The combined theory of planned behaviour and technology acceptance model of mobile learning at Tehran universities

With the growth and progress of science and the constant development of new branches in the field of technologies, enormous changes have occurred in the dominion of education and learning. When observed in the narrow sense of electronic technologies, learning has outgrown its traditional frames and the electronic format has established its dominating role. Mobile learning can be observed as an emerging technology welcomed by various organisations in the present. This study examined the effect of existing factors in the composite-structural model of the theory of technology acceptance and planned behaviour on the acceptance of mobile learning by students of Tehran universities. To this end, 170 questionnaires were distributed and collected at all of the universities/each university of Tehran. Study results indicated that 85.7% of students have accepted mobile learning. Additionally, some of the survey's aspects, such as attitudinal factors, controlling beliefs factors and self-controlling beliefs, have led to a positive effect on the individual's actual behaviour in the acceptance of mobile learning.

[1]  R. Kline Principles and practice of structural equation modeling, 3rd ed. , 2011 .

[2]  Michael G. Morris,et al.  User Acceptance of Information Technology: Theories and Models , 1996 .

[3]  Kathryn Riley,et al.  Schooling The Citizens Of Tomorrow: The Challenges For Teaching And Learning Across The Global North/south Divide , 2004 .

[4]  M. Conner,et al.  Extending the Theory of Planned Behavior: A Review and Avenues for Further Research , 1998 .

[5]  Nian-Shing Chen,et al.  A context-aware video prompt approach to improving students' in-field reflection levels , 2014, Comput. Educ..

[6]  Gwo-Jen Hwang,et al.  Applications, impacts and trends of mobile technology-enhanced learning: a review of 2008-2012 publications in selected SSCI journals , 2014, Int. J. Mob. Learn. Organisation.

[7]  Shirley Taylor,et al.  Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions , 1995 .

[8]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[9]  Gwo-Jen Hwang,et al.  Ubiquitous Computing Technologies in Education , 2007, Int. J. Distance Educ. Technol..

[10]  Jaeki Song,et al.  An investigation of mobile learning readiness in higher education based on the theory of planned behavior , 2012, Comput. Educ..

[11]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[12]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[13]  Kevin Burden,et al.  Mobilising teacher education: a study of a professional learning community , 2013 .

[14]  Roy M. Harrison,et al.  Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.) , 1997 .

[15]  Gwo-Jen Hwang,et al.  Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning , 2008, J. Educ. Technol. Soc..

[16]  Mohamed Amin Embi,et al.  Mobile Learning Readiness Among UKM Lecturers , 2012 .

[17]  R. Schumacker,et al.  A beginner's guide to structural equation modeling, 3rd ed. , 2010 .

[18]  Gwo-Jen Hwang,et al.  High school teachers' perspectives on applying different mobile learning strategies to science courses: the national mobile learning program in Taiwan , 2015, Int. J. Mob. Learn. Organisation.

[19]  Pasi Sahlberg,et al.  Education Reform for Raising Economic Competitiveness , 2006 .

[20]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[21]  Carl J. Dahlman,et al.  Knowledge and Development: A Cross-Section Approach , 2004 .

[22]  David Reynolds,et al.  Smart School Improvement: Towards Schools Learning from Their Best , 2010 .

[23]  Albrecht Schmidt,et al.  Mediacups: experience with design and use of computer-augmented everyday artefacts , 2001, Comput. Networks.

[24]  Gwo-Jen Hwang,et al.  Research trends in mobile and ubiquitous learning: a review of publications in selected journals from 2001 to 2010 , 2011, Br. J. Educ. Technol..

[25]  Mary Ann Wolf,et al.  Turning on mobile learning in North-America: illustrative initiatives and policy implications , 2012 .

[26]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[27]  Gwo-Jen Hwang,et al.  An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness , 2010, J. Educ. Technol. Soc..

[28]  Jocelyn Wishart,et al.  Integrating mobile phones into teaching and learning: A case study of teacher training through professional development workshops , 2015, Br. J. Educ. Technol..

[29]  Asharul Islam Khan,et al.  Mobile Learning (M-Learning) adoption in the Middle East: Lessons learned from the educationally advanced countries , 2015, Telematics Informatics.

[30]  Moez Limayem,et al.  What makes consumers buy from Internet? A longitudinal study of online shopping , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[31]  I. Ajzen Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. , 2002 .

[32]  Gwo-Jen Hwang,et al.  A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students , 2011, Comput. Educ..

[33]  Christer Carlsson,et al.  Factors driving the adoption of m-learning: An empirical study , 2010, Comput. Educ..

[34]  Gwo-Jen Hwang,et al.  An adaptive navigation support system for conducting context-aware ubiquitous learning in museums , 2010, Comput. Educ..

[35]  E. Burton Swanson,et al.  INFORMATION CHANNEL DISPOSITION AND USE , 1987 .

[36]  D. Bot The Oxford Handbook of Applied Linguistics , 2002 .

[37]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[38]  Liang-Chun Chen,et al.  Smart phone demand: An empirical study on the relationships between phone handset, Internet access and mobile services , 2015, Telematics Informatics.

[39]  V. Opfer,et al.  Learning 21st-Century Skills Requires 21st-Century Teaching , 2012 .

[40]  Gwo-Jen Hwang,et al.  Differences between mobile learning environmental preferences of high school teachers and students in Taiwan: a structural equation model analysis , 2016 .

[41]  Gregory D. Abowd,et al.  Classroom 2000: An Experiment with the Instrumentation of a Living Educational Environment , 1999, IBM Syst. J..

[42]  Yao-Ting Sung,et al.  Mobile‐device‐supported strategy for Chinese reading comprehension , 2010 .

[43]  R. P. McDonald,et al.  Principles and practice in reporting structural equation analyses. , 2002, Psychological methods.

[44]  Erny Arniza Ahmad,et al.  The definition and characteristics of ubiquitous learning : A discussion , 2010 .

[45]  Garry Wei-Han Tan,et al.  Determinants of Mobile Learning Adoption: An Empirical Analysis , 2012, J. Comput. Inf. Syst..

[46]  Pat Hanrahan,et al.  ICrafter: A Service Framework for Ubiquitous Computing Environments , 2001, UbiComp.