Abstract
References
- Bakia, M., Shear, L., Toyama, Y., & Lasseter, A. (2012). Understanding the Implications of Online Learning for Educational Productivity. Office of Educational Technology, US Department of Education.
- Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Square (PLS). Approach to causal modeling: Personal computer adoption and use as an illustration. Technol. Stud, 2(2), 2.
- Chen, K.-C., & Jang, S.-J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741–752. https://doi.org/10.1016/j.chb.2010.01.011
- Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
MOTIVATIONAL FACTORS UNDERLYING THE USE OF ONLINE LEARNING SYSTEM IN HIGHER EDUCATION: AN ANALYSIS OF MEASUREMENT MODEL
Abstract
Online learning is a flexible and distributed distance learning system. The motivation of lecturers and students is one of key factors determining the acceptance and use of online learning in higher education. This research is aimed at empirically developing and testing a measurement model of several motivational constructs with the assumptions of indicators that build it. This research proposes a theoretical model which can be integrated into three motivational theories: ARCS, McClelland’s needs, and Self-Determinant Theory (SDT). The construct indicators were developed and then validated empirically at two universities in Makassar, Indonesia. A quantitative method with survey approach was used. The research sample consisted of 71 lecturers and 210 students selected purposively. The analysis of measurement models used partial least square (PLS). The results show that the construct of motivation with indicators that built it met validity and reliability requirements. The results of this research present two alternative instruments for explaining the relationship between motivational factors including the indicators that influence the use of online learning systems in tertiary institutions.
References
- Bakia, M., Shear, L., Toyama, Y., & Lasseter, A. (2012). Understanding the Implications of Online Learning for Educational Productivity. Office of Educational Technology, US Department of Education.
- Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Square (PLS). Approach to causal modeling: Personal computer adoption and use as an illustration. Technol. Stud, 2(2), 2.
- Chen, K.-C., & Jang, S.-J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741–752. https://doi.org/10.1016/j.chb.2010.01.011
- Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | December 31, 2020 |
| Submission Date | February 20, 2020 |
| Published in Issue | Year 2021 Volume: 22 Issue: 1 |