Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]

The successful implementation of e-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 applicat...

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Main Authors: Haji Ali, Noraida, W. Hamzah, W.M. Amir Fazamin, Yusoff, Hafiz, Saman, Md Yazid
Format: Article
Language:English
Published: Penerbit UiTM (UiTM Press) and I-Learn Centre 2016
Subjects:
Online Access:http://ir.uitm.edu.my/id/eprint/16122/
http://ir.uitm.edu.my/id/eprint/16122/1/AJ_NORAIDA%20HAJI%20ALI%20IJEL%2016.pdf
id uitm-16122
recordtype eprints
spelling uitm-161222017-02-16T04:46:13Z http://ir.uitm.edu.my/id/eprint/16122/ Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.] Haji Ali, Noraida W. Hamzah, W.M. Amir Fazamin Yusoff, Hafiz Saman, Md Yazid Computers in education. Information technology Blended learning. Computer assisted instruction. Programmed instruction The successful implementation of e-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 (UTAUT) for predicting of user acceptance of e-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 of e-learning applications. Penerbit UiTM (UiTM Press) and I-Learn Centre 2016 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/16122/1/AJ_NORAIDA%20HAJI%20ALI%20IJEL%2016.pdf Haji Ali, Noraida and W. Hamzah, W.M. Amir Fazamin and Yusoff, Hafiz and Saman, Md Yazid (2016) Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]. International Journal on E-Learning and Higher Education, 4. pp. 82-95. ISSN 1985-8620
repository_type Digital Repository
institution_category Local University
institution Universiti Teknologi MARA
building UiTM Institutional Repository
collection Online Access
language English
topic Computers in education. Information technology
Blended learning. Computer assisted instruction. Programmed instruction
spellingShingle Computers in education. Information technology
Blended learning. Computer assisted instruction. Programmed instruction
Haji Ali, Noraida
W. Hamzah, W.M. Amir Fazamin
Yusoff, Hafiz
Saman, Md Yazid
Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]
description The successful implementation of e-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 (UTAUT) for predicting of user acceptance of e-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 of e-learning applications.
format Article
author Haji Ali, Noraida
W. Hamzah, W.M. Amir Fazamin
Yusoff, Hafiz
Saman, Md Yazid
author_facet Haji Ali, Noraida
W. Hamzah, W.M. Amir Fazamin
Yusoff, Hafiz
Saman, Md Yazid
author_sort Haji Ali, Noraida
title Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]
title_short Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]
title_full Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]
title_fullStr Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]
title_full_unstemmed Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali … [et al.]
title_sort predicting user acceptance of e-learning applications: web usage mining approach / noraida haji ali … [et al.]
publisher Penerbit UiTM (UiTM Press) and I-Learn Centre
publishDate 2016
url http://ir.uitm.edu.my/id/eprint/16122/
http://ir.uitm.edu.my/id/eprint/16122/1/AJ_NORAIDA%20HAJI%20ALI%20IJEL%2016.pdf
first_indexed 2023-09-18T22:55:22Z
last_indexed 2023-09-18T22:55:22Z
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