Prediction of e-learning efficiency by neural networks

Simple item page
creativework.keywordsBalanced scorecard, e-Learning efficiency, Neural networks
creativework.publisherBulgarska Akademiya na Naukite 1iict@bas.bgen
dc.contributor.authorHalachev P.
dc.date.accessioned2024-07-10T14:27:03Z
dc.date.accessioned2024-07-10T14:47:59Z
dc.date.available2024-07-10T14:27:03Z
dc.date.available2024-07-10T14:47:59Z
dc.date.issued2012-01-01
dc.description.abstractA model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.
dc.identifier.doi10.2478/cait-2012-0015
dc.identifier.issn1314-4081
dc.identifier.issn1311-9702
dc.identifier.scopusSCOPUS_ID:84868657334en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/260
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84868657334&origin=inward
dc.titlePrediction of e-learning efficiency by neural networks
dc.typeArticle
oaire.citation.issue2
oaire.citation.volume12
Files
Collections
Articles