Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Golay, Harsh; Sharan, Mudita; Shoran, Preety Title A name given to the resource Student Performance Prediction in Learners Centric Approach with Machine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived EPJ Web of Conferences;Volume;341;Issue;;Article No.;1033; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1051/epjconf/202534101033" target="_blank" rel="noreferrer noopener">https://doi.org/10.1051/epjconf/202534101033</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024551970?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024551970?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Golay H., Department of Computer Science, Christ University, Bangalore, India; Sharan M., Department of Computer Science, Christ University, Bangalore, India; Shoran P., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource Predicting student performance helps educators locate students who are at risk and tailor appropriate and timely interventions for those students. This research proposes a learner-centered machine learning framework to integrate demographic, academic and behavioral features in order to predict student grade performance. The dataset consists of 2392 students and 15 attributes including age, gender, parental education, study time, absences, and extracurricular activities. Four supervised learning models - Logistic Regression, Decision Tree, Random Forest and Support Vector Machines (SVM) were trained and measured using 70:30 stratified split. The performance of the model was evaluated using accuracy, precision, recall, and F1-score metrics. Among these, Decision Tree classifier achieved the highest accuracy (92.48%) which was followed by Random Forest (88.31%), SVM (83.51%) and Logistic Regression (75.16%). The results show that such factors as study time, absences, and parental involvement were the most predictive. The proposed learner-centered approach shows that the combination of contextual, behavioral, and academic data can greatly increase the predictive accuracy and the interpretability of the data, facilitating early risk detection and intervention in education. The Authors, published by EDP Sciences. Subject The topic of the resource Decision tree; Education data mining; Learner centric methodology; Random forest; Student performance prediction.; SVM Publisher An entity responsible for making the resource available EDP Sciences Relation A related resource ISSN: 21016275; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online