Impact analysis of online education development and implementation using machine learning model

dc.contributor.authorDivakarla, U.
dc.contributor.authorChandrasekaran, K.
dc.date.accessioned2026-02-08T16:49:51Z
dc.date.issued2024
dc.description.abstractOnline education is becoming increasingly necessary and in high demand as a result of the current circumstances and the enormous expansion in internet users. Various studies have been done in this area to enhance the positive benefits of offering educational courses online. One of the most crucial concerns for learning contexts like schools and universities, especially during current epidemic period, is the prediction and analysis of students' performance since it aids in the development of practical mechanisms that enhance academic achievement and prevent dropout. Most educational institutions now place a high priority on forecasting and analysing student performance. That is necessary to assist at-risk students, ensure their retention, provide top-notch learning tools and opportunities, and enhance the university's ranking and reputation. This project aims to collect information related to online education and use Machine Learning to predict students' performance. © 2024 Bentham Science Publishers. All rights reserved.
dc.identifier.citationVirtual Lifelong Learning: Educating Society with Modern Communication Technologies, 2024, Vol., , p. 183-199
dc.identifier.isbn9789815196566
dc.identifier.isbn9789815196573
dc.identifier.urihttps://doi.org/10.22104/aet.2025.7210.1990
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/33522
dc.publisherBentham Science Publishers
dc.subjectClassification algorithms
dc.subjectDecision tree
dc.subjectMachine learning
dc.subjectNaive bayes
dc.subjectPrediction
dc.subjectRandom forest
dc.subjectSupport vector machine algorithm
dc.subjectWEKA tool
dc.titleImpact analysis of online education development and implementation using machine learning model

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