Mobil Öğrenmenin Kabulü: Sistematik Literatür İncelemesi

Year 2018, , 571 - 589, 22.08.2018

Nurcan Alkış , Duygu Fındık Coşkunçay

https://doi.org/10.17556/erziefd.440974

Cited By: 5

Abstract

Eğitim
faaliyetlerinin mobil cihazlar ve uygulamalar ile desteklenmeye başlanması ile
mobil öğrenme (m-öğrenme) kavramı ortaya çıkmış ve eğitim bağlamında yeni bir
araştırma alanı olarak yerini almıştır. m-Öğrenmenin avantajlarından
faydalanabilmek için, bu öğrenme yönteminin son kullanıcılar tarafından
benimsenmesi ve kabulü önemlidir. Literatürde, kullanıcıların m-öğrenme
kabulünü etkileyen faktörleri inceleyen birçok çalışma mevcuttur. Bu
çalışma,  m-öğrenme kabulünü etkileyen
faktörleri belirleyebilmek için literatürdeki m-öğrenme kabulü çalışmalarının
sistematik bir şekilde incelenmesini amaçlamıştır. Ayrıca bu çalışmanın bir
diğer amacı m-öğrenme kabulünü tahmin etmek için kullanılan teorilerin
belirlenmesidir. Literatürdeki çalışmalara ulaşmak için sistematik inceleme
prosedürü tasarlanmış ve uygulanmıştır. Sistematik inceleme prosedürü ile elde
edinilen 51 çalışma, örneklem grupları, çalışmaların dayandığı teoriler ve
m-öğrenmenin kabulünü etkileyen faktörler ve aralarındaki ilişkiler kapsamında
incelenmiştir. Yürütülen çalışmalarda toplam 106 faktör belirlenmiş ve bu
faktörler m-öğrenmenin kabulü ve benimsenmesi bağlamında 12 boyut altında
gruplandırılmıştır. Ayrıca bu faktörler arasındaki ilişkiler incelenmiş ve
toplam 222 anlamlı ilişki bulunmuştur. 
Bu ilişkilerden, Algılanan Fayda-Davranışsal Niyet, Algılanan Kullanım
Kolaylığı-Algılanan Fayda ve Algılanan Kullanım Kolaylığı-Davranışsal Niyet
ilişkilerinin istatistiksel olarak anlamlı sonuç veren ve en çok araştırılan
ilk üç ilişki olduğu gözlenmiştir.  

Keywords

sistematik inceleme, teknoloji kabulü, teknoloji kabul modeli, mobil öğrenme

References

• Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
• Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
• Alrasheedi, M., & Capretz, L. F. (2015). Determination of critical success factors affecting mobile learning: a meta-analysis approach. TOJET: The Turkish Online Journal of Educational Technology, 14(2), 41-51.
• Arpaci, I. (2016). Understanding and predicting students' intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150-157.
• Bakhsh, M., Mahmood, A., & Sangi, N. A. (2017). Examination of factors influencing students and faculty behavior towards m-learning acceptance: An empirical study. The International Journal of Information and Learning Technology, 34(3), 166-188.
• Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of systems and software, 80(4), 571-583.
• Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies–Students’ behavior. Computers in Human Behavior, 72, 612-620.
• Chaka, J. G., & Govender, I. (2017). Students' perceptions and readiness towards mobile learning in colleges of education: a Nigerian perspective. South African Journal of Education, 37(1), 1-12.
• Chen, J., Park, Y., & Putzer, G. J. (2010). An examination of the components that increase acceptance of smartphones among healthcare professionals. Electronic journal of health informatics, 5(2), 16.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. Fishbein, M. (1979). A theory of reasoned action: some applications and implications.
• Han, S., Mustonen, P., Seppanen, M., & Kallio, M. (2006). Physicians' acceptance of mobile communication technology: an exploratory study. International Journal of Mobile Communications, 4(2), 210-230.
• Hardless, C., Lundin, J., & Nuldén, U. (2001). Mobile competence development for nomads. Paper presented at the System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on.
• Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners' intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46(2), 381-390.
• Hsia, J.-W. (2016). The effects of locus of control on university students’ mobile learning adoption. Journal of Computing in Higher Education, 28(1), 1-17.
• Hung, J.-L., & Zhang, K. (2012). Examining mobile learning trends 2003–2008: A categorical meta-trend analysis using text mining techniques. Journal of Computing in Higher education, 24(1), 1-17.
• Joo, Y. J., Kim, N., & Kim, N. H. (2016). Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development, 64(4), 611-630.
• Joo, Y. J., Lee, H. W., & Ham, Y. (2014). Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University. Journal of Computing in Higher Education, 26(2), 143-158.
• Jung, H.-J. (2015). Fostering an English Teaching Environment: Factors Influencing English as a Foreign Language Teachers' Adoption of Mobile Learning. Informatics in Education, 14(2), 219.
• Keller, J. M. (2008). First principles of motivation to learn and e3‐learning. Distance Education, 29(2), 175-185.
• Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26.
• Lee, Y.-K., Park, J.-H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(11), 1590-1599.
• Liaw, S.-S., Hatala, M., & Huang, H.-M. (2010). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computers & Education, 54(2), 446-454.
• Mac Callum, K., & Jeffrey, L. (2013). The influence of students' ICT skills and their adoption of mobile learning. Australasian Journal of Educational Technology, 29(3).
• Mohammadi, H. (2015). Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior, 49, 191-207.
• Navarro, C. X., Molina, A. I., & Redondo, M. A. (2016). Factors influencing students' acceptance in m-learning: A literature review and proposal of a taxonomy. Paper presented at the Computers in Education (SIIE), 2016 International Symposium on.
• Nikou, S. A., & Economides, A. A. (2017a). Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Computers in Human Behavior, 68, 83-95.
• Nikou, S. A., & Economides, A. A. (2017b). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73.
• Roschelle, J. (2003). Keynote paper: Unlocking the learning value of wireless mobile devices. Journal of computer assisted learning, 19(3), 260-272.
• Sarrab, M., Al-Shih, H., & Rehman, O. M. H. (2013). Exploring major challenges and benefits of m-learning adoption. British Journal of Applied Science & Technology, 3(4), 826.
• Shroff, R. H., & Keyes, C. J. (2017). A proposed framework to understand the intrinsic motivation factors on university students’ behavioral intention to use a mobile application for learning. Journal of Information Technology Education: Research, 16, 143-168.
• Tan, G. W.-H., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82-91.
• Vate-U-Lan, P. (2008). Mobile learning: Major challenges for engineering education. Paper presented at the Frontiers in Education Conference, 2008. FIE 2008. 38th Annual.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
• Wang, T.-S. (2013). Design and assessment of joyful mobile navigation systems based on TAM and integrating learning models applied on ecological teaching activity. Eurasia Journal of Mathematics, Science & Technology Education, 9(2), 201-212.
• Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118.
• Wu, L., Li, J.-Y., & Fu, C.-Y. (2011). The adoption of mobile healthcare by hospital's professionals: An integrative perspective. Decision Support Systems, 51(3), 587-596.
• Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26(4), 323-338.