[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118984-en":3,"doc-seo-118984-105":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},118984,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","The development of a predictive model for students’ final grades using machine learning techniques","Predictive analytics in education enables educators to improve student achievement by extracting patterns from historical learning records through data mining and machine learning methods. Research remains limited on applying machine learning–based predictive analytics to strengthen student performance in Malaysian higher education. This study builds models for forecasting final grades using records of 450 students enrolled in Business Statistics at Universiti Islam Pahang Sultan Ahmad Shah obtained from its Learning Management System. Decision Tree, k-Nearest Neighbor, and Naïve Bayes models are trained in Python.","The development of a predictive model for students’ final grades using machine learning techniques  \nN. H.A. Rahman1,2, S.A. Sulaiman2,* , and N.A. Ramli2  \n1Department of Business, Faculty of Management and Informatics, Universiti Islam Pahang Sultan Ahmad Shah, Jalang Gambang, 25150 Kuantan, Pahang, Malaysia  \n2Centre for Mathematical Sciences, Universiti Malaysia Pahang, Lebuh Persiaran Tun Khalil Yaakob, 26300 Kuantan Pahang, Malaysia  \nABSTRACT – As per research, utilizing predictive analytics in education can be very beneficial. It can help educators improve students' performance by analyzing historical data through various approaches such as data mining and machine learning. However, there is a scarcity of studies on using machine learning and predictive analytics to enhance student performance in Malaysian higher education. This study used the records of 450 students enrolled in the Business Statistics course at Universiti Islam Pahang Sultan Ahmad Shah (UnIPSAS) from 2013, obtained from UnIPSAS's Learning Management System. The aim was to develop the best predictive model for forecasting students' final grades based on their performance levels, using machine learning techniques such as Decision Tree, k-Nearest Neighbor, and Naïve Bayes. The final model was developed using Python software. The results showed a strong negative correlation between the students' carry marks and their final grades, with an r-value of-0.8. Naïve Bayes was found to bethe best model, with an AUC score of 0.79.  \nARTICLE HISTORY  \nReceived: 14/02/2023  \nRevised: 27/03/2023  \nAccepted: 31/03/2023  \nKEYWORDS  \nMachine learning Predictive models Students’ performance Education  \nINTRODUCTION  \nPredictive analytics research has grown in popularity as a result of its ability to provide useful information to educators, and potentially assisting them in enhancing students’ achievement in higher education. Educators could use predictive analytics to build an effective mechanism to improve academic achievements, preventing students from dropping out and ensuring student retention [1] . The risk of failing a course, the risk of student’s dropout, the grade prediction, and the graduation rate are all common prediction targets [2] . Dropouts have a negative impact on both educational institutions and stakeholders. Furthermore, with the virtual learning methods practised in today’s education system, e-learning dropout rates are often greater than face-to-face education [3] . Many factors, including academic performance, health, family, and personal reasons, can lead to dropout, which varies based on the nature of the study and the higher education provider. If a huge number of students dropping out of their respective universities, the higher education provider's reputation might be dropped. Dropout would also result in a significant loss of human capital for the country, as public universities would generate fewer professionals and experts [4] .  \nStudents' grades and final marks will be disclosed after the final examination, which means that students will only be aware of their accomplishments after the faculty has announced their grade. If students fail, they will have to repeat the subject in the following semester, incurring university expenditures as well as burdening many other parties, such as parents and lecturers. As a result, students are less likely to remain motivated to learn. In addition, the financial strain on the family will also increase as the student's college loan must be paid even if they do not graduate. Therefore, one of the most effective strategies that should be considered by the education providers is to detect the tendency of failure before the students sit for final examination. Identifying students who require further assistance and taking the necessary steps to improve their performance is also critical [5] . Preliminary prediction of students' grades based on their accumulated marks and previous achievement as well as othe","cbCaiuX58Y3BI16c","https://ap.wps.com/l/cbCaiuX58Y3BI16c","pdf",1260695,1,9,"English","en",105,"# Introduction\n## Motivation and prediction targets\n## Problem in Malaysian higher education\n# Methodology\n## Dataset and course context\n## Machine learning models\n## Evaluation approach\n# Results and findings\n## Correlation and best-performing model","[{\"question\":\"What is the goal of the study on students’ final grades?\",\"answer\":\"The study aims to develop predictive models that forecast students’ final grades using machine learning techniques based on performance records and related factors.\"},{\"question\":\"Which machine learning techniques are used to build the predictive models?\",\"answer\":\"The models use Decision Tree, k-Nearest Neighbor (k-NN), and Naïve Bayes, implemented with Python.\"},{\"question\":\"What result indicates that Naïve Bayes performs best?\",\"answer\":\"Naïve Bayes achieves the best performance with an AUC score of 0.79, and it is selected as the top model in the study’s results.\"}]","The development of a predictive model for students’ final grades using machine learning techniques | 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is the goal of the study on students’ final grades?","Question",{"text":76,"@type":77},"The study aims to develop predictive models that forecast students’ final grades using machine learning techniques based on performance records and related factors.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning techniques are used to build the predictive models?",{"text":81,"@type":77},"The models use Decision Tree, k-Nearest Neighbor (k-NN), and Naïve Bayes, implemented with Python.",{"name":83,"@type":74,"acceptedAnswer":84},"What result indicates that Naïve Bayes performs best?",{"text":85,"@type":77},"Naïve Bayes achieves the best performance with an AUC score of 0.79, and it is selected as the top model in the study’s 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