[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122613-en":3,"doc-seo-122613-105":30,"detail-sidebar-cat-0-en-105":90},{"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},122613,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Comparative Between Three Machine Learning Algorithms to Predict and Improve Students’ Academic Performance","The study targets improving educational outcomes by identifying students needing extra support through predictive analytics. Three machine learning algorithms—Support Vector Machine, Linear Regression, and Stochastic Gradient descent—are used to build a classifier that predicts students’ performance in higher institutions. Data are collected via surveys and students’ grade book records across Federal University Dutsinma, Abdu Gusau Polytechnic Talata Mafara, and College of Education Maru. Model comparison relies on Mean Absolute Error, Mean Square Error, Root Mean Square Error, and accuracy, with Support Vector Machine achieving the strongest result.","IJSGS  \nISSN: 2488-9229 FEDERAL UNIVERSITY GUSAU-NIGERIA  \nINTERNATIONAL JOURNAL OF SCIENCE FOR GLOBAL SUSTAINABILITY  \nComparative Between Three Machine Learning Algorithms to Predict and Improve Students’Academic Performance 1Bashiru Aliyu Sani, 2Samaila Baoku I.G, 3Bashir Jamilu Ahmed and4Samaila Musa  \n1 Department of Computer Science, Abdu Gusau Polytechnic Talata Mafara Zamfara tate Nigeria, 2Department of Mathematical Sciences, Faculty of Physical science, Federal University Dutsin-ma Katsina, State, Nigeria,. 3Department of Cyber Security, Faculty of Computer Science and Artificial Intelligence. Federal University Dutsin-ma Katsina  \nState, Nigeria,  \n4Department of Computer Science, Federal University Gusau, Zamfara State, Nigeria  \n* Corresponding Author’s [Email](Email: baliyusani@gmail.com: Phone NO.:)[: ](Email: baliyusani@gmail.com: Phone NO.:)[baliyusani@gmail.com](Email: baliyusani@gmail.com: Phone NO.:)[:](Email: baliyusani@gmail.com: Phone NO.:)[ Phone NO.:](Email: baliyusani@gmail.com: Phone NO.:) 2348062477796 Received on: October, 2022 Revised and Accepted on: November, 2022 Published on: December, 2022  \nABSTRACT  \nThe greatest aim of every educational setup is giving the best educational experience and knowledge to the students. Discovering the students who need extra support and guidance so as to carry out the necessary actions to enhance their performance plays an important role in achieving that aim. In this research work, three machine learning algorithms have been used to build a classifier that can predict the performance of the students in higher institutions considering three Tertiary institutions which are: Federal University Dutsinma, Katsina State, Abdu Gusau Polytechnic Talata Mafara and College of Education Maru, Zamfara State. The machine learning algorithms includ: Support Vector Machine, Linear Regression and Stochastic Gradient descent algorithms. The models have been compared using the Mean Absolute Error, Mean Square Error and Root Mean Square Error classification accuracy. The dataset used to build the models is collected based on a survey given to the students and the students’ grade book. The support vector machine model achieved the best performance that is equal to 99. 1% .  \nKeywords: support vector machine, Linear Regression and Stochastic algorithm  \n1.0 INTRODUCTION  \n1.1 Background of the study  \nWith the wide usage of computers and internet, there are recently a huge increase in public available data that need to be analyzed. Be it online sales information, website traffic, or user habits, data is generated every day. Such a large amount of data presents both a problem and an opportunity. The problem is that it is difficult for humans to analyze such large data. The opportunity is that this type of data is ideal for computers to process, because it is stored digitally in a well-formatted way, and computers can process data much faster than humans.  \nThe concept of machine learning is something born out of this environment. Computers can analyze digital data to find patterns and laws in ways that is too complex for a human to do. The basic idea of machine learning is that a computer can automatically learn from experience (Hussain et al.,2019) . Although machine learning applications vary, its general function is  \nsimilar throughout its applications. The computer analyzes a large amount of data, and finds patterns and rules hidden in the data. These patterns and rules are mathematical in nature, and they can be easily defined and processed by a computer. The computer can then use those rules to meaningfully characterize new data. The creation of rules from data is an automatic process, and it is something that continuously improves with newly presented data. Depending on the type of input data, machine learning algorithms can be divided into supervised and unsupervised learning. In supervised learning, input data comes with a known class structure (Ahmad and Shahzadi, 2018) ","cbCaiptp4nTIbfel","https://ap.wps.com/l/cbCaiptp4nTIbfel","pdf",326058,1,4,"English","en",105,"# Abstract\n# 1.0 Introduction\n## 1.1 Background of the study\n# 2.0 Review of Related Works","[{\"question\":\"Which machine learning algorithms are compared for predicting student performance?\",\"answer\":\"The research compares Support Vector Machine, Linear Regression, and Stochastic Gradient descent algorithms.\"},{\"question\":\"What datasets are used to build the predictive models?\",\"answer\":\"The dataset is collected from a student survey and the students’ grade book records.\"},{\"question\":\"How are the models evaluated and which algorithm performs best?\",\"answer\":\"Models are compared using Mean Absolute Error, Mean Square Error, Root Mean Square Error, and accuracy. Support Vector Machine achieves the best performance at about 99.1%.\"}]","Comparative Between Three Machine Learning Algorithms to Predict and Improve Students’ Academic Performance | PDF",1785811731,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"comparative-between-three-machine-learning-algorithms-to-predict-and-improve-students-academic-performance","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/comparative-between-three-machine-learning-algorithms-to-predict-and-improve-students-academic-performance/122613/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine learning algorithms are compared for predicting student performance?","Question",{"text":74,"@type":75},"The research compares Support Vector Machine, Linear Regression, and Stochastic Gradient descent algorithms.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What datasets are used to build the predictive models?",{"text":79,"@type":75},"The dataset is collected from a student survey and the students’ grade book records.",{"name":81,"@type":72,"acceptedAnswer":82},"How are the models evaluated and which algorithm performs best?",{"text":83,"@type":75},"Models are compared using Mean Absolute Error, Mean Square Error, Root Mean Square Error, and accuracy. 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