## Feature selection\n## Model training and evaluation","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study focuses on predicting student attrition in higher education, especially identifying students at high risk of dropping out before graduation.\"},{\"question\":\"Which algorithms are used to predict attrition?\",\"answer\":\"Random forest and random tree (decision tree-based) machine learning algorithms are used to predict student attrition levels.\"},{\"question\":\"How is the dataset prepared for modeling?\",\"answer\":\"Student assessment data from 2015–2022 are collected from the Federal University Lokoja, converted into ARFF format using Weka libraries, and evaluated using resampling to create training and testing sets, along with correlation-based feature selection.\"}]","Student Attrition Prediction Using Machine Learning Techniques | PDF",1785680407,35,{"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},"student-attrition-prediction-using-machine-learning-techniques","",{"@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/student-attrition-prediction-using-machine-learning-techniques/117927/",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","2026-08-02",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?","Question",{"text":75,"@type":76},"The study focuses on predicting student attrition in higher education, especially identifying students at high risk of dropping out before graduation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithms are used to predict attrition?",{"text":80,"@type":76},"Random forest and random tree (decision tree-based) machine learning algorithms are used to predict student attrition levels.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset prepared for modeling?",{"text":84,"@type":76},"Student assessment data from 2015–2022 are collected from the Federal University Lokoja, converted into ARFF format using Weka libraries, and evaluated using resampling to create training and testing sets, along with correlation-based feature selection.","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"]