[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123120-en":3,"doc-seo-123120-105":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":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},123120,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting student performance using Moodle data and machine learning with feature importance","Despite rapid technological advances in education, poor student academic performance remains a persistent challenge for institutions worldwide. This study predicts students’ academic performance using modular object-oriented dynamic learning environment (Moodle) data combined with tree-based machine learning models and feature importance analysis. Multiple datasets and generic features are used to improve generalizability, and random forest, XGBoost, and C5.0 are compared. The random forest model performs best with ROC-AUC scores of 0.77 (training) and 0.73 (testing), while submission actions are the most important predictors and delete actions the least.","Predicting student performance using Moodle data and machine learning with feature importance  \nJamal Kay Rogers1,3, Tamara Cher Mercado1, Remelyn Cheng2,3  \n1College of Information and Computing, University of Southeastern Philippines, Davao City, Philippines 2College of Arts and Sciences, Mapua Malayan Colleges Mindanao, Davao City, Philippines 3Graduate School Department, University of the Immaculate Conception, Davao City, Philippines  \n\n| Article history:\u003Cbr>Received Jul 7, 2024 Revised Sep 3, 2024 Accepted Sep 7, 2024 | Despite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students ’ academic performance through modular object-oriented dynamic learning environment (Moodle) data and tree-based machine learning algorithms with feature importance. While previous studies aimed at increasing model performance, this study trained a model with multiple data sets and generic features for improved generalizability. Through a comparative analysis of random forest (RF), XGBoost, and C5.0 decision tree (DT) algorithms, the trained RF model emerged as the best model, achieving a good ROC-AUC score of 0.77 and 0.73 in training and testing sets, respectively. The feature importance aspect of the study identified the submission actions as the most crucial predictor of student performance while the delete actions as the least. The Moodle data used in the study was limited to 2-degree programs from the University of Southeastern Philippines (USeP) . The 22 courses still resulted in a small sample size of 1,007 . Future research should broaden its focus to increase generalizability. Overall, the findings highlight the potential of machine learning techniques to inform intervention strategies and enhance student support mechanisms in online education settings, contributing to the intersection of data science and education literature.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Algorithm\u003Cbr>Educational data mining Feature engineering Learning management system Online learning\u003Cbr>Predictive analytics |  |\n\nCorresponding Author:  \nJamal Kay Roger  \nCollege of Information and Computing, University of Southeastern Philippines Iñigo Street, Obrero, Davao City, Philippines  \nEmail: [jamalkay.rogers@usep.edu.ph](jamalkay.rogers@usep.edu.ph)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nDespite technological advancements in education, poor academic performance remains one of educational institutions ’ most challenging aspects [1] . Factors such as the lack of family support, financial issues, motivation, learning facilities, and teaching techniques contribute to this concern. Although the everevolving growth of technology provided great opportunities to improve academic performance, challenges remain due to these factors [2] . Many institutions have employed management techniques, web-based learning, and emerging technologies such as data analytics, the internet of things (IoT), and data mining techniques to address poor academic performance challenges. Machine learning algorithms and deep learning models have been used to analyze student data and predict student success [3], [4] . These student data can be accessed with the advent of online learning through learning management systems (LMS) .  \nThe growing popularity of online learning with LMS has paved the way for institutions to collect user data through databases and logs. Studies have shown that resources and activities from LMS, such as  \nfiles, links, and forum participation, can positively impact academic performance [5] . Using LMS data provides institutions with data-driven approaches that can help identify students needing assistance and provide interventions, ultimately improving educational outcomes [6], [7] .  \nLMS has different platforms like modular object-oriented dynamic learning environment (Moodle), ","cbCaidttRbnZ35bn","https://ap.wps.com/l/cbCaidttRbnZ35bn","pdf",435708,1,9,"English","en",105,"# Introduction\n## Online learning and learning management systems (LMS)\n## Moodle and educational data mining (EDM)\n## Machine learning for early interventions\n# Related work and predictive modeling","[{\"question\":\"How does the study predict student academic performance?\",\"answer\":\"It uses Moodle data and applies tree-based machine learning algorithms, alongside feature importance analysis, to model and explain performance outcomes.\"},{\"question\":\"Which algorithm achieved the best predictive results?\",\"answer\":\"Random forest emerged as the best model, reaching ROC-AUC scores of 0.77 for training and 0.73 for testing.\"},{\"question\":\"What does feature importance reveal about student behavior?\",\"answer\":\"Submission actions are identified as the most crucial predictor of performance, while delete actions are the least influential.\"}]","Predicting student performance using Moodle data and machine learning with feature importance | 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