English Teachers' Intentions to Continue Using Open Educational Resources Language Processing Technologies

Öz

Open educational resources refer to the digital learning tools enabling lifelong learning formally and informally with free-of-charge access. Their openness principle makes learning effective and efficient. Therefore, this study aimed to examine teachers' familiarity status with open educational resources language processing technologies and to reveal the intention of English teachers to continue using open educational resources in language teaching. As one of the quantitative methods, this cross-sectional survey study consists of two steps. In the first step, we asked English teachers about their awareness of open educational resources language processing technologies with a questionnaire. Secondly, we measured their intention to continue using open educational resources language processing technologies with an integrated model including the Technology Acceptance Model, Planned Behavior Theory, Expectation Confirmation Model, and Flow Theory. The participants were English teachers working at all school levels in the 2022-2023 academic years in Afyonkarahisar, Turkey. We tested this comprehensive model with a partial least squares structural equation model. The results of the first step showed that 54% of the English teachers knew or used any of the open educational resources language processing technologies. The structural equation model revealed a positive effect of perceived usefulness, subjective values, perceived behavioral control, and concentration on the intention of English teachers to continue using open educational resources language processing technologies. However, attitude, satisfaction, and perceived pleasure did not significantly affect their intention. Consequently, the future of open education resources lies in a clear understanding of teachers' perceptions of open education resources, and this study is of great importance in understanding it.

Anahtar Kelimeler

• Open Educational Resources
• Language Education
• Technology Acceptance Model
• Planned Behavior Theory
• Expectation-Confirmation Model.

İngilizce Öğretmenlerinin Açık Eğitim Kaynaklarından Dil İşleme Teknolojilerini Kullanmaya Devam Etme Niyetlerinin İncelenmesi

Öz

Açık eğitim kaynakları, ücretsiz erişim ile resmi ve gayri resmi olarak yaşam boyu öğrenmeyi sağlayan dijital öğrenme araçları olarak tanımlanabilir. Açıklık ilkeleri, öğrenmeyi etkili ve verimli hâle getirmektedir. Bu nedenle bu çalışma, öğretmenlerin açık eğitim kaynaklarından dil işleme teknolojilerine aşinalık durumunu incelemeyi ve İngilizce öğretmenlerinin dil öğretiminde açık eğitim kaynaklarını kullanmaya devam etme niyetini ortaya çıkarmayı amaçlamıştır. Nicel yöntemlerden biri olan bu kesitsel tarama araştırması iki adımdan oluşur. İlk adımda bir anket ile İngilizce öğretmenlerine açık eğitim kaynakları dil işleme teknolojileri konusundaki farkındalıkları sorulmuştur. İkinci olarak, Teknoloji Kabul Modeli, Planlanmış Davranış Teorisi, Beklenti Onay Modeli ve Akış Teorisi gibi entegre bir modelle açık eğitim kaynakları dil işleme teknolojilerini kullanmaya devam etme niyetleri ölçülmüştür. Katılımcılar 2022-2023 eğitim-öğretim yılında Afyonkarahisar ilinde tüm okul kademelerinde görev yapan İngilizce öğretmenlerinden seçilmiştir. Bu kapsamlı model, kısmi en küçük kareler yapısal eşitlik modeli kullanılarak test edilmiştir. İlk adımın sonuçları, İngilizce öğretmenlerinin %54'ünün açık eğitim kaynaklarının dil işleme teknolojilerini bildiğini veya kullandığını göstermiştir. Yapısal eşitlik modeli, Algılanan Kullanışlılığın, Öznel Değerlerin, Algılanan Davranışsal Kontrolün ve Konsantrasyonun İngilizce öğretmenlerinin açık eğitim kaynakları dil işleme teknolojilerini kullanmaya devam etme niyeti üzerinde olumlu bir etkisi olduğunu ortaya koymuştur. Ancak, Tutum, Memnuniyet ve Algılanan zevk, niyetlerini önemli ölçüde etkilememiştir. Sonuç olarak, açık eğitim kaynaklarının geleceği, öğretmenlerin açık eğitim kaynaklarına ilişkin algılarının net bir şekilde anlaşılmasında yatmaktadır ve bu çalışma da algıları anlamada büyük önem taşımaktadır.

Anahtar Kelimeler

• Açık Eğitim Kaynakları
• Dil Öğretimi
• Teknoloji Kabul Modeli
• Planlanmış Davranış Kuramı
• Beklenti Kabul Modeli

Kaynakça

1. Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous ıntention to use m-learning: An integrated model. Education and Information Technologies, 25, 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
3. Atkins, D. E., Brown, J. S., & Hammond, A. L. (2007). A review of the open educational resources (OER) movement: Achievements, challenges, and new opportunities (Vol. 164). Mountain View: Creative common.
4. Bajaj, P., Khan, A., Tabash, M. I., & Anagreh, S. (2021). Teachers’ intention to continue the use of online teaching tools post COVID-19. Cogent Education, 8(1), 2002130. https://doi.org/10.1080/2331186X.2021.2002130
5. Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373. https://doi.org/10.1521/jscp.1986.4.3.359
6. Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214. https://doi.org/10.1016/S0167-9236(01)00111-7
7. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370. https://doi.org/10.2307/3250921
8. Blyth, C. S., & Thoms, J. J. (Eds.). (2021). Open education and second language learning and teaching: The rise of a new knowledge ecology (Vol. 87). Multilingual Matters. https://doi.org/10.2307/jj.1231862

9. Chen, S. C., Liu, M. L., & Lin, C. P. (2013). Integrating technology readiness into the expectation–confirmation model: An empirical study of mobile services. Cyberpsychology, Behavior, and Social Networking, 16(8), 604-612. https://doi.org/10.1089/cyber.2012.0606
10. 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. https://doi.org/10.1016/j.compedu.2012.04.015
11. Cheng, E. W. (2019). Choosing between the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Educational Technology Research and Development, 67, 21-37. https://doi.org/10.1007/s11423-018-9598-6
12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
13. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
14. Davis F D, 1989, Perceived usefulness, perceived ease of use, and user acceptance of ınformation technology. MIS Quarterly, 13, 319–340. https://doi.org/10.2307/249008
15. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Massachusetts Institute of Technology.
16. Dunn, R., Hattie, J., & Bowles, T. (2018). Using the Theory of Planned Behavior to explore teachers’ intentions to engage in ongoing teacher professional learning. Studies in Educational Evaluation, 59, 288-294. https://doi.org/10.1016/j.stueduc.2018.10.001
17. Eksail, F. A. A., & Afari, E. (2020). Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Education and Information Technologies, 25(4), 2681-2697. https://doi.org/10.1007/s10639-019-10086-2
18. Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students' perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215-235. https://doi.org/10.1111/j.1540-4609.2006.00114.x
19. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
20. Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education. McGraw-Hill.
21. Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2014). Technologies for foreign language learning: A review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1), 70-105. https://doi.org/10.1080/09588221.2012.700315
22. Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. https://doi.org/10.1504/IJMDA.2017.087624
23. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139–152. https://doi.org/10.2753/MTP1069-6679190202
24. Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
25. Kelly, H. (2014). A path analysis of educator perceptions of open educational resources using the technology acceptance model. International Review of Research in Open and Distributed Learning, 15(2), 26-42. https://doi.org/10.19173/irrodl.v15i2.1715
26. Kesmodel, U. S. (2018). Cross‐sectional studies–what are they good for?. Acta Obstetricia et Gynecologica Scandinavica, 97(4), 388-393. https://doi.org/10.1111/aogs.13331
27. Kessler, G. (2013). Teaching ESL/EFL in a world of social media, mash‐ups, and hyper‐collaboration. TESOL Journal, 4(4), 615-632. https://doi.org/10.1002/tesj.106
28. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223. https://doi.org/10.1287/isre.13.2.205.83
29. Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
30. Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095-1104. https://doi.org/10.1016/j.im.2003.10.007
31. Li, Y., Wang, Q., & Lei, J. (2019). Modeling Chinese Teachers' attitudes toward using technology for teaching with a SEM approach. Computers in the Schools, 36(2), 122-141. https://doi.org/10.1080/07380569.2019.1600979
32. MacKinnon, T., & Pasfield-Neofitou, S. E. (2016). OER “produsage” as a model to support language teaching and learning. Education Policy Analysis Archives, 24(40), 1-18. https://doi.org/10.14507/epaa.24.1825
33. Mishra, S. (2017). Open educational resources: Removing barriers from within. Distance Education, 38(3), 369-380. https://doi.org/10.1080/01587919.2017.1369350
34. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4), 217-230. https://doi.org/10.1016/S0378-7206(00)00061-6
35. Mtebe, J., & Gallagher, M. (2022). Continued usage intentions of digital technologies post-pandemic through the expectation-confirmation model: The case of a Tanzanian University. International Journal of Education and Development using Information and Communication Technology, 18(1), 125-145.
36. Nunnally, J. C. (1978). Psychometric theory: 2d Ed. McGraw-Hill.
37. Pérez-Paredes, P., Ordoñana Guillamón, C., & Aguado Jiménez, P. (2018). Language teachers’ perceptions on the use of OER language processing technologies in MALL. Computer Assisted Language Learning, 31(5-6), 522-545. https://doi.org/10.1080/09588221.2017.1418754
38. Ramoutar, S. (2021). Open education resources: Supporting diversity and sharing in education. TechTrends, 65(4), 410-412. https://doi.org/10.1007/s11528-021-00615-7
39. Şahin, F. (2021). Öğretmen adaylarının bilişim teknolojileri kullanım niyetlerinde duyguların ve temel psikolojik ihtiyaçların rolü: Teknolojinin kabulüne motivasyonel bir yaklaşım [Doctoral dissertation]. Anadolu University, Türkiye.
40. Sun, P. P., & Mei, B. (2022). Modeling preservice Chinese-as-a-second/foreign-language teachers’ adoption of educational technology: a technology acceptance perspective. Computer Assisted Language Learning, 35(4), 816-839. https://doi.org/10.1080/09588221.2020.1750430
41. Sun, Y., Zhou, T., & Li, J. (2010, October). Are students willing to use your online open resources?. In 2010 Third International Symposium on Information Processing (pp. 208-212), Qingdao. IEEE. https://doi.org/10.1109/ISIP.2010.17
42. Tang, H., Lin, Y. J., & Qian, Y. (2021). Improving k-12 teachers’ acceptance of open educational resources by open educational practices: A mixed methods inquiry. Educational Technology Research and Development, 69, 3209-3232. https://doi.org/10.1007/s11423-021-10046-z
43. Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), 3-18. https://doi.org/10.1080/10494821003714632
44. Thoms, J. J., & Thoms, B. L. (2014). Open educational resources in the United States: Insights from university foreign language directors. System, 45, 138-146. https://doi.org/10.1016/j.system.2014.05.006
45. Thoms, J. J., Arshavskaya, E., & Poole, F. J. (2018). Open Educational Resources and ESL Education: Insights from US Educators. TESL-EJ, 22(2), n2.
46. UNESCO. (2023). Open educational resources. Retrieved from https://www.unesco.org/en/open-educational-resources on 27.10.2023. https://doi.org/10.54676/LBIV3337
47. Ünal, E., & Güngör, F. (2021). The continuance intention of users toward mobile assisted language learning: The case of DuoLingo. Asian Journal of Distance Education, 16(2), 197-218. https://doi.org/10.5281/zenodo.5811777
48. Erhan, Ü. (2020). Exploring the effect of collaborative learning on teacher candidates’ ıntentions to use Web 2.0 technologies. International Journal of Contemporary Educational Research, 7(2), 1-14. https://doi.org/10.33200/ijcer.736876
49. Volungevičienė, A., Lydeka, Z., & Mejerytė–Narkevičienė, K. (2012). Measuring conscious use of open content in competence–based education. Proceedings of the ICICTE, Greece, 190-203.
50. Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26, 323-338. https://doi.org/10.1007/s12525-015-0214-x
51. Zangirolami-Raimundo, J., de Oliveira Echeimberg, J., & Leone, C. (2018). Research methodology topics: Cross-sectional studies. Journal of Human Growth and Development, 28(3), 356-360. https://doi.org/10.7322/jhgd.152198