Predicting actual use of mobile technology in learning: towards e-learning culture framework
With the advent of communication technology evolution, learning becomes flexible and accessible at any time and anywhere. Educational technology researchers have extensively integrated the Theory of Acceptance Model (TAM) and Theory of Planned Behavior to link the beliefs and actions in mobile learn...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Science Publishing Corporation Inc
2018
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/64301/ http://irep.iium.edu.my/64301/ http://irep.iium.edu.my/64301/ http://irep.iium.edu.my/64301/1/64301_Predicting%20Actual%20Use%20of%20Mobile%20Technology.pdf |
| Summary: | With the advent of communication technology evolution, learning becomes flexible and accessible at any time and anywhere. Educational technology researchers have extensively integrated the Theory of Acceptance Model (TAM) and Theory of Planned Behavior to link the beliefs and actions in mobile learning. This study attempted to predict a hypothesized model of mobile learning culture via smart phone. In this present study, the m-learning culture is conceptualized from the factors of attitude, ethical use, technology competent; technology reliance and social well being. Social well being from the perspectives of The Adaptive Structuration has been
integrated to underpin the study. 185 samples were drawn from the population of Korea University. Using self constructed questionnaire for the survey, the analyses involved descriptive and simultaneous Multiple Regression Analysis (MRA). The study was supported by qualitative design via interviews. The findings indicate all predictors are significant except for technology reliance and ethical use. Social well being is the highest predictor for the m-learning via smartphone. This study has been explored from both quantitative and qualitative research which provide important empirical information to support m-learning culture and its predictors.
The findings have contributed to a model of m-learning which extends the literature and existing models of TAM and Theory of Planned Behavior |
|---|