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Learning Analytics and Online Courses: A Bayesian Belief Network Approach to Predict Success

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dc.contributor.author Nirban, Virendra Singh
dc.contributor.author Shukla, Tanu
dc.date.accessioned 2024-05-17T10:01:45Z
dc.date.available 2024-05-17T10:01:45Z
dc.date.issued 2022-11
dc.identifier.uri https://link.springer.com/chapter/10.1007/978-981-19-5331-6_40
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/14934
dc.description.abstract In the field of educational research, learning analytics is one of the prevailing areas of exploration. The study explores a part of learning analytics using a Bayesian networks (BN) model to predict the success of the course in the online mode of education. Through the simulation results, it was found that the BN approach can be used to suggest improved online instruction delivery methods, helping the instructors and students reform their practices to maintain a synergy for a successful running of the course. As the study was executed on engineering students, it could further be generalized using students of other streams for comprehensive understanding. The study reveals that the student synergy with the method of teaching, paper difficulty, and take-home assignments are found to be the main determinants of the success of E-learning courses. The study reveals that student’s synergy with the method. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.subject Humanities en_US
dc.subject Bayesian Networks (BN) en_US
dc.title Learning Analytics and Online Courses: A Bayesian Belief Network Approach to Predict Success en_US
dc.type Article en_US


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