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Student retention and sustained academic success matter to students and university staff. This study predicts whether social sciences students are at risk (AR) or not at risk (NAR) regarding degree completion. A stratified sample of 300 students was analyzed using a multilayer perception neural network with inputs including CGPA, study time, prior marks, home environment, habits, learning skills, hardworking level, and academic interaction, achieving strong classification accuracy in training and holdout samples.",{"@graph":69,"@context":122},[70,84,105],{"@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/prediction-of-students-academic-performance-using-artificial-neural-network/595777/",{"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/prediction-of-students-academic-performance-using-artificial-neural-network/595777.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study’s main objective?","Question",{"text":112,"@type":113},"The study aims to predict students’ chances of being at risk (AR) or not at risk (NAR) with respect to their degree.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which model is used for classification?",{"text":117,"@type":113},"A multilayer perception neural network model is used to classify students into at risk or not at risk groups.",{"name":119,"@type":110,"acceptedAnswer":120},"How accurate were the predictions?",{"text":121,"@type":113},"The model achieved over 95% correct classification in the training sample and over 85% in the holdout sample.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},595777,1792467303,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46,"preview_page_count":4},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Prediction of Students’ Academic Performance using Artificial Neural Network  \nZahoor Ahmad * and Erum Shahzadi**  \nAbstract  \nUniversities play a remarkable role in the development of a country by producing skilled graduates for the country. Graduation rate is low as compared to the enrollment rate in the higher education institutions. Academic failure is main reason for non-degree completion. Students’ retention and high academic performance are significant for students, academic and administrative staff of universities. In this paper, our objective is to Predict the chance of students being at risk (AR) or not ‘Not at risk’ (NAR) with respect to their degree. Population of study consisted of all students of social sciences studying in 4th semester and they enrolled in 2007 session of BS and MA/MSc program at University of Gujrat Hafiz Hayat Campus. By using stratified sampling with proportional allocation method, a sample of 300 students was selected. We have used Multilayer Perception Neural Network Model to predict the chance of students being at risk (AR) or not ‘Not at risk’ (NAR) with respect to their degree on the basis of CGPA at the end of 2nd semester, Study time. Previous degree marks, Home environment, Study habits Learning skills, Hardworking and Academic interaction. In classifying the students at risk/not at risk, we could achieve a rate of correct classification of over 95% in training sample and over 85% in holdout sample. The estimated models can be used to predict the students being at risk or not with respect to their degree.  \nKeywords: Student’s Retention, at risk, not at risk, Multilayer Perception, Neural Network.  \n*Department of Statistics, University of Gujrat. Email: [zahoor.ahmed@uog.edu.pk](zahoor.ahmed@uog.edu.pk)  \n**Department of Statistics, University of Gujrat. Email: [erum.shahzadi@uog.edu.com](erum.shahzadi@uog.edu.com)  \nIntroduction  \nUniversities have came under increasing performance assessment because they played a critical role in national development efforts. Enrollment in higher education institutions and the proportion graduates entering colleges and universities have been gradually increasing, graduation rates have not kept rapidity and have remained remarkably flat. Academic failure at the undergraduate level is one of the main reasons for non-degree completion and consequently needs to be addressed to decrease the drop-out percentages (Cheesman, Simpson, & Wint, 2006) .  \nSeveral studies have been conducted on the prediction of academic performance of university students. Naik and Ragothaman (2004) carried out a study to predict MBA student success. This study demonstrated that the neural network model is useful tool for predicting MBA student performance. Many studies highlighted a series of factors that lead to the prediction of academic performance of university students. Vandamme, Meskens, and Superby, (2005) reported the factors that influence student performance and explored prediction methods. The main aim of this study was to classify students into three groups: ‘low risk’ students, ‘medium risk’ students, and ‘high risk’ students. Neural Network, Decision tree and Discriminant analysis was used to predict the achievement of freshmen. In this study discriminant analysis performed better than neural network.  \nDifferent researches have focused on comparing the performance of different methods for prediction of academic performance of university students. Ibrahim and Rusli (2007) conducted a study for predicting students’ academic performance. Three predictive models had been developed namely Artificial Neural Network, Decision Tree and Linear regression. The result depicted that more than 80% accuracy was achieved by all of three models. This study concluded that performance of Artificial Neural Network was better than others.  \nOladokun, Adebanjo, and Owaba-Chales,(2008) presented a study on predicting student academic performance. In this study an Artificial Neural Ne","cbCaicKICWdTBk10","https://ap.wps.com/l/cbCaicKICWdTBk10","pdf",181679,"English","# Abstract\n# Introduction\n# Research Objective\n# Research Methodology","[{\"question\":\"What is the study’s main objective?\",\"answer\":\"The study aims to predict students’ chances of being at risk (AR) or not at risk (NAR) with respect to their degree.\"},{\"question\":\"Which model is used for classification?\",\"answer\":\"A multilayer perception neural network model is used to classify students into at risk or not at risk groups.\"},{\"question\":\"How accurate were the predictions?\",\"answer\":\"The model achieved over 95% correct classification in the training sample and over 85% in the holdout sample.\"}]","Prediction of Students’ Academic Performance using Artificial Neural Network | PDF",1792081840]