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dc.contributor.authorCebeci, Halil Ibrahim
dc.contributor.authorYazgan, Harun Resit
dc.contributor.authorGeyik, Abdulkadir
dc.date.accessioned2019-10-25T04:49:51Z
dc.date.available2019-10-25T04:49:51Z
dc.date.created2017-09-25 09:27
dc.date.issued2009-03-01
dc.identifieroai:rlt.journals.sfu.ca:article/895
dc.identifierhttps://journal.alt.ac.uk/index.php/rlt/article/view/895
dc.identifier10.3402/rlt.v17i1.10772
dc.identifier.urihttp://hdl.handle.net/20.500.12424/1323716
dc.description.abstractThis study explores the relationship between the student performance and instructional design. The research was conducted at the E-Learning School at a university in Turkey. A list of design factors that had potential influence on student success was created through a review of the literature and interviews with relevant experts. From this, the five most import design factors were chosen. The experts scored 25 university courses on the extent to which they demonstrated the chosen design factors. Multiple-regression and supervised artificial neural network (ANN) models were used to examine the relationship between student grade point averages and the scores on the five design factors. The results indicated that there is no statistical difference between the two models. Both models identified the use of examples and applications as the most influential factor. The ANN model provided more information and was used to predict the course-specific factor values required for a desired level of success.Keywords: e-learning; distance education; instructional design factors; multimedia systems; artificial neural networksDOI: 10.1080/09687760802649889
dc.format.mediumapplication/pdf
dc.language.isoeng
dc.publisherResearch in Learning Technology
dc.relation.ispartofhttps://journal.alt.ac.uk/index.php/rlt/article/view/895/1146
dc.sourceResearch in Learning Technology; Vol 17, No 1 (2009)
dc.subjecte-learning
dc.subjectdistance education
dc.subjectinstructional design factors
dc.subjectmultimedia systems
dc.subjectartificial neural networks
dc.titleA comparative analysis of the effects of instructional design factors on student success in e-learning: multiple-regression versus neural networks
dc.typeinfo:eu-repo/semantics/article
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:11306460
ge.identifier.permalinkhttps://www.globethics.net/gel/11306460
ge.lastmodificationdate2017-09-25 09:27
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid148950
ge.oai.repositoryid98390
ge.oai.setnameOriginal Research Articles
ge.oai.setspecrlt:ORA
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttps://journal.alt.ac.uk/index.php/rlt/article/view/895


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