1. Mobile Learning: Lecturers versus Students on Usage and
Perception using the UTAUT Model
Lenandlar Singh
Department of Computer Science, University of Guyana
lenandlar.singh@uog.edu.gy
Kemuel Gaffar
Department of Computer Science, University of Guyana
kemuel.gaffar@uog.edu.gy
Troy Thomas
Department of Mathematics, Physics and Statistics, University of Guyana
troy.thomas@uog.edu.gy

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

12. Phone Capabilities

13. Mobile Phone Usage

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!