[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120276-en":3,"doc-seo-120276-105":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},120276,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Based Engagement Prediction for Online Courses","The study addresses the rapidly growing need for distance learning during the epidemic period, emphasizing scalable analysis of student engagement to support online education quality. Three machine learning models—decision trees, support vector machines, and random forests—are trained to predict online course participation. Model evaluation uses k-fold cross-validation and compares accuracy, precision, recall, and F1 score. Random forests achieve the best overall performance, while SVM performs weakest. The work further analyzes feature importance to reveal key drivers of engagement and guide educators in improving learning engagement and retention.","Machine Learning Based Engagement Prediction for Online Courses  \nWanning Wang  \nQingdao University of Science and Technology, Department of Information Sciences and Technology, 266061 No. 99 Songling Road, Qingdao, Shandong Province, China  \nAbstract. Within the constraints of the epidemic, the demand for distance learning in education is growing rapidly, and technological advances are opening up new possibilities for online education. This study investigates the performance of three machine learning models (decision trees, SVMs, and random forests) in predicting online course participation. To ensure the accuracy and generalizability of the results, the paper evaluated the models using k-fold cross-validation. Performance metrics such as accuracy, precision, recall and F1 score were used for comparison. The results show that the Random Forest model outperforms the other models on all metrics while the SVM model performs the weakest among the three models.  \nTherefore, this study conducted a feature importance analysis specifically for the decision tree and random forest models to gain insight into the predictive power of individual features. This helps educators and course designers to develop strategies to improve engagement and retention. In summary, this study emphasizes the effectiveness of random forests in predicting engagement in online courses and highlights the potential of machine learning in improving the quality of e-learning environments. The findings can help optimize ongoing online education discussions and can guide future research in the field of e-learning.  \n1 Introduction  \nThe new coronavirus outbreak at the end of 2019 has left many people stranded at home and unable to socialize as much as they would like. In order to be able to keep up with teaching and learning tasks, most students are at home utilizing online education platforms and resources. To ensure the quality of education, tools that can analyze and understand student engagement at scale are needed. Machine learning algorithms are able to process the massive amounts of data generated by online learning platforms to gain insights into student behavior. By analyzing the way students engage with course material, algorithms can customize content, pace, and pedagogy to the individual needs of the student, thus improving learning outcomes. Dias et al. proposed an application of the DeepLMS model to support online learning specific to online learning during epidemics, which inspired this study. The results of the study show that machine learning techniques are still highly predictive in online learning environments after an epidemic [1] . In addition, Mehta et al. (2022) developed a  \n[Corresponding author: mhinkle75096@student.napavalley.edu](Corresponding author: mhinkle75096@student.napavalley.edu)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nthree-dimensional DenseNet self-attentive neural network for automatic detection of student engagement, demonstrating the strong potential of deep learning applications in this area [2] . Hussain and Wenhao Zhu et al. employed various ML algorithms to identify students with low engagement in an Open University (OU) social science course to assess the impact of engagement on student performance and investigated the relationship between student engagement and course assessment scores [3]. It was ultimately concluded that J48, Decision Tree, JRIP and Gradient Boosting classifiers performed better in terms of accuracy, kappa value and recall compared to other models. Based on this they developed a dashboard which is easy to use by the faculty members of the Open University. Nicholas R. Stepanek explores the feasibility of applying machine learning to categorize student posts according to level of engagement based","cbCaipWdKdwk3Wcs","https://ap.wps.com/l/cbCaipWdKdwk3Wcs","pdf",365787,1,6,"English","en",105,"# Introduction\n# Method\n## Decision Tree\n## Random Forest\n## Support Vector Machine\n# Experimental Setup and Evaluation\n# Results and Discussion\n# Feature Importance Analysis\n# Conclusion","[{\"question\":\"Which machine learning models are compared for predicting online course engagement?\",\"answer\":\"The paper compares decision trees, support vector machines (SVMs), and random forests for engagement prediction.\"},{\"question\":\"How are the models evaluated to ensure accuracy and generalizability?\",\"answer\":\"Evaluation is performed using k-fold cross-validation and compares metrics including accuracy, precision, recall, and F1 score.\"},{\"question\":\"Which model performs best and which performs worst?\",\"answer\":\"Random forest outperforms the other models across all metrics, while the SVM model shows the weakest performance among the three.\"}]","Machine Learning Based Engagement Prediction for Online Courses | PDF",1785729201,15,{"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},"machine-learning-based-engagement-prediction-for-online-courses","",{"@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/machine-learning-based-engagement-prediction-for-online-courses/120276/",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},"Which machine learning models are compared for predicting online course engagement?","Question",{"text":75,"@type":76},"The paper compares decision trees, support vector machines (SVMs), and random forests for engagement prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the models evaluated to ensure accuracy and generalizability?",{"text":80,"@type":76},"Evaluation is performed using k-fold cross-validation and compares metrics including accuracy, precision, recall, and F1 score.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and which performs worst?",{"text":84,"@type":76},"Random forest outperforms the other models across all metrics, while the SVM model shows the weakest performance among the three.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"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"]