[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127336-en":3,"doc-seo-127336-105":29,"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":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},127336,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Predicting Student Status Using Machine Learning by Analyzing Classroom Behaviors with X-API Data","Educational data mining addresses academic challenges by extracting hidden insights from large datasets collected across learning environments. This research leverages Experience API (xAPI) with the Kalboard 360 platform to build a behaviorally grounded student performance model that measures how learner interactions influence academic outcomes. Multiple machine learning methods are implemented, including logistic regression, k-nearest neighbors, support vector machines, decision trees, random forests, and XGBoost. Results indicate a clear improvement in categorization accuracy and support personalization through formative assessment and early identification of each student’s needs.","Predicting student status using machine learning by analyzing classroom behaviors with X-API data  \nAbdelamine Elouafi1, Ilyas Tammouch1, Souad Eddarouich2, Raja Touahni1  \n1Faculty of Science/Telecommunications Systems, and Decision Engineering Laboratory, Ibn Tofail University, Kenitra, Morocco  \n2Regional Educational Center, Rabat, Morocco  \n\n| Article history:\u003Cbr>Received Jul 11, 2024 Revised Sep 30, 2024 Accepted Oct 7, 2024 |\n| --- |\n| Keywords:\u003Cbr>Decision tree\u003Cbr>K-nearest neighbors Logistics regression Random forest\u003Cbr>SVM\u003Cbr>The performance of students XGBoost |\n\nCorresponding Author:  \nWe explore the emergence and growing significance of educational data mining, a field dedicated to extracting valuable insights from vast datasets gathered from diverse educational environments. Utilizing the experience API (XAPI) and the Kalboard 360 online learning platform, our research presents a novel behaviorally based student performance model that evaluates the influence of student interactions on academic results. We create reliable models for precisely projecting academic success by utilizing machine learning techniques including logistic regression, k-nearest neighbors (KNNs), support vector machines (SVM), decision trees, random forests (RF), and XGBoost. The outcomes show a notable increase in categorization accuracy. Through the personalization of instruction, formative assessment support, and proactive identification of each student's unique needs to maximize their learning experience, this approach holds the potential to improve educational processes.  \nThis is an open access article under the CC BY-SA license.  \nAbdelamine Elouafi  \nFaculty of Science/Telecommunications Systems, and Decision Engineering Laboratory Ibn Tofail University  \nKenita, Morocco  \nEmail: [abdelamine.elouafi@uit.ac.ma](abdelamine.elouafi@uit.ac.ma)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nToday, the volume of educational data is rapidly expanding, marking the emergence of a new field: educational data mining. This field focuses on developing methods to address educational challenges by uncovering hidden insights from data collected across various educational environments [1] . L'existing literature has mainly aimed at predicting academic performance by exploring the impact of students ‘external environment on their academic achievement studies have notably used institutional bases and international assessments, such as TIMSS, PISA and PIRLS, to identify the key factors influencing this performance [2], [3] . However, our contribution stands out by focusing on the impact of behavioral traits on student performance [4], by integrating data collected through the Kalboard 360 platform and applying advanced data mining methods, our study aims to comprehensively how these specific behaviors directly influence academic success.  \nThe following sections of this article will demonstrate how our innovative methodological approach fills these gaps by providing valuable insights to improve educational strategies and guide future research in this crucial area [5], we apply six machine learning algorithms: decision tree, random forests (RF), k-nearest neighbors (KNNs) and support vector machines (SVM) to build a robust academic performance model [6], [7] . The goal of this study is to promote the continuous improvement of teaching methods,  \nparticularly by helping teachers in the diagnostic and summative assessment phases to analyze student behavior in the classroom.  \nMuch study has been conducted on forecasting student performance and behavior in the classroom, includes a variety of innovative ways and tools for achieving goals, gathering information, making decisions, and making recommendations. Some of the information used as a source for this article is included below. The authors concluded that the school administration and atmosphere have an impact on students' academic performance [8], [9] On the other hand, The authors found that the teacher is ","cbCaimttcfXK1axs","https://ap.wps.com/l/cbCaimttcfXK1axs","pdf",580698,1,"English","en",105,"# Introduction\n## Background and motivation\n## Research gap and contribution\n## Existing studies\n# Method Overview\n## Machine learning algorithms used\n## Data collection and modeling approach\n# Discussion and Potential Impact\n## Educational strategies and teacher support","[{\"question\":\"What problem does the study address in educational data mining?\",\"answer\":\"It focuses on extracting insights from educational data to understand how student behaviors and interactions relate to academic success.\"},{\"question\":\"Which data sources and platform are used to build the prediction model?\",\"answer\":\"The model uses Experience API (xAPI) data collected through the Kalboard 360 online learning platform.\"},{\"question\":\"Which machine learning algorithms are applied to predict student status?\",\"answer\":\"The study applies logistic regression, k-nearest neighbors, support vector machines, decision trees, random forests, and XGBoost.\"}]","Predicting Student Status Using Machine Learning by Analyzing Classroom Behaviors with X-API Data | 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