Shahid Chmran universty of ahvaz
Abstract: (3423 Views)
Objective: Learning through mobile phone is a type of distance learning that takes place in many situations with social interaction and content through personal electronic devices. The purpose of the present study was to investigate the factors affecting the behavioral intention of learning graduate students of Shahid Chamran University of Ahvaz through mobile phones.
Method: The current research is applied in terms of its purpose and survey in terms of its execution
Findings: The results showed that all the structures of the theory of planned behavior and the technology acceptance model have an effect on the behavioral intention of the graduate students of Shahid Chamran University of Ahvaz to learn through mobile phones.
Conclusion: by considering the characteristics and needs of users and their applications in the virtual education system, buy or rent powerful servers for Providing virtual education services and taking into account the use of new educational technologies in evaluating the performance of faculty professors will improve the level of using educational systems while learning through mobile phones.
Method: The current research is applied in terms of its purpose and survey in terms of its execution
Findings: The results showed that all the structures of the theory of planned behavior and the technology acceptance model have an effect on the behavioral intention of the graduate students of Shahid Chamran University of Ahvaz to learn through mobile phones.
Conclusion: by considering the characteristics and needs of users and their applications in the virtual education system, buy or rent powerful servers for Providing virtual education services and taking into account the use of new educational technologies in evaluating the performance of faculty professors will improve the level of using educational systems while learning through mobile phones.
References
1. ترابی، مجید؛ ابراهیمی مهربانی، شادی. (1394). بررسی تمایل به استفاده از یادگیری از طریق تلفن همراه با توجه به نقش نوآوری‌های فنی در میان دانشجویان تحصیلات تکمیلی دانشگاه آزاد اسلامی واحد دهاقان. سومین کنفرانس بین-المللی حسابداری و مدیریت، تهران، 1-15.
2. رحیم‌نیا، فریبرز؛ سروری، تهمینه؛ پورسلیمی، مجتبی. (1397). بررسی تأثیر پشیمانی از برند بر قصد رفتاری به واسطه رضایت و نقش تعدیل‌گری هویت برند استفاده‌کنندگان در باشگاه‌های ورزشی درجه یک شهر مشهد. مدیریت برند، 5(15)، 1-28.
3. زمانی، بی‌بی عشرت؛ ببری، حسن؛ قربانی، سمیه. (1392). شناسایی راه‌کارهای توسعه یادگیری سیار در فعالیت‌های یاددهی- یادگیری آموزش پزشکی از دیدگاه دانشجویان علوم پزشکی اصفهان و متخصصان فناوری اطلاعات. مجله ایرانی آموزش در علوم پزشکی، ۱۳ (۲)، 87-۹۷.
4. شبیری، سید محمد؛ شمسی پاپکیاده، سیده زهرا. (1395). ارزیابی عوامل مؤثر بر پیاده‌سازی یادگیری سیار در برنامه آموزش محیط زیست با استفاده از مدل رفتار برنامه‌ریزی‌شده. نشریه علمی- پژوهشی فناوری آموزش، 11(1)، 51-61.
5. غفاری آشتیانی، پیمان؛ صادق حری، محمد؛ غلامی، بهمن. (1390). بررسی نقش اعتماد الکترونیک و هنجارهای ذهنی در پذیرش وب سایت تجارت الکترونیک توسط مشتریان (مطالعه موردی: شرکت قطارهای مسافربری رجاء). مدیریت بازاریابی، 6(12)، 63-80.
6. قربانعلی زاده، رسول؛ سیاهکالی مرادی، جواد. (1399). بررسی تأثیر هنجار ذهنی، نگرش نسبت به رفتار و کنترل رفتاری درک شده بر نیت مدیران ارشد دولتی در حمایت از پروژه فناوری اطلاعات (مطالعه موردی: سازمان تأمین اجتماعی قم). رویکردهای پژوهشی نوین در مدیریت و حسابداری، 38(8)، 1-18.
7. مانیان، امیر؛ سهرابی، بابک؛ مرتضوی، احسان. (1393). بررسی عوامل موثر بر پذیرش یادگیری سیار مورد مطالعه: دانشجویان رشته‌های مدیریت، دانشگاه تهران و فردوسی مشهد. پژوهشنامه مدیریت اجرایی، 6 (12): 131-154.
8. ممتازیان، علیرضا؛ رجب دری، حسین. (1396). رابطه پذیرش و استفاده از فناوری با یادگیری سیار در دانشجویان حسابداری. فناوری آموزش و یادگیری، 3(10)، 125-148.
9. Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in human behavior, 63, 75-90.
10. Aburub, F., & Alnawas, I. (2019). A new integrated model to explore factors that influence adoption of mobile learning in higher education: An empirical investigation. Education and Information Technologies, 24(3), 2145-2158.
11. Adel Ali, R., & Rafie Mohd Arshad, M. (2018). Empirical analysis on factors impacting on intention to use m-learning in basic education in Egypt. International Review of Research in Open and Distributed Learning, 19(2).
12. Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125-138.
13. Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human behavior, 56, 93-102.
14. Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686.
15. Arokiasamy, A. R. A. (2017). A qualitative study on the impact of mobile technology among students in private higher education institutions (PHEIs) in Peninsular Malaysia. Journal of Entrepreneurship and Business, 5(2).
16. 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.
17. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & education, 59(3), 1054-1064.
18. Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers & Education, 123, 53-64.
19. Dassa, L., & Vaughan, M. (2018). # Class again? How education faculty engage the disengaged college student. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 91(1), 42-45.
20. Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning, 20(3).
21. Güler, Ç. (2017). Use of WhatsApp in higher education: What's up with assessing peers anonymously?. Journal of Educational Computing Research, 55(2), 272-289.
22. Hameed, F., & Qayyum, A. (2018). Determinants of behavioral intention towards mobile learning in Pakistan: Mediating role of attitude. Business and Economic Review, 10(1), 33-61.
23. Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International journal of medical informatics, 101, 75-84.
24. Kim, J., Eys, M., Robertson-Wilson, J., Dunn, E., & Rellinger, K. (2019). Subjective norms matter for physical activity intentions more than previously thought: Reconsidering measurement and analytical approaches. Psychology of Sport and Exercise, 43, 359-367.
25. Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of bank marketing.
26. Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058-208074.
27. Naveed, Q. N., Alam, M. M., & Tairan, N. (2020). Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability, 12(20), 8618.
28. Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73.
29. O’Dea, S. (2020). Number of smartphone users worldwide from 2016 to 2021. Statista Research Department.
30. Peciuliauskiene, P., Tamoliune, G., & Trepule, E. (2022). Exploring the roles of information search and information evaluation literacy and pre-service teachers’ ICT self-efficacy in teaching. International Journal of Educational Technology in Higher Education, 19(1), 1-19.
31. Peteranetz, M. S., Flanigan, A. E., Shell, D. F., & Soh, L. K. (2018). Career aspirations, perceived instrumentality, and achievement in undergraduate computer science courses. Contemporary Educational Psychology, 53, 27-44.
32. Quan, L., Al-Ansi, A., & Han, H. (2022). Assessing customer financial risk perception and attitude in the hotel industry: Exploring the role of protective measures against COVID-19. International Journal of Hospitality Management, 101, 103123.
33. Shamsuddin, A., Wahab, E., Abdullah, N. H., & Suratkon, A. (2018, November). Mobile learning adoption in enhancing learning experience among HEI students. In 2018 IEEE 10th International Conference on Engineering Education (ICEED) (pp. 202-207). IEEE.
34. Siripipatthanakul, S., Siripipattanakul, S., Limna, P., & Pholphong, L. (2022). Predicting Intention to Choose the Online Degree During the COVID-19 Pandemic: The Mediating Role of Perceived Effectiveness. Asia-Pacific Review of Research in Education, 1(1), 1-19.
35. Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use e-filing: The role of technology readiness. The Journal of Asian Finance, Economics and Business, 7(9), 537-547.
36. Uther, M. (2019). Mobile learning—trends and practices. Education Sciences, 9(1), 33.
37. 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.
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
