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dc.contributor.authorLoncarevic, Veljko-
dc.contributor.authorLekić, Vučelja-
dc.contributor.authorDamljanovic, Nada-
dc.date.accessioned2024-10-08T07:41:08Z-
dc.date.available2024-10-08T07:41:08Z-
dc.date.issued2024-
dc.identifier.isbn9788677762766en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/21164-
dc.description.abstractThis research paper presents an approach for predicting student academic success using Hidden Markov Models (HMMs). Leveraging a comprehensive dataset encompassing students' demographics, academic performance, attendance records, and course engagement, the study employs an HMM framework to model levels of student academic success. Observable emissions derived from the data, such as grades and interaction patterns, are utilized to train the HMM and infer the most likely sequence of hidden states for new students. Evaluation of the proposed model demonstrates promising predictive accuracy. Through rigorous assessment using standard metrics including state prediction accuracy and state transition accuracy, the effectiveness of the HMM in capturing diverse student trajectories is demonstrated, underscoring the potential of HMMs as a powerful tool for understanding and predicting student outcomes, offering valuable insights for educational interventions and support systems.en_US
dc.language.isoenen_US
dc.publisherFaculty of Technical Sciences Čačak, University of Kragujevacen_US
dc.relationMSTDI - 451-03-66/2024-03/200132en_US
dc.relation.ispartof10th International Scientific Conference Technics, Informatics and Education - TIE 2024en_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectHidden Markov Modelsen_US
dc.subjectacademic success predictionen_US
dc.subjectstudent trajectoriesen_US
dc.subjectpredictive modelingen_US
dc.subjecteducational data analysisen_US
dc.titlePredicting Student Academic Success with Hidden Markov Modelsen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.identifier.doi10.46793/TIE24.068Len_US
dc.type.versionPublishedVersionen_US
Налази се у колекцијама:Faculty of Technical Sciences, Čačak

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