Please use this identifier to cite or link to this item: http://umt-ir.umt.edu.my:8080/handle/123456789/5446
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dc.contributor.authorNoraida Haji Ali-
dc.contributor.authorW.M. Amir Fazamin W. Hamzah-
dc.contributor.authorHafiz Yusof-
dc.contributor.authorMd Yazid Saman-
dc.date.accessioned2017-04-09T08:13:16Z-
dc.date.available2017-04-09T08:13:16Z-
dc.date.issued2016-01-
dc.identifier.citationVol.4en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5446-
dc.description.abstractThe successful implementation ofe-learning applications is closely related to user acceptance. Previous studies show the use of log files data in the web usage mining to predict user acceptance. However, the log files data did not record the entire behaviour of users who use the e-learning applications that are embedded in a website. Therefore, this study has proposed the web usage mining using Tin Can API to gather user s data. The Tin Can API will be used to track and to record user behaviours in e-learning applications. The generated data have been mapped to the Unified Theory of Acceptance and Use of Technology (UTA UT) for predicting of user acceptance ofe-learning applications. From regression analysis, the results showed the performance expectancy and effort expectancy were found directly and significantly related to the intention to use e-learning applications. Behavioural intention and facilitating conditions also were found directly and significantly related to the behaviour of use of e-learning applications. Thus, the approach of web usage mining using Tin Can API can be used to gather usage data for predicting user acceptance ofe-learning applications.en_US
dc.language.isoenen_US
dc.publisherInternational journal on e-learning and higher educationen_US
dc.subjectE-learningen_US
dc.subjectUser acceptanceen_US
dc.subjectUTAUT modeen_US
dc.subjectWeb usage miningen_US
dc.titlePredicting User Acceptance of e-Learning Applicationsen_US
dc.title.alternativeWeb Usage Mining Approachen_US
Appears in Collections:Journal Articles

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