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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Penerbit UiTM (UiTM Press),
2015.
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| Online-ссылка: | Link Metadata |
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| 100 | 1 | 0 | |a Haji Ali, Noraida |e author |
| 700 | 1 | 0 | |a W. Hamzah, W.M. Amir Fazamin |e author |
| 700 | 1 | 0 | |a Yusoff, Hafiz |e author |
| 700 | 1 | 0 | |a Saman, Md Yazid |e author |
| 245 | 0 | 0 | |a Predicting user acceptance of e-learning applications: web usage mining approach / Noraida Haji Ali ... [et al.] |
| 260 | |b Penerbit UiTM (UiTM Press), |c 2015. | ||
| 500 | |a https://ir.uitm.edu.my/id/eprint/59613/1/59613.pdf | ||
| 520 | |a 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. | ||
| 546 | |a en | ||
| 655 | 7 | |a Article |2 local | |
| 655 | 7 | |a PeerReviewed |2 local | |
| 787 | 0 | |n https://ir.uitm.edu.my/id/eprint/59613/ | |
| 787 | 0 | |n https://journalined.uitm.edu.my/ | |
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