mobile learning adoption in higher education in guyana

1 Company Proprietary and Confidential

Mobile Learning Adoption in Higher Education in

GuyanaTroy Thomas

Lenandlar Singh

Kemuel Gaffar

University of Guyana

November 4, 2012


OUTLINE

• Background

• Hypotheses

• Data and Method

• Results

• Discussion

• Conclusion

• Recommendations and Future Work


BACKGROUND

• E-learning and technology has transformed the educational landscape worldwide giving rise to new pedagogical systems

• One area of e-learning that is gaining increasing popularity and attention is mobile learning (MLearning) - a new educational paradigm!

• MLearning allows "anywhere", "anytime" teaching and learning!

• Constantly evolving with rapid increase of mobile devices and changing lifestyles of people

• Ubiquitous and Pervasive


BACKGROUND

• Research in m-learning still in its infancy stage

• Wang et al. (2010) indicates that studies that explore the best practice of m-learning are largely undefined.

• Lack of empirical evidence to show that mobile technology engages students and promote learning (Hlodan, 2010)

• Need for systematic studies that examine instructors’ and students’ m-learning experience AND factors that affect adoption and acceptance


TECHNOLOGY ACCEPTANCE

• Most MLearning studies are based on the Technology Acceptance Model - TAM (Davis, 1989)

• or the Unified Theory of Acceptance and Use of Technology - UTAUT (Venkatesh et al., 2003)

• Objective is to identify factors that influence adoption and relationship among factors


THE UTAUT MODEL - Venkatesh et al. (2003)


THE UTAUT MODEL

• UTAUT model should explain 70% of the variance in BI (Venkatesh et al., 2003)

• In the UTAUT model, the interactions are essential to finding significant effects of SF on BI and FC does not affect BI (Venkatesh et al., 2003)

• Most MLearning in HE studies are done in Western countries (Schepers and Wetzels, 2007; Traxler, 2007). Research emerging in non-Western context.


THE UTAUT MODEL

• Do the hypothesized UTAUT relationships hold in non-Western context?

• Jairak et al. (2009) (Thailand) finds an effect of FC on BI and no effect of PE on BI (violations of UTAUT)

• Nassuora (2012) (Saudi Arabia) find no effect of SF on BI (may be due to omission of interactions.)

• Im, Hong and Kang (2011) (US and Korea) confirm the UTAUT relationships (even without interactions)


HYPOTHESIS

H1 : PE is positively related to BI

H2 : EE is positively related to BI

H3 : SF is positively related to BI

H4 : ATT is positively related to BI

H5 : PE is positively related to ATT

H6 : EE is positively related to ATT

H7 : SF is positively related to ATT

H8 : FC is positively related to ATT

H9: FC is positively related to BI


CONCEPTUAL STRUCTURAL FRAMEWORK


DATA AND METHOD

• Large scale (online) survey of students

• Survey sent to entire student population via email

• 322 usable responses collected (43.4% males, 56.5% females)


METHOD

• Modified UTAUT framework and survey items adopted from Jairak et al. (2009) - used 5-point rating scales

• Confirmatory Factor Analysis (CFA) to determine whether the factors are retrieved from the data.

• Reliability of factors and validity of loadings evaluated.

• Model fit: RMSEA (<0.06) and CFI (>0.95) fit indices used. Chi-square too sensitive for large samples (Chen 2007; Hui & Bentler 1999).


RESULTS - CFA

• Initial CFA did not fit very well (RMSEA= 0.08, CFI=0.98)

• Modified one correlation between error terms (modification index = 135.96). FC2 and FC3 are correlated.

• Revised model fits well (RMSEA 0.059, CFI=0.98). Change in Chi-square = 156.89 for 1 df.

• Factor Loadings - generally greater than 0.7 (validity)

• Internal Consistency Reliability (Cronbach alpha greater than 0.7)


RESULTS - SEM

• Initial CFA did not fit very well (RMSEA= 0.08, CFI=0.98)

• Modified one correlation between error terms (modification index = 135.96). FC2 and FC3 are correlated.

• Revised model fits well (RMSEA 0.059, CFI=0.98). Change in Chi-square = 156.89 for 1 df.

• Factor Loadings - generally greater than 0.7 (validity)

• Internal Consistency Reliability (Cronbach alpha greater than 0.7)


RESULTS - SEM


CONCLUSION• 59% of BI explained (Lower than 70% for UTAUT).

Researches should experiment with other variables to explain more variance.

• FC affects BI (even without ATT) - contradicts UTAUT and Nassuora 2012.

• EE does not affect BI (even without ATT) - contradicts UTAUT and Jairak et al. 2009

• FC affects Attitude - contradicts Jairak et al. 2009

• SF affects BI without interactions - contradicts UTAUT

• All the other hypotheses confirmed. UTAUT still valuable.

• The exploratory treatment is warranted!


CONCLUSION – HE at UG

• The usefulness (PE) of the technologies is more important than how easy they are to use (EE).

• Attitude and the FC are most important to adoption. The effect of limited resources.

• PE, EE, FC predict ATT and SF predicts BI so approach adoption promotion needs to be holistic. All factors are important!

• • 55% (R-squared) of the variance in ATT is

explained. Opportunity for research on attitude promotion.


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REFERENCESIm, I., Hong, S., and Kang, M. S. (2011). An international comparison of technology

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Jairak, K., Praneetpolgrang, P., and 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, 17(SP3):36.1–36.8.

Nassuora, A. B. (2012). Student Acceptance of Mobile Learning for Higher Education. American Academic & Scholarly Research Journal, 4(2):0–5.

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Teo, T. (2011). Technology Acceptance in Education. In Teo, T., editor, Technology Acceptance in Education: Research and Issues, pages 1–5. Sense Publishers, Rotterdam.


REFERENCES

Traxler, J. (2005). Defining mobile learning. In IADIS International Conference Mobile Learning 2005, number September 2004, pages 261–266.

Traxler, J. (2007). Defining, Discussing, and Evaluating Mobile Learning: The moving finger writes and having writ…. International Review of Research in Open and Distance Learning, 8(2):1–12.

van Raaij, E. M. and Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3):838–852.

Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3):425–478.

Wang, Y.-S. and Shih, Y.-W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, 26(1):158–165.


END OF PRESENTATION

Thank You!

Kemuel Gaffar

[email protected]