[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127475-en":3,"doc-seo-127475-105":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},127475,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Comparative Study of Machine Learning Algorithms for Virtual Learning Environment Performance Prediction - Research","Virtual learning environments have become an increasingly common choice for students across diverse cultural and socioeconomic backgrounds, yet improving learning outcomes remains challenging because instruction is delivered primarily online. This study analyzes students’ participation and performance using the publicly available Open University learning analytics dataset. Multiple machine learning prediction models are compared—including support vector machine, random forest, Naive Bayes, logistic regression, and decision trees—using confusion matrices and hyperparameter search to identify the most accurate approach for classifying VLE performance.","A comparative study of machine learning algorithms for virtual learning environment performance prediction  \nEdi Ismanto1, Hadhrami Ab. Ghani2, Nurul Izrin Binti Md Saleh2  \n1Department of Informatics Education, Universitas Muhammadiyah Riau, Pekanbaru, Indonesia 2Department of Data Science, Faculty of Data Science and Computing, Universiti Malaysia Kelantan, Kota Bharu, Malaysia  \nArticle history:  \nReceived Jun 11, 2022 Revised Jan 10, 2023 Accepted Mar 10, 2023  \nKeywords:  \nClassification techniques Exploratory data analysis Machine learning Performance evaluation Virtual learning environment  \nCorresponding Author:  \nVirtual learning environment is becoming an increasingly popular study option for students from diverse cultural and socioeconomic backgrounds around the world. Although this learning environment is quite adaptable, improving student performance is difficult due to the online-only learning method. Therefore, it is essential to investigate students' participation and performance in virtual learning in order to improve their performance. Using a publicly available Open University learning analytics dataset, this study examines a variety of machine learning-based prediction algorithms to determine the best method for predicting students' academic success, hence providing additional alternatives for enhancing their academic achievement. Support vector machine, random forest, Nave Bayes, logical regression, and decision trees are employed for the purpose of prediction using machine learning methods. It is noticed that the random forest and logistic regression approach predict student performance with the highest average accuracy values compared to the alternatives. In a number of instances, the support vector machine has been seen to outperform the other methods.  \nThis is an open access article under the CC BY-SA license.  \nEdi Ismanto  \nDepartment of Informatics Education, Universitas Muhammadiyah Riau Jalan Tuanku Tambusai, Kota Pekanbaru, Provinsi Riau-Indonesia Email: [edi.ismanto@umri.ac.id](edi.ismanto@umri.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe emergence of more and more online and open courses in various countries has shown a significant impact on the progress of the development of distance education. This enables multiple course delivery formats to be developed by higher education institutions and organizations. Researchers are interested in analyzing student activity patterns in taking online courses because of the growing number of higher education institutions that use distance learning with the use of a virtual learning environment (VLE) such as massive online open courses (MOOCs) . Large datasets from VLEs may be evaluated and utilized to provide recommendations for enhancing the online learning experience.  \nLearning analytics' major goal is to extract students' study patterns in order to improve the quality of learning and instruction. Learning analysis data not only provides instructional references for instructors to improve the quality of their teaching but also ideas for instructors to assist students in changing their learning practices. The success of students taking online courses is one of the indicators of the success of higher education.  \nThe advancement of machine learning (ML), which is now widely utilized to tackle data problems, is causing ML research to expand [1]–[3] . In order to increase model performance, ML algorithm capabilities are constantly upgraded [4]–[6] . Several high-performance classification algorithms, such as support vector machine (SVM), random forest (RF), Nave Bayes (NB), logistic regression (LR), and decision trees (DT), have  \nbeen investigated in the literature and will be utilized to develop prediction models in the Open University learning analytics dataset to solve VLE data challenges (OULAD) . OULAD is a database that contains information on courses, students, and their interactions with the virtual learning environment (VLE), which curre","cbCaiu8X9Rli3uAW","https://ap.wps.com/l/cbCaiu8X9Rli3uAW","pdf",453893,1,10,"English","en",105,"# Introduction\n# Method\n## Exploratory data analysis (EDA)","[{\"question\":\"What problem does the study address in virtual learning environments?\",\"answer\":\"It addresses the difficulty of improving student performance in online-only learning and motivates investigating participation and performance to enhance academic outcomes.\"},{\"question\":\"Which machine learning algorithms are compared for prediction?\",\"answer\":\"Support vector machine, random forest, Naive Bayes, logistic regression, and decision trees are used to build prediction models.\"},{\"question\":\"How is model performance evaluated in this research?\",\"answer\":\"A confusion matrix measures and evaluates each model, while grid search and random search are used to tune hyperparameters.\"}]","A Comparative Study of Machine Learning Algorithms for Virtual Learning Environment Performance Prediction - 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