[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120147-en":3,"doc-seo-120147-105":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},120147,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting student self-efficacy in Muslim societies using machine learning algorithms","The study investigates how student self-efficacy can be predicted in Muslim societies, addressing a research gap where machine-learning-based predictor modeling remains limited. Using an empirical dataset of secondary school students, four algorithms—Decision Tree, Random Forest, XGBoost, and Neural Network—were trained on demographic variables and 10 socioemotional, cognitive, and regulatory factors, including culturally relevant measures tied to religious/spiritual beliefs and collectivist-individualist orientation. Model reliability was evaluated with RMSE and R2, and results identify self-regulation, problem-solving, and belonging as the strongest contributors.","TYPE Original Research PUBLISHED 13 December 2024 DOI 10. 3389/fdata.2024.1449572  \nOPEN ACCESS  \nEDITED BY  \nZakariya Yahya Algamal, University of Mosul, Iraq  \nREVIEWED BY  \nZheng Dong, Bytedance, China Hussein Alrehan, University of Mosul, Iraq  \n*CORRESPONDENCE  \nMohammed Ba-Aoum  \n [mbaaoum@vt.edu](mbaaoum@vt.edu);  \n [mbaaoum@kfupm.edu.sa](mbaaoum@kfupm.edu.sa)  \nRECEIVED 15 June 2024  \nACCEPTED 25 November 2024  \nPUBLISHED 13 December 2024  \nCITATION  \nBa-Aoum M, Alrezq M, Datta J and Triantis KP (2024) Predicting student self-e􀀈cacy in Muslim societies using machine learning algorithms. Front. Big Data 7:1449572 .  \ndoi: 10.3389/fdata.2024.1449572  \nCOPYRIGHT  \n© 2024 Ba-Aoum, Alrezq, Datta and Triantis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting student self-e􀀈cacyin Muslim societies using machine learning algorithms  \nMohammed Ba-Aoum1,2*, Mohammed Alrezq1 , Jyotishka Datta3 and Konstantinos P. Triantis1  \n1 Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States,  \n2 Department of Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, 3 Department of Statistics, Virginia Tech, Blacksburg, VA, United States  \nIntroduction: Self-e􀀈cacy is a critical determinant of students’ academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-e􀀈cacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students’ self-e􀀈cacy.  \nMethods: An empirical dataset collected was used to examine self-e􀀈cacy among secondary school students in Muslim societies. Four machine learning algorithms-Decision Tree, Random Forest, XGBoost, and Neural Network-were employed to predict self-e􀀈cacy using two demographic variables and 10 socioemotional, cognitive, and regulatory factors. The predictors included culturally relevant variables such as religious/spiritual beliefs and collectivist-individualist orientation. Model performance was assessed using root mean square error (RMSE) and r-squared (R2 ) metrics to ensure reliability and validity.  \nResults: The results showed that Random Forest outperformed the other models in accuracy, as measured by R2 and RMSE metrics. Among the predictors, self-regulation, problem-solving, and a sense of belonging emerged as the most significant factors, contributing to more than half of the model’s predictive power. Other variables such as gratitude, forgiveness, empathy, and meaning-making displayed moderate predictive value, while gender, emotion regulation, and collectivist-individualist orientation had minimal impact. Notably, religious/spiritual beliefs and regional factors showed negligible influence on self-e􀀈cacy predictions.  \nDiscussion: This study enhances the understanding of factors influencing selfe􀀈cacy among students in Muslim societies and o􀀀ers a data-driven foundation for developing targeted educational interventions. The findings highlight the utility of machine learning in education research, demonstrating its ability to uncover insights for equitable and e􀀀ective decision-making. By emphasizing the importance of regulatory and socio-emotional factors, this research provides actionable insights to elevate student performance and well-being in diverse cultural contexts.  \nKEYWORDS  \nacademic performance, educational equity, machine learning, Muslim societies, selfe􀀈cacy, self-regulation, socio-emotional ","cbCaitlvktFxiHlU","https://ap.wps.com/l/cbCaitlvktFxiHlU","pdf",2004988,1,16,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What is the main purpose of the study?\",\"answer\":\"To identify and predict the key factors influencing student self-efficacy in Muslim societies using machine learning models.\"},{\"question\":\"Which machine learning algorithms were used and what inputs were included?\",\"answer\":\"Decision Tree, Random Forest, XGBoost, and Neural Network were applied using two demographic variables and 10 socioemotional, cognitive, and regulatory factors, including culturally relevant measures.\"},{\"question\":\"Which model performed best and which predictors were most important?\",\"answer\":\"Random Forest outperformed the others based on R2 and RMSE. Self-regulation, problem-solving, and a sense of belonging were the most significant predictors, explaining over half of the model’s predictive power.\"}]","Predicting student self-efficacy in Muslim societies using machine learning algorithms | PDF",1785728437,40,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-student-self-efficacy-in-muslim-societies-using-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/predicting-student-self-efficacy-in-muslim-societies-using-machine-learning-algorithms/120147/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of the study?","Question",{"text":75,"@type":76},"To identify and predict the key factors influencing student self-efficacy in Muslim societies using machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were used and what inputs were included?",{"text":80,"@type":76},"Decision Tree, Random Forest, XGBoost, and Neural Network were applied using two demographic variables and 10 socioemotional, cognitive, and regulatory factors, including culturally relevant measures.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and which predictors were most important?",{"text":84,"@type":76},"Random Forest outperformed the others based on R2 and RMSE. Self-regulation, problem-solving, and a sense of belonging were the most significant predictors, explaining over half of the model’s predictive power.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]