[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123885-en":3,"doc-seo-123885-105":29,"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123885,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Modeling and Classification of Student Performance Based on a Machine Learning Model - Article 27","Machine Learning is increasingly adopted in education to improve multiple aspects of the learning system. This study builds a predictive model using Machine Learning techniques to forecast students’ academic performance, support proactive assessment by categorizing students according to performance potential, and enable early intervention. Using a sample of 1087 students from King Abdulaziz University in Saudi Arabia, the study predicts student scores with the GMM (Gaussian mixture model) approach. Results show students’ mean scores range from 14 to 93, and the mixture model with four components and varying variances provides the optimal prediction.","Information Sciences Letters  \n\n| Volume 12\u003Cbr>Issue 12 Dec. 2023 | Article 27 |\n| --- | --- |\n| 2023\u003Cbr>Modeling and Classification of Student Performance Based on a Machine Learning Model\u003Cbr>Abdulellah A. Alsulaimani\u003Cbr>Department of Educational Technologies, Faculty of Education, King Abdulaziz University, Jeddah, Saudi Arabia, [aalsulaimani@kau.edu.sa](aalsulaimani@kau.edu.sa)\u003Cbr>Follow this and additional works at: [https://digitalcommons.aaru.edu.jo/isl](https://digitalcommons.aaru.edu.jo/isl) |  |\n\nRecommended Citation  \nA. Alsulaimani, Abdulellah (2023) \"Modeling and Classification of Student Performance Based on a Machine Learning Model,\" Information Sciences Letters: Vol. 12 : Iss. 12 , PP-.  \nAvailable at: [https://digitalcommons.aaru.edu.jo/isl/vol12/iss12/27](https://digitalcommons.aaru.edu.jo/isl/vol12/iss12/27)  \nThis Article is brought to you for free and open access by Arab Journals Platform. It has been accepted for inclusion in Information Sciences Letters by an authorized editor. The journal is hosted on Digital Commons, an Elsevier platform. For more information, please contact [rakan@aaru.edu.jo](rakan@aaru.edu.jo), [marah@aaru.edu.jo](marah@aaru.edu.jo),  \n[u.murad@aaru.edu.jo](u.murad@aaru.edu.jo).  \nInformation Sciences Letters  \nAn International Journal  \n[http://dx.doi.org/10.18576/isl/121228](http://dx.doi.org/10.18576/isl/121228)  \nModeling and Classification of Student Performance Based on a Machine Learning Model  \nAbdulellah A. Alsulaimani  \nDepartment of Educational Technologies, Faculty of Education, King Abdulaziz University, Jeddah, Saudi Arabia  \nReceived: 10 Sep. 2023, Revised: 25 Oct. 2023, Accepted: 28 Nov. 2023  \nPublished online: 1 Dec. 2023.  \nAbstract: The increasing popularity of Machine Learning in the education business can be attributed to its capacity to enhance several aspects of the educational system. The objective of the present study is to construct a prediction model utilizing Machine Learning techniques in order to forecast students' academic performance. In the contemporary competitive landscape, academic institutions are compelled to engage in the proactive task of predicting students'academic performance, categorizing them based on their individual talents, and implementing strategies to enhance their success in examinations. In order to identify students who may require early intervention and support, educational institutions must have the capacity to analyze student learning behavior through the application of predictive models for student achievement. The present study utilized a sample of 1087 students enrolled at King Abdulaziz University in Saudi Arabia to make predictions about student scores by employing the GMM model. The results indicated that the mean score achieved by students enrolled in this particular course varied between 14 and 93. The findings also indicate that the optimal model for predicting students' academic achievement is the mixture model with four components and varying variances.  \nKeywords: Machine Learning; Modeling; classification; Gaussian mixtures model, prediction.  \n1 Introduction  \nThe study examines age, sex, obesity, average family income, family size, father and mother education, marital status, and school characteristics including gender, academic level, and others [1] stress, lifestyle, and academic performance. Random forests, gradient boosting, stacking, and artificial neural networks were used. Gradient boosting, randomization, ANN, and logistic regression followed stacking as the best algorithm. Lifestyle greatly impacts academic success. This study predicts undergraduate Chinese university students' GPAs using socioeconomic background and admission exam results using ANN [2] . In the first stage, statistical assessments of students' background information showed that GPAs improved annually, female students had better grades than male students, rural and urban students performed similarly, and non-repeating students performed","cbCaiszNMIMl8Q9b","https://ap.wps.com/l/cbCaiszNMIMl8Q9b","pdf",929508,1,"English","en",105,"# Introduction\n## Related Work and Prior Studies\n# Methodology\n## Data and Sample Description\n## GMM-Based Prediction Approach\n# Results and Findings\n## Score Range and Model Selection\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To construct a prediction model using Machine Learning techniques to forecast students’ academic performance and support proactive categorization and intervention.\"},{\"question\":\"Which dataset and model are used for the predictions?\",\"answer\":\"The study uses data from 1087 students at King Abdulaziz University and employs the GMM (Gaussian mixture model) to predict student scores.\"},{\"question\":\"What do the results indicate about the best prediction model?\",\"answer\":\"The optimal approach is a mixture model with four components and varying variances, producing the best model performance for predicting academic achievement.\"}]","Modeling and Classification of Student Performance Based on a Machine Learning Model - 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