Araştırma Makalesi
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The Effects of ARCS on the Acceptance of Online Learning Environments During the Novel Coronavirus Pandemic: A Structural Regression Analysis

Yıl 2024, Cilt: 13 Sayı: 3, 560 - 576

Öz

Although the use of online learning environments has been increasingly integrated into teaching and learning of university students worldwide, the global pandemic COVID-19 has urged almost the exclusive use of these environments for at least temporary periods. Since there may be situations that disrupt the efficiency of education-teaching environments such as a pandemic, it is expected that university teaching will continue either in a mixed-mode combining face-to-face and online learning or in some cases by online learning only. Hence, it is essential to assess the acceptance of online environments by university students. This study assesses the effect of motivation on the acceptance of such environments. It concentrates on examining the effect of motivation regarding teaching materials on the acceptance of online learning environments. For this, while the motivation concept related to the teaching materials is based on the Attention, Relevance, Confidence, Satisfaction model developed by Keller (1987), the acceptance of online learning environments is based on the Technology Acceptance Model developed by Davis (1989). The results obtained are discussed within the framework of the relevant literature.

Kaynakça

  • Ajzen, I. & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour (Englewood Cliffs, NJ, Prentice-Hall). http://www.citeulike.org/group/38/article/235626
  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50 (2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  • Arora, A. S., & Sharma, A. (2018). Integrating the ARCS Model with Instruction for Enhanced Learning. Journal of Engineering Education Transformations, 32(1), 85-89. https://doi.org/10.16920/jeet/2018/v32i1/130823
  • Bagci, K., & Celik, H. E. (2018). Examination of factors affecting continuance intention to use web-based distance learning system via structural equation modelling. Eurasian Journal of Educational Research, 18(78), 43-66. Retrieved from https://dergipark.org.tr/en/pub/ejer/issue/42563/512855
  • Balantekin, Y., & Bilgin, A. (2017). ARCS Motivasyon modeli’nin öğrencilerin motivasyonlarına, tutumlarına ve akademik başarılarına etkisi [The Effect of ARCS Motivational Model on Motivational Level, Attitudes and Academic Success of the Students]. Elementary Education Online, 16(1). https://doi.org/10.17051/io.2017.04081
  • Bentler, P. M., & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606. https://doi.org/10.1037/0033-2909.88.3.588
  • Bollen, K. (1989). Structural Equations with Latent Variables. Wiley-Interscience.
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). CA: Sag
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri [Scientific research methods] (23rd ed.). Pegem Akademi.
  • Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, application, and programming (2nd ed.). Lawrence Erlbaum.
  • Byrne, B. M.., & Campbell, T. L. (1999). Cross-cultural comparisons and the presumption of equivalent measurement and theoretical structure: A look beneath the surface. Journal of Cross-Cultural Psychology, 30, 555- 574. https://doi.org/10.1177/0022022199030005001
  • Byrne, B., M., (2010). Structural Equation Modeling with AMOS: basic concepts, applications, and programming (2nd ed). Taylor and Francis Group.
  • Carman, J. M. (2005). Blended learning design: Five key ingredients. Agilant Learning, 1-11. Retrieved from https://www.it.iitb.ac.in/~s1000brains/rswork/dokuwiki/media/5_ingredientsofblended_learning_design.pdf
  • Chaka, G. J. & Govendar, 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. https://doi.org/10.15700/saje.v37n1a1282
  • Cobb, C. (2013). The use of an animated pedagogical agent as a mnemonic device to promote learning and motivation in online education. [PhD thesis] Walden University.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Cüceloğlu, D. (1991). İnsan ve davranışı: Psikolojinin temel kavramları [Human and behavior: Basic concepts of psychology]. Remzi kitapevi.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340 https://doi.org/10.2307/249008
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of applied social psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
  • Dinçer, S., & Doğanay, A. (2016). Öğretim materyali’ne İlişkin motivasyon ölçeği (ÖMMÖ) Türkçe uyarlama çalışması [Turkish Adaptation Study of Instructional Materials Motivation Survey (IMMS)]. İlköğretim Online, 15(4), 1131-1148. : http://dx.doi.org/10.17051/io.2016.19056
  • Eagly, A. H., & Chaiken, S. (2007). The advantages of an inclusive definition of attitude. Social cognition, 25(5), 582-602. https://doi.org/10.1521/soco.2007.25.5.582
  • Erdem, A. R. ve Gözüküçük, M. (2013). İlköğretim 3. 4. ve 5. öğrencilerinin Türkçe dersine yönelik motivasyonu ve tutumları arasındaki ilişki [The relationship between motivations and attitudes of the 3rd 4th and 5th class primary students for Turkish lesson]. Pegem Eğitim ve Öğretim Dergisi, 3(2), 13-24. Retrieved from https://dergipark.org.tr/tr/pub/pegegog/issue/22583/241215
  • Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61. https://doi.org/10.1007/bf02299597
  • Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910. https://doi.org/10.1080/10494820.2017.1421560
  • Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175-191. https://doi.org/10.3758/bf03193146
  • Finn, A., Wang, L., & Frank, T. (2009). Attribute perceptions, customer satisfaction and intention to recommend e-services. Journal of Interactive Marketing, 23(3), 209-220. https://doi.org/10.1016/j.intmar.2009.04.006
  • Fishbein, M. ve Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading MA.
  • Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). McGraw-Hill.
  • Guritno, S., & Siringoringo, H. (2013). Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia-Social and Behavioral Sciences, 81, 212-216. https://doi.org/10.1016/j.sbspro.2013.06.415
  • Gülbahar, Y. (2005). Öğrenme stilleri ve teknoloji [Learning styles and technology]. Eğitim ve Bilim, 30(138). Retrieved from http://egitimvebilim.ted.org.tr/index.php/EB/article/view/4989/1096
  • Gülbahar, Y. (2012). E-öğrenme [E-learning]. Pegem Yayıncılık
  • Güldal, H. (2014). Bulut tabanlı bir ders yönetim sistemi yazılımının geliştirilmesine dayalı olarak öğretim elemanı ve öğrencilerin teknoloji kabullerinin incelenmesi [Investigating the faculty and the students' technology acceptance based on the development of cloud-based course management system software]. [PhD Thesis]. Trakya Üniversitesi
  • Hair, J.F., Sarstedt, M., Ringle, C.M., & Gudergan, S.P. (2018). Advanced Issues in partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks.
  • Hamutoğlu, N. B. (2018). İşbirlikli öğrenme etkinliklerinde bulut bilişim teknolojilerinin üniversite öğrencilerinin kabul, paylaşmaya uygunluk ve öğrenme performanslarına etkisi [The effect of cloud computing technologies in collaborative learning activities on university students’ acceptance, eligibility for responsibilitysharing, and learning performance]. [PhD Thesis]. Sakarya Üniversitesi.
  • Hamutoğlu, N., & Basarmak, U. (2020). External and ınternal barriers in technology ıntegration: a structural regression analysis. Journal of Information Technology Education: Research, 19(1), 17-40. Retrieved from https://www.learntechlib.org/p/216651/.
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of management information systems, 16(2), 91-112. https://doi.org/10.1080/07421222.1999.11518247
  • Jonassen, D. H. (1986). Hypertext principles for text and courseware design. Educational Psychologist, 21(4). 269-292. https://doi.org/10.1207/s15326985ep2104_3
  • 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. https://doi.org/10.1007/s11423-016-9436-7
  • Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272. Retrieved from https://www.learntechlib.org/p/201680/.
  • Joreskog, K. G., & Sorbom, D. (1993). Lisrel 8: Structural equation modeling with the SIMPLIS command language. Scientific International Software, Inc.
  • Kaçar, Z. (2011). Ortaöğretim öğrencilerinin çoklu zeka alanlarına göre bilgisayara yönelik tutumlarının karşılaştırılması [Comparison of secondary school students' attitudes towards computers according to multiple intelligence domains.]. [Master Thesis]. Sakarya Üniversitesi.
  • Karahan, B. Ü. ve Taştan, M. (2016). 5. ve 6. sınıf öğrencilerinin okumaya karşı tutum ve motivasyonlarının okuduğunu anlama becerileri ile ilişkisi [The correlation of attitudes and motivations of 5th and 6th grade students toward reading with the skills of reading comprehension]. Uluslararası Türkçe Edebiyat Kültür Eğitim Dergisi, 5(2), 949-969. Retrieved from https://dergipark.org.tr/tr/download/article-file/227897
  • Keller, J. M. (1983). Motivational design of instruction. In C. M. Reigeluth (Ed.) Instructional design theories and models: An overview of their current status (pp. 383-434). Lawrence Erlbaum.
  • Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2-10. https://doi.org/10.1007/BF02905780
  • Keller, J. M. (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175-185. https://doi.org/10.1080/01587910802154970
  • Keller, J. M. (2016). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1-15. https://doi.org/10.17275/per.16.06.3.2
  • Keller, J.M. (2010). Motivational design for learning and performance: The ARCS Model approach. Springer.
  • Khalifa, M. ve Ning Shen, K. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of enterprise information management, 21(2), 110-124. http://dx.doi.org/10.1108/17410390810851372
  • Kline, R. B. (2011). Principles and practice of structural equation modeling. The Guilford Press.
  • 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
  • Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
  • Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599-607. Retrieved https://www.learntechlib.org/p/66899/.
  • Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), 81. https://doi.org/10.12691/education-3-8-16.
  • Mayer, R. E. (2003). The promise of multimedia learning: using the same instructional design methods across different media. Learning and Instruction, 13(2), 125-139. https://doi.org/10.1016/S0959-4752(02)00016-6
  • Michael G. Moore (1989) Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7. http://dx.doi.org/10.1080/08923648909526659
  • Moore, M. G. (1993). Theory of transactional distance. In D. Keegan, (Ed.), Theoretical principles of distance education. Routledge.
  • Moreno, V., Cavazotte, F., & Alves, I. (2017). Explaining university students’ effective use of e-learning platforms. British Journal of Educational Technology, 48(4), 995–1009. https://doi.org/10.1111/bjet.12469
  • Nagy, J. T. (2018). Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.2886
  • Ocak, M.A., Topal, A.D., Ağca, R.K. ve Akçayır, M. (2011). Öğretim Tasarımı Kuramlar, Modeller ve Uygulamalar [Instructional Design Theories, Models and Practices]. Anı Yayıncılık
  • Orji, R., Reilly, D., Oyibo, K., & Orji, F. A. (2019). Deconstructing persuasiveness of strategies in behaviour change systems using the ARCS model of motivation. Behaviour & Information Technology, 38(4), 319-335. https://doi.org/10.1080/0144929X.2018.1520302
  • Qin, L., Kim, Y., Hsu, J., & Tan, X. (2011). The effects of social influence on user acceptance of online social networks. International Journal of Human-Computer Interaction, 27(9), 885–899. https://doi.org/10.1080/10447318.2011.555311
  • Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51. Retrieved from https://ramayah.com/journalarticlespdf/impactpeu.pdf
  • Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling. Lawrence Erlbaum Associates Publishers.
  • Reeves, T. C. (1998). The impact of media and technology in schools. Journal of the Journal of Art and Design Education, 2, 58-63.
  • Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
  • Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019
  • Schermelleh-Engel, K., & Moosbrugger, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
  • Šumak, B., Heričko, M., Pušnik, M., & Polančič, G. (2011). Factors affecting acceptance and use of Moodle: An empirical study based on TAM. Informatica, 35(1).
  • Tan, F. Z. (2014). Öğrenme, örgütlerde öğrenme, öğrenen organizasyonlar terimlerinin tanımı ve kavramsal ayırım. Business & Management Studies: An International Journal, 2(2), 188-217.
  • Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38, 197–201. https://doi.org/10.1111/j.2044- 8317.1985.tb00834.x
  • Ursavaş, Ö. F., Şahin, S., & McIlroy, D. (2014). Technology acceptance measure for teachers: T-TAM/Öğretmenler için teknoloji kabul ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885-917.
  • Ülgen, G. (1995). Eğitim Psikolojisi Birey ve Ögrenme. Bilim Yayınları.
  • Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & management, 40(6), 541-549.
  • Venkatesh, V., & Bala, H. (2008).Technology Acceptance Model 3 and research Agenda on Interventions. Decision Sciences, 39(2), 273-315.
  • Wachira, P., & Keengwe, J. (2011). Technology integration barriers: Urban school mathematics teachers perspectives. Journal of Science Education and Technology, 20(1), 17-25. https://doi.org/10.1007/s10956-010-9230- y
  • Wang, Y. H. (2017). Expectation, service quality, satisfaction, and behavioral intention evidence from Taiwan's medical tourism industry. Advances in Management and Applied Economics, 7(1), 1- 16.
  • Yıldız, V., Baydaş, Ö., Göktaş, Y. (2019). ARCS Motivasyon Modeli: 1997-2018 Yılları Arasında Yapılmış Uygulamalı Makalelerin İçerik Analizi. Trakya Eğitim Dergisi, 9(4), 723-741. https://doi.org/10.24315/tred.520477
Yıl 2024, Cilt: 13 Sayı: 3, 560 - 576

Öz

Çevrimiçi öğrenme ortamlarının kullanımı dünya çapında üniversite öğrencilerinin öğretim ve öğrenimine giderek daha fazla entegre edilmiş olsa da, küresel COVID-19 salgını en azından geçici sürelerle bu ortamların neredeyse özel olarak kullanılmasını zorunlu kılmıştır. Pandemi gibi eğitim-öğretim ortamlarının verimliliğini sekteye uğratan durumlar olabileceği için, üniversite öğretiminin ya yüz yüze ve çevrimiçi öğrenimi birleştiren karma öğrenme ya da bazı durumlarda yalnızca çevrimiçi öğrenimle devam etmesi beklenmektedir. Bu nedenle, üniversite öğrencileri tarafından çevrimiçi ortamların kabulünün değerlendirilmesi önemlidir. Bu çalışma, motivasyonun bu tür ortamların kabulü üzerindeki etkisini değerlendirmektedir. Öğretim materyallerine ilişkin motivasyonun çevrimiçi öğrenme ortamlarının kabulü üzerindeki etkisini incelemeye odaklanmaktadır. Bunun için öğretim materyallerine ilişkin motivasyon kavramı Keller (1987) tarafından geliştirilen Dikkat, İlgililik, Güven, Doyum modeline dayandırılırken, çevrimiçi öğrenme ortamlarının kabulü Davis (1989) tarafından geliştirilen Teknoloji Kabul Modeline dayanmaktadır. Elde edilen sonuçlar ilgili literatür çerçevesinde tartışılmıştır.

Kaynakça

  • Ajzen, I. & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour (Englewood Cliffs, NJ, Prentice-Hall). http://www.citeulike.org/group/38/article/235626
  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50 (2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  • Arora, A. S., & Sharma, A. (2018). Integrating the ARCS Model with Instruction for Enhanced Learning. Journal of Engineering Education Transformations, 32(1), 85-89. https://doi.org/10.16920/jeet/2018/v32i1/130823
  • Bagci, K., & Celik, H. E. (2018). Examination of factors affecting continuance intention to use web-based distance learning system via structural equation modelling. Eurasian Journal of Educational Research, 18(78), 43-66. Retrieved from https://dergipark.org.tr/en/pub/ejer/issue/42563/512855
  • Balantekin, Y., & Bilgin, A. (2017). ARCS Motivasyon modeli’nin öğrencilerin motivasyonlarına, tutumlarına ve akademik başarılarına etkisi [The Effect of ARCS Motivational Model on Motivational Level, Attitudes and Academic Success of the Students]. Elementary Education Online, 16(1). https://doi.org/10.17051/io.2017.04081
  • Bentler, P. M., & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606. https://doi.org/10.1037/0033-2909.88.3.588
  • Bollen, K. (1989). Structural Equations with Latent Variables. Wiley-Interscience.
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). CA: Sag
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri [Scientific research methods] (23rd ed.). Pegem Akademi.
  • Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, application, and programming (2nd ed.). Lawrence Erlbaum.
  • Byrne, B. M.., & Campbell, T. L. (1999). Cross-cultural comparisons and the presumption of equivalent measurement and theoretical structure: A look beneath the surface. Journal of Cross-Cultural Psychology, 30, 555- 574. https://doi.org/10.1177/0022022199030005001
  • Byrne, B., M., (2010). Structural Equation Modeling with AMOS: basic concepts, applications, and programming (2nd ed). Taylor and Francis Group.
  • Carman, J. M. (2005). Blended learning design: Five key ingredients. Agilant Learning, 1-11. Retrieved from https://www.it.iitb.ac.in/~s1000brains/rswork/dokuwiki/media/5_ingredientsofblended_learning_design.pdf
  • Chaka, G. J. & Govendar, 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. https://doi.org/10.15700/saje.v37n1a1282
  • Cobb, C. (2013). The use of an animated pedagogical agent as a mnemonic device to promote learning and motivation in online education. [PhD thesis] Walden University.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Cüceloğlu, D. (1991). İnsan ve davranışı: Psikolojinin temel kavramları [Human and behavior: Basic concepts of psychology]. Remzi kitapevi.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340 https://doi.org/10.2307/249008
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of applied social psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
  • Dinçer, S., & Doğanay, A. (2016). Öğretim materyali’ne İlişkin motivasyon ölçeği (ÖMMÖ) Türkçe uyarlama çalışması [Turkish Adaptation Study of Instructional Materials Motivation Survey (IMMS)]. İlköğretim Online, 15(4), 1131-1148. : http://dx.doi.org/10.17051/io.2016.19056
  • Eagly, A. H., & Chaiken, S. (2007). The advantages of an inclusive definition of attitude. Social cognition, 25(5), 582-602. https://doi.org/10.1521/soco.2007.25.5.582
  • Erdem, A. R. ve Gözüküçük, M. (2013). İlköğretim 3. 4. ve 5. öğrencilerinin Türkçe dersine yönelik motivasyonu ve tutumları arasındaki ilişki [The relationship between motivations and attitudes of the 3rd 4th and 5th class primary students for Turkish lesson]. Pegem Eğitim ve Öğretim Dergisi, 3(2), 13-24. Retrieved from https://dergipark.org.tr/tr/pub/pegegog/issue/22583/241215
  • Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61. https://doi.org/10.1007/bf02299597
  • Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910. https://doi.org/10.1080/10494820.2017.1421560
  • Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods, 39(2), 175-191. https://doi.org/10.3758/bf03193146
  • Finn, A., Wang, L., & Frank, T. (2009). Attribute perceptions, customer satisfaction and intention to recommend e-services. Journal of Interactive Marketing, 23(3), 209-220. https://doi.org/10.1016/j.intmar.2009.04.006
  • Fishbein, M. ve Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading MA.
  • Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). McGraw-Hill.
  • Guritno, S., & Siringoringo, H. (2013). Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia-Social and Behavioral Sciences, 81, 212-216. https://doi.org/10.1016/j.sbspro.2013.06.415
  • Gülbahar, Y. (2005). Öğrenme stilleri ve teknoloji [Learning styles and technology]. Eğitim ve Bilim, 30(138). Retrieved from http://egitimvebilim.ted.org.tr/index.php/EB/article/view/4989/1096
  • Gülbahar, Y. (2012). E-öğrenme [E-learning]. Pegem Yayıncılık
  • Güldal, H. (2014). Bulut tabanlı bir ders yönetim sistemi yazılımının geliştirilmesine dayalı olarak öğretim elemanı ve öğrencilerin teknoloji kabullerinin incelenmesi [Investigating the faculty and the students' technology acceptance based on the development of cloud-based course management system software]. [PhD Thesis]. Trakya Üniversitesi
  • Hair, J.F., Sarstedt, M., Ringle, C.M., & Gudergan, S.P. (2018). Advanced Issues in partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks.
  • Hamutoğlu, N. B. (2018). İşbirlikli öğrenme etkinliklerinde bulut bilişim teknolojilerinin üniversite öğrencilerinin kabul, paylaşmaya uygunluk ve öğrenme performanslarına etkisi [The effect of cloud computing technologies in collaborative learning activities on university students’ acceptance, eligibility for responsibilitysharing, and learning performance]. [PhD Thesis]. Sakarya Üniversitesi.
  • Hamutoğlu, N., & Basarmak, U. (2020). External and ınternal barriers in technology ıntegration: a structural regression analysis. Journal of Information Technology Education: Research, 19(1), 17-40. Retrieved from https://www.learntechlib.org/p/216651/.
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of management information systems, 16(2), 91-112. https://doi.org/10.1080/07421222.1999.11518247
  • Jonassen, D. H. (1986). Hypertext principles for text and courseware design. Educational Psychologist, 21(4). 269-292. https://doi.org/10.1207/s15326985ep2104_3
  • 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. https://doi.org/10.1007/s11423-016-9436-7
  • Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272. Retrieved from https://www.learntechlib.org/p/201680/.
  • Joreskog, K. G., & Sorbom, D. (1993). Lisrel 8: Structural equation modeling with the SIMPLIS command language. Scientific International Software, Inc.
  • Kaçar, Z. (2011). Ortaöğretim öğrencilerinin çoklu zeka alanlarına göre bilgisayara yönelik tutumlarının karşılaştırılması [Comparison of secondary school students' attitudes towards computers according to multiple intelligence domains.]. [Master Thesis]. Sakarya Üniversitesi.
  • Karahan, B. Ü. ve Taştan, M. (2016). 5. ve 6. sınıf öğrencilerinin okumaya karşı tutum ve motivasyonlarının okuduğunu anlama becerileri ile ilişkisi [The correlation of attitudes and motivations of 5th and 6th grade students toward reading with the skills of reading comprehension]. Uluslararası Türkçe Edebiyat Kültür Eğitim Dergisi, 5(2), 949-969. Retrieved from https://dergipark.org.tr/tr/download/article-file/227897
  • Keller, J. M. (1983). Motivational design of instruction. In C. M. Reigeluth (Ed.) Instructional design theories and models: An overview of their current status (pp. 383-434). Lawrence Erlbaum.
  • Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2-10. https://doi.org/10.1007/BF02905780
  • Keller, J. M. (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175-185. https://doi.org/10.1080/01587910802154970
  • Keller, J. M. (2016). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1-15. https://doi.org/10.17275/per.16.06.3.2
  • Keller, J.M. (2010). Motivational design for learning and performance: The ARCS Model approach. Springer.
  • Khalifa, M. ve Ning Shen, K. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of enterprise information management, 21(2), 110-124. http://dx.doi.org/10.1108/17410390810851372
  • Kline, R. B. (2011). Principles and practice of structural equation modeling. The Guilford Press.
  • 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
  • Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
  • Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599-607. Retrieved https://www.learntechlib.org/p/66899/.
  • Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), 81. https://doi.org/10.12691/education-3-8-16.
  • Mayer, R. E. (2003). The promise of multimedia learning: using the same instructional design methods across different media. Learning and Instruction, 13(2), 125-139. https://doi.org/10.1016/S0959-4752(02)00016-6
  • Michael G. Moore (1989) Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7. http://dx.doi.org/10.1080/08923648909526659
  • Moore, M. G. (1993). Theory of transactional distance. In D. Keegan, (Ed.), Theoretical principles of distance education. Routledge.
  • Moreno, V., Cavazotte, F., & Alves, I. (2017). Explaining university students’ effective use of e-learning platforms. British Journal of Educational Technology, 48(4), 995–1009. https://doi.org/10.1111/bjet.12469
  • Nagy, J. T. (2018). Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.2886
  • Ocak, M.A., Topal, A.D., Ağca, R.K. ve Akçayır, M. (2011). Öğretim Tasarımı Kuramlar, Modeller ve Uygulamalar [Instructional Design Theories, Models and Practices]. Anı Yayıncılık
  • Orji, R., Reilly, D., Oyibo, K., & Orji, F. A. (2019). Deconstructing persuasiveness of strategies in behaviour change systems using the ARCS model of motivation. Behaviour & Information Technology, 38(4), 319-335. https://doi.org/10.1080/0144929X.2018.1520302
  • Qin, L., Kim, Y., Hsu, J., & Tan, X. (2011). The effects of social influence on user acceptance of online social networks. International Journal of Human-Computer Interaction, 27(9), 885–899. https://doi.org/10.1080/10447318.2011.555311
  • Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51. Retrieved from https://ramayah.com/journalarticlespdf/impactpeu.pdf
  • Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling. Lawrence Erlbaum Associates Publishers.
  • Reeves, T. C. (1998). The impact of media and technology in schools. Journal of the Journal of Art and Design Education, 2, 58-63.
  • Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
  • Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019
  • Schermelleh-Engel, K., & Moosbrugger, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
  • Šumak, B., Heričko, M., Pušnik, M., & Polančič, G. (2011). Factors affecting acceptance and use of Moodle: An empirical study based on TAM. Informatica, 35(1).
  • Tan, F. Z. (2014). Öğrenme, örgütlerde öğrenme, öğrenen organizasyonlar terimlerinin tanımı ve kavramsal ayırım. Business & Management Studies: An International Journal, 2(2), 188-217.
  • Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38, 197–201. https://doi.org/10.1111/j.2044- 8317.1985.tb00834.x
  • Ursavaş, Ö. F., Şahin, S., & McIlroy, D. (2014). Technology acceptance measure for teachers: T-TAM/Öğretmenler için teknoloji kabul ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885-917.
  • Ülgen, G. (1995). Eğitim Psikolojisi Birey ve Ögrenme. Bilim Yayınları.
  • Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & management, 40(6), 541-549.
  • Venkatesh, V., & Bala, H. (2008).Technology Acceptance Model 3 and research Agenda on Interventions. Decision Sciences, 39(2), 273-315.
  • Wachira, P., & Keengwe, J. (2011). Technology integration barriers: Urban school mathematics teachers perspectives. Journal of Science Education and Technology, 20(1), 17-25. https://doi.org/10.1007/s10956-010-9230- y
  • Wang, Y. H. (2017). Expectation, service quality, satisfaction, and behavioral intention evidence from Taiwan's medical tourism industry. Advances in Management and Applied Economics, 7(1), 1- 16.
  • Yıldız, V., Baydaş, Ö., Göktaş, Y. (2019). ARCS Motivasyon Modeli: 1997-2018 Yılları Arasında Yapılmış Uygulamalı Makalelerin İçerik Analizi. Trakya Eğitim Dergisi, 9(4), 723-741. https://doi.org/10.24315/tred.520477
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri
Bölüm Makaleler
Yazarlar

Nazire Burçin Hamutoğlu 0000-0003-0941-9070

Hurşit Cem Salar 0000-0003-3986-0342

Emre Çam 0000-0001-9413-0292

Orhan Gemikonakli 0000-0002-0513-1128

Erken Görünüm Tarihi 18 Temmuz 2024
Yayımlanma Tarihi
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 3

Kaynak Göster

APA Hamutoğlu, N. B., Salar, H. C., Çam, E., Gemikonakli, O. (2024). The Effects of ARCS on the Acceptance of Online Learning Environments During the Novel Coronavirus Pandemic: A Structural Regression Analysis. Bartın University Journal of Faculty of Education, 13(3), 560-576.
All the articles published in the journal are open access and distributed under the conditions of CommonsAttribution-NonCommercial 4.0 International License
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