[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118757-en":3,"doc-seo-118757-105":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},118757,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Academic Performance Prediction using Machine learning algorithms - Abstract, Introduction, Related Works","The study aims to predict student performance across semesters and to compare predictive accuracy using multiple machine learning approaches on a Kaggle student performance dataset. Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision Tree, and K-NN are applied to estimate grade outcomes as indicators of performance. The results report high predictive effectiveness, achieving 94.9% accuracy, supporting the use of automated learning analytics for assessment and decision-making.","Academic Performance Prediction using Machine learning algorithms  \nTao Hai 1[0000-0002-6156-1974], Jincheng Zhou 2[0000-0002-1995-4002],  \nShirin Abolfath Zadeh 3[0000-0002-8867-9542], Afolake O. Adedayo4[0000-0002-4057-3861]  \n, Sf Gan5, Celestine Iwendi 6[0000-0003-4350-3911]  \n1,2,5 School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China  \n[haitao@bjwlxy.edu.cn](haitao@bjwlxy.edu.cn1)[1](haitao@bjwlxy.edu.cn1), [guideaaa@126.com](guideaaa@126.com2)[2](guideaaa@126.com2), [466497569@qq.com](466497569@qq.com5)[5](466497569@qq.com5)  \n[3](3),[4](4),[6](6) School of Creative Technologies, University of Bolton, United Kingdom.  \nPostcode: BL3 5AB  \n[Shirin.abolfathzadeh@ieee.org](Shirin.abolfathzadeh@ieee.org3)[3](Shirin.abolfathzadeh@ieee.org3), [afolake.adedayo@ieee.org](afolake.adedayo@ieee.org4)[4](afolake.adedayo@ieee.org4)  \n, [celestine.iwendi@ieee.org](celestine.iwendi@ieee.org6)[6](celestine.iwendi@ieee.org6)  \nAbstract. The objective of the study is to use a method to predict student performance during the semesters and to compare accuracy perceptron for adataset of student performance. In this regard, Machine Learning techniques were applied to the student performance dataset provided [by the Kaggle.com](by the Kaggle.com)[ ](by the Kaggle.com)[website. Multilayer Perceptron](website. Multilayer Perceptron), Random Forest, SVM, Naïve Bayes, Decision tree and K-NN algorithms were used to predict the Grade result of students as a factor of performance. The Student Performance dataset is used to forecast how well students will perform in their tests. As a result, with 94.9% accuracy, the results were predicted.  \nKeywords: Academic Performance, Prediction, Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision tree, K-NN, Machine Learning.  \n1 Introduction  \nAcademic observation is crucial and has been regularly used these days. Although this type of observation is thought to be necessary, it would be challenging to carry out onevery student in a class, especially if the class has many pupils. Observing students in the classroom enables us to recognise their behaviour, which enables us to give them the appropriate direction and also contributes to improving student behaviour (Mindell 1974) . The COVID-19 pandemic has increased demand for online learning strategies and Learning Management Systems (LMS) and its developing field of Learning Analytics (LA) has grown in significance as a result. These tools track and assess students’ activities to boost teaching and make decisions in the educational system. Because of its accuracy and speed, the Online Examination System is now thought of as a rapidly growing examination technique. Almost every organisation uses testing systems. Exams also enable organisations to easily monitor students'advancement over time. Consequently, the result may be computed more quickly. [1]  \nBoth teaching and learning profit from the prediction of student academic success [1],[2] . To be proactive, educators can utilise the anticipated results to determine how many students will perform well, averagely, or poorly in a class. For instance, teachers may think about adopting proactive actions to help those students perform better in the semester if the expected results reveal that some students in the class would be \"academically at risk.\" Representative examples of preventative approaches involve expanding recitation sessions, increasing office hours, enhancing student problem-solving skills with computer simulations and animations, implementing a range of active and interactive learning methodologies, and so forth.  \n2 Related works  \nAs [1] aimed, educational institutions - Educational Data Mining (EDM) - boost performance by evaluating student learning behaviour, which includes domain modelling, analysis and visualization of data related to the education system. that paper applied linear regression and multilayer perceptron and conc","cbCaipcwIS9fwKRC","https://ap.wps.com/l/cbCaipcwIS9fwKRC","pdf",560627,1,12,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction\n# 2 Related works","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To predict student performance during semesters and compare the accuracy of machine learning models for student grade outcomes.\"},{\"question\":\"Which machine learning algorithms are used for prediction?\",\"answer\":\"Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision Tree, and K-NN are used to predict grades based on performance factors.\"},{\"question\":\"How accurate are the prediction results reported?\",\"answer\":\"The study reports results predicted with 94.9% accuracy using the applied methods.\"}]","Academic Performance Prediction using Machine learning algorithms - 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