_mobile learning lecturers versus students on usage and perception using the utaut model
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This study investigated the usage and perceptions of mobile learning (m-learning) among lecturers and students at the University of Guyana using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Surveys were administered to 508 students and 63 lecturers to examine factors influencing attitudes and intentions to use m-learning technologies. Path analysis found performance expectancy and facilitating conditions significantly influenced behavioral intention for students, while effort expectancy was most influential for lecturers. Overall, students showed higher levels of performance expectancy, effort expectancy, social influence, and attitudes toward m-learning compared to lecturers. The study recommends further research with larger lecturer samples and addressing facilitating conditions to influence lecturer use of m-learning.
_mobile learning lecturers versus students on usage and perception using the utaut model
- 1. Mobile Learning: Lecturers versus Students on Usage and Perception using the UTAUT Model Lenandlar Singh Department of Computer Science, University of Guyana [email protected] Kemuel Gaffar Department of Computer Science, University of Guyana [email protected] Troy Thomas Department of Mathematics, Physics and Statistics, University of Guyana [email protected]
- 2. Outline • Introduction • Background • Review of Literature • Research Questions • Methodology • Results • Discussion • Conclusion • Recommendations and Future Work
- 3. Introduction • E-learning has transformed the educational landscape worldwide • Technology is fundamentally changing the way we teach and learn • New pedagogical models, curriculum delivery methods and learning systems. • One area of e-learning that is gaining increasing popularity and attention is mobile learning (m-learning) • M-learning has essentially extended the reach of e-learning and distance education systems by allowing educators and students to teach and learn anywhere, anytime and on the move
- 4. Introduction
- 5. Background & Context • Despite increased use of mobile devices, Guyana is faced with several problems o Bandwidth issues o Cost of equipment /service o Lack of competition (only 2 mobile providers) • With these in mind, m-learning at the University of Guyana appears far- fetched o Lack of policy o Inadequate infrastructure o Cost of hardware and software systems o Culture? • M-learning is an opportunity for the University of Guyana and similar environments o What opportunities exist? o Lack of experimentation and perception!
- 6. Literature Review • Research in m-learning still in its infancy stage • Experimentation mostly in developed countries with affordable mobile technology and fast-paced developments • Wang et al. (2010) claims that studies that explore the best practice of m- learning are largely undefined. • Need for systematic studies that examine instructors’ and students’ m- learning experience • Lack of empirical evidence to show that mobile technology engages students and promote learning (Hlodan, 2010) • Jairak et al. (2009) found positive effects of various factors on attitude to technology and behavioural intention.
- 7. Research Questions (i) To investigate the extent of ownership and usage of mobile devices by staff and students at the University of Guyana. (ii) To investigate the relationships among the elements of the UTAUT model with specific focus on how they influence attitude towards and intentions to use M- learning technologies. (iii) To assess the relative propensity for adoption of M- learning between students and lecturers.
- 8. Method Large scale (online) survey of students and lecturers • Approximately 10% response rate from student population (508 responses) • Approximately 20% response rate from lecturers after supplementary paper survey (63 responses) Survey items • Attitudinal items adopted from Jairak et al. (2009) Analysis • UTAUT (Unified Theory of Acceptance and Use of Technology) Model by Vanketesh et al. (2003)
- 9. Method Analysis • Factor analysis: Principal Component Analysis (PCA). Factor scores generated by regression method and saved. • Evaluation of internal consistency (Cronbach Alpha). • Path analysis based on the factor scores. • Comparisions of mean scale levels: Students vs. Lecturers.
- 10. Tested Model 4 The Research Model Used (Adopted from Jairak et al. (2009) with modifications)
- 11. Device Ownership
- 14. Factors from student Data
- 15. Factors from Staff Data
- 16. Path Model for Student Data
- 17. Path Model for Staff Data
- 18. Mean Scale Levels NSDx /*96.1 Scale Stud N Lect N Stud SD (Stud) SD (Lect) Lect 95% CI (Stud) Lower Upper PE 381 56 16.22 2.966 2.838 15.20 15.922 16.518 EE 390 56 12.56 2.219 2.084 10.95 12.34 12.78 SF 394 55 9.98 2.693 2.213 9.75 9.714 10.246 FC 359 54 17.19 3.895 4.120 13.69 16.787 17.593 ATT 396 56 12.79 2.111 1.678 12.14 12.582 12.998 BI 386 54 11.78 2.546 2.540 9.76 11.526 12.034 x x
- 19. Recommendations • Further research on scale development. • Further research with larger sample size for lecturers. • To influence the use of mobile learning among lecturers (if desired) the facilitating conditions inclusive of support mechanisms, and infrastructure should be addressed since this seems to have a substantial impact on behaviour.
- 20. Recommendations • The university can also seek to form relationships with technology providers so that devices and services (e.g. mobile phones) can be make available at reasonable prices. • Further experimental studies should be conducted on the effects of the use of mobile learning on the quality of student experience and on the effects on learning.
- 21. References Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. (2006), Multivariate Data Analysis, 6 edn, Pearson Education Inc. Hlodan, O. (2010). Mobile learning anytime, anywhere. BioScience, Vol. 60, No. 9 (October 2010), p. 682 Jairak, K., Praneetpolgrang, P. & Mekhabunchakij, K. (2009). An Acceptance of Mobile Learning for Higher Education Students in Thailand. Special Issue of the International Journal of the Computer, the Internet and Management, Vol. 17 No. SP3, December, 2009 Tacq, Jacques (1997) Multivariate Analysis Techniques in Social Science Research: From Problem to Analysis. SAGE Publications Ltd.
- 22. References Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, vol. 27, pp. 425-478. 2003. Wang. M., Shen. R., Novak, D., & Pan, X. (2009). The impact of mobile learning on students’ learning behaviours and performance: Report from a large blended classroom. British Journal of Educational Technology Vol. 40, No. 4 2009, 673–695
- 23. End of Presentation THANK YOU!
Editor's Notes
- #5: Define mobile learningFormalInformalUbiquitous and pervasive
- #11: Explain modelUTAUT model has been used in various contexts; one is mobile learning for example Jairak et al. (2009)Added age and gender (exogenenous) demographic factors.Jairak et al. (2009) did not find significance between PE to BI and FC to ATB (not predictors)
- #20: This should focus on determining whether the scales are distinct of whether they overlap.This would enable the use of confirmatory factor analysis which facilitate better examination of the measurement models. Both metric and scalar invariance can be tested so that we are in a better position to evaluate how well the scales are working in the population. This would also allow the use of LISREL (Structural Equation) models to test the relationships between the factors of the UTAUT model
- #21: This may help to bridge the gap between lecturers and students in terms the capability of the mobile devices especially phones.