Faculty of Informatics, Doctor of Philosophy Program in Information Technology The sixth International Conference on eLearning for knowledge-Based Society.

The sixth International Conference on eLearning for knowledge-Based Society 17 -18 December 2009, Bangkok Metro, Thailand

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AN ACCEPTANCE OF MOBILE LEARNING FOR HIGHER EDUCATION STUDENTS IN THAILAND

Kallaya Jairak Prasong Praneetpolgrang Kittima Mekhabunchakit

Faculty of Informatics, Doctor of Philosophy Program in Information Technology

Sripatum University, Bangkok, Thailand 10900


OutlineIntroduction

Theories Background

Research Framework

Hypotheses

Research Results

Conclusion

References

1

2

3

4

5

6

7

8

Research Methodology


Introduction • The major equipment of e-Learning that focus on learning anytime and anywhere is Personal computer.

• Because of the physical limitations of the Personal Computer.

• The m-Learning fulfilled in the ubiquitous learning idea.


Introduction • The user perception of m-Learning in each countries have difference in context.

www.abjade.com/Portals/0/mobile_learning7.jpg

https://www.cs.tcd.ie/~xili/mobilelearning/links.htmlwww.nottingham.ac.uk/lsri/ehy/images/pda.JPG


Introduction (Cont.)

Source :National Statistical office of Thailand (TNSO), “Explore the use of information technology and communications in the household 2008,” 2009. [Online].http://service.nso.go.th/nso/nsopublish/service/download/ictHRep51.zip . [Accessed: Oct. 22, 2009]

28.2%36.7%

41.6% 47.3%

52.8%


Objective• To study the level of acceptance

on mobile Learning (m-Learning) for higher education students in Thailand.


• Technology Acceptance Model (TAM)• Unified Theory of Acceptance and

Use of Technology Model (UTAUT)

Theories Background


Source : http://www.eportfolios.ac.uk/mobile/mobiles.jpg. [Online].[Accessed: Oct. 22, 2009]

Mobile Devices


Original Technology Acceptance Model (TAM)

External

Variables

Attitude

Towards

Behavior

Behavioral

Intention

To Use

Actual

System

To Use

Perceived

Usefulness (PU)

Perceived

Ease of Use

(PEOU)

Source : F.D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, ” MIS Quarterly, 13, pp. 319-340,1989.


Unified Theory of Acceptance and Use of Technology Model (UTAUT)

PerformanceExpectancy

(PE)

EffortExpectancy

(EE)

UseBehavior

(UB)

Gender

SocialFactors

(SF)

Facilitating Conditions

(FC)

BehavioralIntention

(BI)

Age Experience VoluntarinessOf Use

Source : V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: toward a unified view,” MIS Quarterly, vol. 27, pp. 425-478. 2003.


Research FrameworkPerformanceExpectancy

(PE)

Social Factors (SF)

FacilitatingConditions

(FC)

BehavioralIntentionTo UseEffort

Expectancy(EE)

AttitudeTowardsBehavior

H1

H2

H3

H4

H5

H6

H7

H8

H9

Research Framework


Research Hypotheses H1: PE has a significant positive relationship with BI.H2: EE has a significant positive relationship with BI

H3: SF has a significant positive relationship with BI.

H4: FC has a significant positive relationship with BI.

H5: PE has a significant positive relationship with AT.

H6: EE has a significant positive relationship with AT.

H7: SF has a significant positive relationship with AT.H8: FC has a significant positive relationship with AT.

H9: AT has a significant positive relationship with BI.


• The Data Collection and Analysis.• Reliability of Instruments.• Analysis Research Result.

Research Methodology


The Data Collection• The sample of 400 students and we can collected

questionnaires students set that is a• We collected data in five universities.

– Sripatum University – Payap University

– North-Chiangmai University

– Rajamangala University of Technology Lanna.

– Rajabhat Chiangmai University

390 97.50%


Factor analysis and ReliabilityComponent

1 2 3 4 5 6PE1 0.759PE2 0.714PE3 0.714PE4 0.671EE1 0.794EE2 0.807EE3 0.757

SF1 0.862SF2 0.855SF3 0.390FC1 0.562FC2 0.817FC3 0.834FC4 0.696A1 0.639A2 0.708A3 0.466BI1 0.807BI2 0.823BI3 0.819

Alpha Value

0.811 0.913 0.821 0.842 0.791 0.796

Cronbach’s alpha coefficients range from 0.79 to 0.91

PE

EE

SF

FC

AI

BI


Research Result Part IDemographic attributes of the respondents

Items Frequency Percent (%)

1. Gender

Male 116 29.7 Female

Total 390 1002. Type of location of University

Public University Private University 131 33.6

274 70.3

259 66.4


Research Result Part I (Cont.)The demographic attributes of the respondents

(95.10)%


Research Result Part I (Cont.)


Research Result Part I (Cont.)The most influential person for students in m-learning usage.

40.3 %40.8 % 16.7 %


Research Result Part IIThe level of Acceptance on m-learning

in each of constructs.

X


Research Result of Regression Analysis

X

Performance

Expectancy

Social

Factors

Facilitating

Conditions

Behavioral

Intention

To UseEffort

Expectancy

Attitude

Towards

Behavior

0.095*

0.274***

0.257***

0.398***

0.278***

0.219***

0.142**

* Significant at p < 0.05, ** Significant at p < 0.01, *** Significant at p < 0.001

H2

H3

H4

H5

H6

H7

H9

H1

H8


Research Result Part III• Easy to Use.

• Interesting interface. • Training before to use m-Learning.

www.ucea.edu/profdev/seminars/2008academyalumni/


• Although more than half of the students in this study have not familiar with m-Learning but they have good perception and

have high level of acceptance on m-Learning.

• The administration of university should focus on how to design m-Learning system that fit and appropriate to student’s

perception.

Conclusions


[1] D. Phuangthong and S. Malisawan, “A study of behavioral intention for 3G mobile Internet technology: Preliminary research on mobile learning,” Proceedings of the Second International Conference on eLearning for Knowledge-Based Society, 2005.

[2] Y. Liu, F. Hu, and H. Li, “Understanding learners' perspectives on m-learning: results from a survey,” Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to increase Digital Citizenship, 2009.

[3] M. Virvou and E. Alepis, “Mobile educational features in authoring tools for personalised tutoring,” Computers & Education, vol. 44, pp. 53–68, 2005.

[4] P.Y. Chao and G.D. Chen, “Augmenting paper-based learning with mobile phones,” Interacting with Computers, vol. 21, pp. 173–185, 2009.

[5] R. Brown, H. Ryu, and D. Parsons, “Mobile helper for university students: a design for a mobile learning environment,” Proceedings of the 18th Australia conference on Computer-Human Interaction: Design: Activities, Artefacts and Environments, 2006, p. 300.

References


[6] Z. Liu, G. Zhao, W. Zheng, and J. Jin, “The Research and Exploration of Mobile-Learning Based on Web2. 0,” Knowledge Acquisition and Modeling, 2008. KAM'08. International Symposium on, 2008, pp. 520–524.

[7] Paul TJ James, “The 5th Challenges and Opportunities for Mobile-learning in Thailand,” Fifth International Conference on eLearning for Knowledge-Based Society, 2008.

[8] Q.Min, S. JI, and G. QU, “Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model,” Tsinghua Science And Technology, pp. 257-264, 2008.[9] I. Ajen and M. Fishbein, Understanding Attitudes and Predicing Social Behavior, Prentice Hall, Englewood Cliffs, NJ, 1980.[10] F.D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13, pp. 319-340, 1989.

References (Cont.)


[11] F. D. Davis, R. P. Bagozzi, and P. r. Warshaw, “User Acceptance of Computer Technology: A comparison of Two Theoretical Models,” Management Science, 35 (8), pp. 982-1003, 1989.[12] Ajzen I, Madden T J, “Prediction of goal directed behaviors: Attitudes, intentions, and perceived behavioral control,” Journal of Experimental Social Psychology, vol. 22(5), pp. 453-474, 1986.[13] I. Ajzen, “The theory of planned behavior,” Organizational behavior and human decision processes, vol. 50, pp. 179–211, 1991.[14] E.M. Rogers, Diffusion of innovations, Free press, 1995.[15] V. Venkatesh and F.D. Davis, “A theoretical extension of the technology acceptance model: Four longitudinal field studies,” Management science, vol. 46, 2000, pp. 186–204.

References (Cont.)


[16] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: toward a unified view,” MIS Quarterly, vol. 27, pp. 425-478. 2003. [17] Quan Sun, Chunlei Wang and Hao Cao, “An extended TAM for Analyzing Adoption Behavior of mobile commerce,” Proc. 8th Int’l Conf. Mobile Business. (ICMB2009), IEEE CS Press, pp. 52-56, 2009.[18] K.C. Yang, “Exploring factors affecting the adoption of mobile commerce in Singapore,” Telematics and Informatics, vol. 22, pp. 257–277, 2005.[19] M. Masrom and R. Hussein, User Acceptance of Information Technology : Understanding Theories and Model, Venton Publishing(M) Sd n. Bhd, Selangor Darul Ehsan, Malaysia, 2008.[20] T. Zhou, “Exploring Mobile User Acceptance Based on UTAUT and Contextual Offering,” Electronic Commerce and Security, 2008 International Symposium on, pp. 241–245. 2008.[21] J. Traxler, “Defining mobile learning,” Proceedings, IADIS international conference on mobile learning, Malta, 2005.

References (Cont.)


Thank youQuestion & AnswerEmail : [email protected]

Faculty of Informatics, Doctor of Philosophy Program in Information Technology The sixth International Conference on eLearning for knowledge-Based Society.

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