بررسی نگرش دانشجویان به آموزش الکترونیکی؛ مورد مطالعه دانشکدۀ زبان‌ها و ادبیات خارجی دانشگاه تهران

نویسندگان

استادیار دانشکده زبان‌ها و ادبیات خارجی، دانشگاه تهران

10.52547/irphe.28.2.129

چکیده

به دنبال شیوع ویروس کرونا در دنیا، آموزش الکترونیکی در دانشگاه‌های ایران رواج یافت. هدف این مطالعه بررسی نگرش دانشجویان رشتۀ زبان‌ها و ادبیات خارجی دانشگاه تهران در مقاطع کارشناسی و کارشناسی ارشد به این گونه آموزش بود. با این هدف و بر اساس الگوی پذیرش فناوری دیویس، متناسب با رشتۀ نامبرده، پرسشنامه‌ای محقق- ساخت با 74 سؤال، متضمن 3 شاخص اصلی «تلقی از سودمندی»، «تلقی از سهولت دسترسی»، «تمایل به استفاده در آینده» و 6  زیرشاخص در خصوص آموزش الکترونیکی زبان تهیه شد. 166 دانشجو به پرسشنامۀ الکترونیکی پاسخ دادند و میزان آلفای کرونباخ برای شاخص‌ها عبارت از سودمندی 971/0، سهولت استفاده 972/0 و تمایل به استفاده در آینده 94/0 بود. برای تحلیل یافته‌ها از آزمون‌های t یک‌گروهی و دوگروهی و آزمون تحلیل واریانس یک‌راهه و آزمون رتبه‌بندی فریدمن استفاده شد. یافته‌های پژوهش نشان داد که سطح سه شاخص اصلی پژوهش در میان دانشجویان هر دو مقطع در کلیۀ رشته‌ها پایین‌تر از حد متوسط است؛ یعنی از نظر دانشجویان آموزش الکترونیکی قابل دسترس و سودمند نیست و در کل، آنان نگرش مثبتی به این­ گونه آموزش ندارند. طبق نتایج، دانشجویان بر این باورند که آموزش الکترونیکی آنان را به استقلال در یادگیری سوق داده، اما علی‌رغم دسترسی آسان به امکانات و ابزارهای سامانۀ یادگیری الکترونیکی، تعامل سازنده میان استادان و دانشجویان شکل نگرفته است. تفاوت‌های بسیاری نیز در نگرش دانشجویان دو مقطع وجود داشت؛ از نظر دانشجویان کارشناسی ارشد استفاده از سامانۀ یادگیری و پرورش مهارت‌های زبانی در کلاس‌ها نسبت به دانشجویان کارشناسی آسان ­تر بوده است.
 

کلیدواژه‌ها

عنوان مقاله [English]

Evaluating Students’ attitude toward electronic education: University of Tehran’s Faculty of Foreign Languages and Literatures case

نویسندگان [English]

  • Soodeh Eghtesad
  • Marzieh Mehrabi

Assistant professor, Department of French Language and Literature, Faculty of Foreign Languages and Literatures, University of Tehran, Tehran, Iran

چکیده [English]

Following the outbreak of coronavirus in the world, Iranian universities embraced electronic teaching. The purpose of this study was to investigate the attitude of the students in the Faculty of Foreign Languages and Literature of Tehran University towards this type of education in bachelor and master levels. With this aim and based on Davis' technology acceptance model, a researcher-made questionnaire with 74 questions was prepared, including 3 main indicators"perception of usefulness", "perception of ease of use", "willingness to use in the future" and 6 sub-indices regarding electronic language learning. One hundred sixty six students responded to the electronic questionnaire and Cronbach's alpha for the indicators were the following: the perception of usefulness indicators 0.971, ease of use 0.972  and intention to use in the future 0.94. Findings indicated that the level of the three indicators among bachelor and master levels students in all major groups were lower than the average; that is, from the point of view of students electronic courses were neither very easily accessed nor useful, which suggests a rather negative attitude toward electronic education. Students believed that electronic courses made them more independent in learning, yet despite a relatively easy access to university’s e-learning platform, constructive interaction between professors and students has has not been formed. In addition, differences were perceived in bachelor and master students’ attitudes. From the point of view of master's surdents, it was easier to use the learning system and develop language skills in classes than undergraduate students. Master students found electronic courses relatively more user-friendly and believed that language skills were more easily developed in those courses.  
 

کلیدواژه‌ها [English]

  • Electronic Learning
  • technology acceptance model
  • foreign languages and literatures
  • Perceived Usefulness
  • perceived ease of use
  • Iran
1. Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256.
2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood: Cliffs, NJ: Prentice-Hall.
3. Ajzen, I. (1991). The theory of planned behavior; organization behavior and human decision processes. Academic Press, 50(2), 179-211. Doi: 10.1016/0749-5978(91)90020-T.
4. Alfadda, H.A., & Mahdi, H.S. (2021). Measuring students’ use of zoom application in language course based on the Technology Acceptance Model (TAM). Journal of Psycholinguist Res. Doi : 10.1007/ s10936-020-09752-1.
5. Al-Maatouk, Q., Othman, M.S., Aldraiweesh, A., Alturki, U., Al-Rahmi, W.M., & Aljeraiwi, A.A. (2020). Task-technology fit and technology acceptance model application to structure and evaluate the adoption of social media in academia. IEEE Access, 8, 78427-78440.
6. Al-Rahmi, W.M., Yahaya, N., Aldraiweesh, A.A., Alamri, M.M., Aljarboa, N.A., Alturki, U., & Aljeraiwi, A.A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE Access, 7, 26797-26809.
7. Andujar, A., Salaberri-Ramiro, M.S., & Cruz Martínez, M.S. (2020). Integrating flipped foreign language learning through mobile devices: Technology acceptance and flipped learning experience. Sustainability, 12(3).
8. Buabeng-Andoh, C. (2021). Exploring university students’ intention to use mobile learning: A research model approach. Educ Inf Technol, 26, 241-256. Doi :10.1007/s10639-020-10267-4
9. Chang, C.T., Hajiyev, J., & Su, C.R. (2017). Examining the students’ behavioral intention to use E-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010.
10. Chang, S.C., & Tung, F.C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
11. Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2021). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 1-19.
12. Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22-28. https://doi.org/10.1016/j.chb.2015.05.049.
13. Cruz-Cárdenas, J., Zabelina, E., Deyneka, O., Guadalupe-Lanas, J., & Velín-Fárez, M. (2019). Role of demographic factors, attitudes toward technology, and cultural values in the prediction of technology-based consumer behaviors: A study in developing and emerging countries. Technological Forecasting and Social Change, 149, 119768.
14. Davis, F.D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. [Thesis]. England: MIT Sloan School of Management, Cambridge University.
15. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
16. Drot-Delange, D., & Gomis, E. (2012). Dispositif hybride et enseignement des langues à l’université : Quelle acceptation par les étudiants spécialistes d’autres disciplines ? Journées Communication et Apprentissage Instrumentés en Réseau, Sep 2012, Amiens, France.
17. Friesen, N. (2009). Re-thinking e-learning research: Foundations, methods and practices. New York: Peter Lang.
18. Gamble, C. (2018). Exploring EFL university students’ acceptance of e-learning using TAM. Kwansei Gakuin University Humanities Review, 22, 23-37.
19. Goh, E., & Wen, J. (2020). Applying the technology acceptance model to understanding hospitality management and students’ intentions to use electronic discussion boards as a learning tool. Journal of Teaching in Travel and Tourism, 24-42. Doi: 10.1080/15313220.2020.1768621.
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). Doi:/10.19173/ irrodl.v20i4.4061.
21. Harasim, L. (2012). Learning Theory and Online Technologies. 1-192. Doi: 10.4324/9780203846933.
22. Jereb, E., & Šmitek, B. (2006). Applying multimedia instruction in E-learning. Innovations in Education & Teaching International, 43(1), 15-27. Doi:10.1080/14703290500467335.
23. Khodadad Hoseiny, S.H., Noori, A., & Zabihi, M.R. (2013). E-learning acceptance in higher education: Application of flow theory, technology acceptance model & e-service quality. Quarterly Journal of Research & Planning in Higher Education, 19 (1), 111-136 [in Persian].
24. Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-learning and Digital Media, 15(4), 191-216. Doi:10.1177/2042753018785180.
25. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
26. Liu, H., Wang, L., & Koehler, M.J. (2019). Exploring the intention‐behavior gap in the technology acceptance model: A mixed‐methods study in the context of foreign‐language teaching in China. British Journal of Educational Technology, 50(5), 2536-2556.
27. Martinho, D.S., Santos, E.M., Miguel, M.I., & Cordeiro, D.S. (2018). Factors that influence the adoption of postgraduate online courses. International Journal of Emerging Technologies in Learning (iJET), 13(12), 123–141. https://Doi.org/10.3991/ijet.v13i12.8864.
28. Mayer, R.E., & Moreno, R. (2005). A cognitive theory of multimedia learning: Implications for design principles. Journal of Educational Psychology, 91(2), 358-368.
29. Mehrabi, M., & Homapour, S. (2018). The effect of the substrate type in virtual concurrent classes on the oral comprehension of the Iranian language learners: The case of Adobe Connect Platform and Skype Software. IQBQ, 9 (2), 251-276 [in Persian].
30. Peng, H., Su, Y.J., Chou, C., & Tsai, C.C. (2009). Ubiquitous knowledge construction: Mobile learning re-defined and a conceptual framework. Innovations in Education and Teaching International, 46, 171-183.
31. Rafiee, M., & Abbasian-Naghneh, S. (2019). E-learning: development of a model to assess the acceptance and readiness of technology among language learners. Computer Assisted Language Learning, 1-21. Doi: 10.1080/09588221.2019.1640255
32. Rendi, V., Khon Siavash, M., & Masoumi, B. (2015). Influencing factors on internet customers purchasing behavior in Iran based on Technology Acceptance Model (TAM). Journal of Development & Evolution Management, 1393(special issue), 109-118 [in Persian].
33. Ritter, N.L. (2017). Technology acceptance model of online learning management systems in higher education: A meta-analytic structural equation model. International Journal of Learning Management Systems, 5(1), 1-15. https://doi.org/10.18576/ijlms/050101.
34. Saeed, K.A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45, 376- 386.
35. Salloum, S.A., Mohammad Alhamad, A.G., Al-Emran, M., Monem, A.A., & Shaalan, Kh. (2019). Exploring students’ acceptance of E-learning through the development of a comprehnsive technology acceptance model. IEEE Acces, Vol 7, 128445-128462. Doi: 10.1109/ACCESS.2019.2939467.
36. Sangrà, A., Vlachopoulos, D., & Cabrera, N. (2012). Building an inclusive definition of E-learning: An approach to the conceptual framework. International Review of Research in Open and Distributed Learning, 13(2), 145–159. Doi:10.19173/irrodl.v13i2.1161.
37. Scherer, R., Fazilat, S., & Tondeur, J. (2020). All the same or different? Revising measures of teachers’ technology acceptance. Computers and Education, 143, 1-17. Doi:10.1016/j.compedu.2019.103656.
38. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009.
39. Son, J.B. (2018). Teacher development in technology-enhanced language teaching. Cham: Springer International Publishing.
40. Springer, C. (2015). Apprentissage des langues en ligne et humanités numériques: Une mise en équation. Colloque sur Humanités Numériques: Identités, pratiques et théories. Montréal.
41. Tapscott, D., & Williams, A.D. (2006). Wikinomics. How Mass Collaboration Changes Everything. New York: Penguin.
42. Tarhini, A., Hassouna, M., Abbasi, M.S., & Orozco, J. (2015). Towards the acceptance of RSS to support learning: An empirical study to validate the technology acceptance model in Lebanon. Electronic Journal of e-Learning, 13(1), 30-41.
43. Toland, S., White, J., Mills, D., & Bolliger, D.U. (2014). EFL instructors’ perceptions of use‌fulness and ease of use of the LMS Manaba.The JALT CALL Journal, 10(3), 221‌236.
44. Wong, G.K. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong. Australasian Journal of Educational Technology, 31(6). https://doi.org/10.14742/ajet.1890.
45. Zogheib, B., Rabaa’i, A., Zogheib, S., & Elsaheli, A. (2015). University student perceptions of technology use in mathematics learning. Journal of Information Technology Education, 14, 417-438.