Araştırma Makalesi
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Yıl 2017, Cilt: 5 Sayı: 2, 1 - 18, 28.12.2017

Öz

Kaynakça

  • Anderson, J. C., & Gerbing, D. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173. Baydaş, Ö., & Göktaş, Y. (2016). Influential factors on preservice teachers' intentions to use ICT in future lessons. Computers in Human Behavior, 56, 170-178. 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. Chen, R. J. (2010). Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education, 55(1), 32-42. Ciampa, K. (2014). Learning in a mobile age: an investigation of student motivation. Journal of Computer Assisted Learning, 30(1), 82-96. Cole, D. A. (1987). Utility of confirmatory factor analysis in test validation research. Journal of Consulting and Clinical Psychology, 55(4), 1019-1031. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189−211. Comrey, A. L. & Lee, H. B. (1992). A first course in factor analysis. Hillsdale, NJ: Erlbaum. Çakıroğlu, Ü., Gökoğlu, S., & Öztürk, M. (2017). Pre-service computer teachers’ tendencies towards the use of mobile technologies: A technology acceptance model perspective. European Journal of Open, Distance and E-learning, 20(1). Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318−339. 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. Demir, K., & Akpınar, E. (2016). Mobil öğrenmeye yönelik tutum ölçeği geliştirme çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 6(1), 59-79. Doğan, M., Şen, R. & Yılmaz, V. (2015). İnternet bankacılığına ilişkin davranışların planlanmış davranış teorisi ve teknoloji kabul modeli kullanılarak önerilen bir yapısal eşitlik modeliyle incelenmesi. Uşak Üniversitesi Sosyal Bilimler Dergisi, 2015(22). Dold, C. J. (2016). Rethinking mobile learning in light of current theories and studies. The Journal of Academic Librarianship, 42(6), 679-686. DuFour, A., Lajeunesse, K., Pipada, R., Xu, S., & Nomee, J. (2017). The effect of data security perception on wearable device acceptance: a technology acceptance model. Proceedings of Student-Faculty Research Day, D11, 1-6. Eltayeb, M., & Dawson, M. (2016). Understanding user’s acceptance of personal cloud computing: Using the Technology Acceptance Model. Information Technology: New Generations, 448, 3-12. 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. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Gao, S., Krogstie, J., & Siau, K. (2011). Developing an instrument to measure the adoption of mobile services. Mobile Information Systems, 7(1), 45-67. Hamidi, H., & Chavoshi, A. (2017). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics. Hossain, L., & de Silva, A. (2009). Exploring user acceptance of technology using social networks. The Journal of High Technology Management Research, 20(1), 1-18. Howell, D. W. (2016). Social media site use and the technology acceptance model: Social media sites and organization success (Unpublished doctoral dissertation). Capella University. ITU. (2017). ICT facts & figures: The world in 2017. Jaradat, R. M. (2014). Students' attitudes and perceptions towards using m-learning for French language learning: A case study on Princess Nora University. International Journal of Man - Machine Studies, 2(1), 33-44. Jöreskog, K., & Sörbom, D. (2001). LISREL 8.51. Mooresvile: Scientific Software. Karimi, S. (2016). Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769-776. Kline, R. B. (2005). Principle and practice of structural equation modeling. New York, NY: Guilford Press. Koç, T., Turan, A. H., & Okursoy, A. (2016). Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling. The International Journal of Management Education, 14(3), 286-300. Kose, U., Koc, D., & Yucesoy, S. A. (2013). Design and development of a sample" computer programming" course tool via story-based e-learning approach. Educational Sciences: Theory and Practice, 13(2), 1235-1250. Kreijns, K., Van Acker, F., Vermeulen, M., & Van Buuren, H. (2013). What stimulates teachers to integrate ICT in their pedagogical practices? The use of digital learning materials in education. Computers in Human Behavior, 29(1), 217-225. Lawless, K. A., & Pellegrino, J. W. (2007). Professional development in integrating technology into teaching and learning: Knowns, unknowns, and ways to pursue better questions and answers. Review of Educational Research, 77(4), 575-614. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21(5-6), 385-392. Lei, J. (2009). Digital natives as preservice teachers: What technology preparation is needed?. Journal of Computing in Teacher Education, 25(3), 87-97. Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21(6), 387-395. Mac Callum, K. S. (2011). Influences on the adoption of mobile technology by students and teachers (Unpublished doctoral dissertation) Massey University, Albany, New Zealand. Mac Callum, K., Jeffrey, L., & Kinshuk. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education: Research, 13, Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factory analysis: The effects of sample size. Psychological Bulletin, 103(3), 391-410. doi: 10.1007/BF01102761 Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76-85. Meng, H., & Wang, T. (2012). Acceptance of IWBs instruction and contamitant behavior through self-regulation learning. GSTF Journal on Computing, 1(4). Menzi, N., Önal, N., & çalışkan, E. (2012). Mobil teknolojilerin eğitim amaçlı kullanımına yönelik akademisyen görüşlerinin Teknoloji Kabul Modeli çerçevesinde incelenmesi. Ege Eğitim Dergisi, 13(1), 40-55. Milli Eğitim Bakanlığı (MEB). (2017a). Milli Eğitim Bakanlığı FATİH Projesi. http://fatihprojesi.meb.gov.tr adresinden 1 Nisan 2017 tarihinde edinilmiştir. Moran, M., Hawkes, M., & El Gayar, O. (2010). Tablet personal computer integration in higher education: Applying the unified theory of acceptance and use technology model to understand supporting factors. Journal of Educational Computing Research, 42(1), 79-101. Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449. Müller-Seitz, G., Dautzenberg, K., Creusen, U., & Stromereder, C. (2009). Customer acceptance of RFID technology: Evidence from the German electronic retail sector. Journal of Retailing and Consumer Services, 16(1), 31-39. Naqvi, S. J. (2012). M-services Adoption in Oman Using Technology Acceptance Modeling Approach. Communications of the IBIMA, 2012, 1. Neuman, L. W. (2007). Toplumsal araştırma yöntemleri: Nitel ve nicel yaklaşımlar (Çev. S. Özge). İstanbul: Yayın Odası. Ottenbreit-Leftwich, A. T., Brush, T. A., Strycker, J., Gronseth, S., Roman, T., Abaci, S., ... & Plucker, J. (2012). Preparation versus practice: How do teacher education programs and practicing teachers align in their use of technology to support teaching and learning?. Computers & Education, 59(2), 399-411. Ozan, O. (2013). Bağlantıcı mobil öğrenme ortamlarında yönlendirici destek (Yayımlanmamış doktora tezi). Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150. Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605. Park, Y. (2011). A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types. The International Review of Research in Open and Distributed Learning, 12(2), 78-102. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. Pullen, D., Swabey, K., Abadooz, M., & Sing, T. K. R. (2015). Pre-service teachers' acceptance and use of mobile learning in Malaysia. Australian Educational Computing, 30(1), 1-14. 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TEKNOLOJİ KABUL MODELİ ÇERÇEVESİNDE ÖĞRETMEN ADAYLARININ MOBİL TEKNOLOJİLERE YÖNELİK GÖRÜŞLERİNİN İNCELENMESİ / INVESTIGATION OF PRE-SERVICE TEACHERS' OPINIONS TOWARD MOBILE TECHNOLOGIES WITHIN THE FRAME OF TECHNOLOGY ACCEPTANCE MODEL

Yıl 2017, Cilt: 5 Sayı: 2, 1 - 18, 28.12.2017

Öz




The
purpose of this research is to examine the opinions of prospective teachers
toward mobile technology within the frame of Technology Acceptance Model
. This research aims to explain structural equation
model which is formed according to the Technology Acceptance model, in which
teacher candidates' perceived usefulness, perceived ease of use, and attitude
variables affecting behavioral intention and usage are related to the use of
mobile technologies.
In the study, in order to investigate the causal
relationship between the variables in the theoretical model was used causal research
design.
Participants of the
study consisted of 350 teacher candidates from a faculty of education at a
university in Central Anatolia. The data of the study were collected with
scale, including 4 demographic information, 3 questions and 20 items which were
measured using 7 points likert scale, developed by researchers in order to
determine the factors affecting the use of mobile technology by the teacher
candidates. The structural equation model was used to test the relationship
between perceived usefulness, perceived ease of use, attitude and behavioral
intentions within the theoretical model which is formed according to Technology
Acceptance Model.
In the research,
the good-fit indexes of the total model concurrent contribution of each
variable related to the path analysis performed for the theoretical model for
the relation between the perceived usefulness, perceived ease of use, attitude,
behavioral intention and usage were determined. The NFI, the GFI, and the AGFI
were found to be .86, .84, and .80, respectively. The goodness of fit implies
that the values ​​fit the data obtained from the theoretical model. At the same
time, the RMSEA value was set at .08, which seems to be sufficient for
harmonization. Similarly, the ratio of χ2 / df, determined as 3.68, is
indicative of compatibility between observed and reproduced covariance
matrices.
As a result of the
study, the perceived usefulness and perceived ease of use of teaching teacher
candidates' attitudes have a positive effect on attitude, and attitude has
positive effect on behavioral intention, and also behavioral intention has a
positive effect on usage.




Kaynakça

  • Anderson, J. C., & Gerbing, D. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173. Baydaş, Ö., & Göktaş, Y. (2016). Influential factors on preservice teachers' intentions to use ICT in future lessons. Computers in Human Behavior, 56, 170-178. 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. Chen, R. J. (2010). Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education, 55(1), 32-42. Ciampa, K. (2014). Learning in a mobile age: an investigation of student motivation. Journal of Computer Assisted Learning, 30(1), 82-96. Cole, D. A. (1987). Utility of confirmatory factor analysis in test validation research. Journal of Consulting and Clinical Psychology, 55(4), 1019-1031. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189−211. Comrey, A. L. & Lee, H. B. (1992). A first course in factor analysis. Hillsdale, NJ: Erlbaum. Çakıroğlu, Ü., Gökoğlu, S., & Öztürk, M. (2017). Pre-service computer teachers’ tendencies towards the use of mobile technologies: A technology acceptance model perspective. European Journal of Open, Distance and E-learning, 20(1). Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318−339. 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. Demir, K., & Akpınar, E. (2016). Mobil öğrenmeye yönelik tutum ölçeği geliştirme çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 6(1), 59-79. Doğan, M., Şen, R. & Yılmaz, V. (2015). İnternet bankacılığına ilişkin davranışların planlanmış davranış teorisi ve teknoloji kabul modeli kullanılarak önerilen bir yapısal eşitlik modeliyle incelenmesi. Uşak Üniversitesi Sosyal Bilimler Dergisi, 2015(22). Dold, C. J. (2016). Rethinking mobile learning in light of current theories and studies. The Journal of Academic Librarianship, 42(6), 679-686. DuFour, A., Lajeunesse, K., Pipada, R., Xu, S., & Nomee, J. (2017). The effect of data security perception on wearable device acceptance: a technology acceptance model. Proceedings of Student-Faculty Research Day, D11, 1-6. Eltayeb, M., & Dawson, M. (2016). Understanding user’s acceptance of personal cloud computing: Using the Technology Acceptance Model. Information Technology: New Generations, 448, 3-12. 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. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Gao, S., Krogstie, J., & Siau, K. (2011). Developing an instrument to measure the adoption of mobile services. Mobile Information Systems, 7(1), 45-67. Hamidi, H., & Chavoshi, A. (2017). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics. Hossain, L., & de Silva, A. (2009). Exploring user acceptance of technology using social networks. The Journal of High Technology Management Research, 20(1), 1-18. Howell, D. W. (2016). Social media site use and the technology acceptance model: Social media sites and organization success (Unpublished doctoral dissertation). Capella University. ITU. (2017). ICT facts & figures: The world in 2017. Jaradat, R. M. (2014). Students' attitudes and perceptions towards using m-learning for French language learning: A case study on Princess Nora University. International Journal of Man - Machine Studies, 2(1), 33-44. Jöreskog, K., & Sörbom, D. (2001). LISREL 8.51. Mooresvile: Scientific Software. Karimi, S. (2016). Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769-776. Kline, R. B. (2005). Principle and practice of structural equation modeling. New York, NY: Guilford Press. Koç, T., Turan, A. H., & Okursoy, A. (2016). Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling. The International Journal of Management Education, 14(3), 286-300. Kose, U., Koc, D., & Yucesoy, S. A. (2013). Design and development of a sample" computer programming" course tool via story-based e-learning approach. Educational Sciences: Theory and Practice, 13(2), 1235-1250. Kreijns, K., Van Acker, F., Vermeulen, M., & Van Buuren, H. (2013). What stimulates teachers to integrate ICT in their pedagogical practices? The use of digital learning materials in education. Computers in Human Behavior, 29(1), 217-225. Lawless, K. A., & Pellegrino, J. W. (2007). Professional development in integrating technology into teaching and learning: Knowns, unknowns, and ways to pursue better questions and answers. Review of Educational Research, 77(4), 575-614. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21(5-6), 385-392. Lei, J. (2009). Digital natives as preservice teachers: What technology preparation is needed?. Journal of Computing in Teacher Education, 25(3), 87-97. Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21(6), 387-395. Mac Callum, K. S. (2011). Influences on the adoption of mobile technology by students and teachers (Unpublished doctoral dissertation) Massey University, Albany, New Zealand. Mac Callum, K., Jeffrey, L., & Kinshuk. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education: Research, 13, Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factory analysis: The effects of sample size. Psychological Bulletin, 103(3), 391-410. doi: 10.1007/BF01102761 Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76-85. Meng, H., & Wang, T. (2012). Acceptance of IWBs instruction and contamitant behavior through self-regulation learning. GSTF Journal on Computing, 1(4). Menzi, N., Önal, N., & çalışkan, E. (2012). Mobil teknolojilerin eğitim amaçlı kullanımına yönelik akademisyen görüşlerinin Teknoloji Kabul Modeli çerçevesinde incelenmesi. Ege Eğitim Dergisi, 13(1), 40-55. Milli Eğitim Bakanlığı (MEB). (2017a). Milli Eğitim Bakanlığı FATİH Projesi. http://fatihprojesi.meb.gov.tr adresinden 1 Nisan 2017 tarihinde edinilmiştir. Moran, M., Hawkes, M., & El Gayar, O. (2010). Tablet personal computer integration in higher education: Applying the unified theory of acceptance and use technology model to understand supporting factors. Journal of Educational Computing Research, 42(1), 79-101. Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449. Müller-Seitz, G., Dautzenberg, K., Creusen, U., & Stromereder, C. (2009). Customer acceptance of RFID technology: Evidence from the German electronic retail sector. Journal of Retailing and Consumer Services, 16(1), 31-39. Naqvi, S. J. (2012). M-services Adoption in Oman Using Technology Acceptance Modeling Approach. Communications of the IBIMA, 2012, 1. Neuman, L. W. (2007). Toplumsal araştırma yöntemleri: Nitel ve nicel yaklaşımlar (Çev. S. Özge). İstanbul: Yayın Odası. Ottenbreit-Leftwich, A. T., Brush, T. A., Strycker, J., Gronseth, S., Roman, T., Abaci, S., ... & Plucker, J. (2012). Preparation versus practice: How do teacher education programs and practicing teachers align in their use of technology to support teaching and learning?. Computers & Education, 59(2), 399-411. Ozan, O. (2013). Bağlantıcı mobil öğrenme ortamlarında yönlendirici destek (Yayımlanmamış doktora tezi). Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150. Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605. Park, Y. (2011). A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types. The International Review of Research in Open and Distributed Learning, 12(2), 78-102. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. Pullen, D., Swabey, K., Abadooz, M., & Sing, T. K. R. (2015). Pre-service teachers' acceptance and use of mobile learning in Malaysia. Australian Educational Computing, 30(1), 1-14. Quinn, C. (2000). mLearning: Mobile, wireless, in-your-pocket learning. LiNE Zine, 2006. Schumacker, R. E., & Lomax, R. G. (1996). A beginner’s guide to structural equation modeling. Hilsdale, NJ: Erlbaum. Shafeek, S. A. (2011). E-learning Technology Acceptance Model with cultural factors. (Unpublished doctoral dissertation). Liverpool John Moores University, UK. Smith, H. J., Higgins, S., Wall, K., & Miller, J. (2005). Interactive whiteboards: boon or bandwagon? A critical review of the literature. Journal of Computer Assisted Learning, 21, 91-101. Tabachnick , B., & Fidell, L. (1996). Using multivariate Statistics. New York: Harper & Row. Tang, Y., & Tseng, H. W. (2013). Distance learners' self-efficacy and information literacy skills. The Journal of Academic Librarianship, 39(6), 517-521. Taylor, S., & Todd, P. A. (1995). Assessing IT Usage: The role of prior experience. MIS Quarterly, 19(2), 561−570.
 Tella, A., & Olasina, G. (2014). Predicting users' continuance intention toward e-payment system: An extension of the technology acceptance model. International Journal of Information Systems and Social Change (IJISSC), 5(1), 47-67. Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4), 413-424. Teo, T., & Ursavaş, Ö. F. (2012). Technology acceptance of pre-service teacher in Turkey: A croos-cultural model validation study. International Journal of Instructional Media, 39(3), 187-195. Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 1000-1009. Teo, T., Ursavaş, Ö. F., & Bahçekapılı, E. (2012). An assessment of pre-service teachers’ technology acceptance in Turkey: A structural modeling approach. The Asia-Pacific Education Researcher, 21(1), 191-202. Thong, J. Y., Hong, W., & Tam, K. Y. (2002). Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context, and individual differences?. International Journal of Human-Computer Studies, 57(3), 215-242. Tüfekci, Ö. K. (2014). Karekodların pazarlama iletişimi rolünü teknoloji kabul modeli ile açıklamaya yönelik bir araştırma. Pamukkale İşletme ve Bilim Yönetimi Dergisi, 1(1), 36-42. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. 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. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. Wong, K. T., Osman, R., Goh, S.C., ve Rahmat, M. K. (2013). Understanding Student Teachers' Behavioural Intention to Use Technology: Technology Acceptance Model (TAM) Validation and Testing. International Journal of Instruction, 6(1), 89-104. Wong, K.T., Goh, S.C., Hafizul, F. H., & Osman, R. (2010). Computer attitudes and use among novice teachers: The moderating effects of school environment. MJLI, 7, 93-112. Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model-A critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41.
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Konular Eğitim Üzerine Çalışmalar
Bölüm Makaleler
Yazarlar

Zuhal Çubukçu

Şule Betül Tosuntaş

Kağan Kırcaburun

Yayımlanma Tarihi 28 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 5 Sayı: 2

Kaynak Göster

APA Çubukçu, Z., Tosuntaş, Ş. B., & Kırcaburun, K. (2017). TEKNOLOJİ KABUL MODELİ ÇERÇEVESİNDE ÖĞRETMEN ADAYLARININ MOBİL TEKNOLOJİLERE YÖNELİK GÖRÜŞLERİNİN İNCELENMESİ / INVESTIGATION OF PRE-SERVICE TEACHERS’ OPINIONS TOWARD MOBILE TECHNOLOGIES WITHIN THE FRAME OF TECHNOLOGY ACCEPTANCE MODEL. Asian Journal of Instruction (E-AJI), 5(2), 1-18.

ASYA ÖĞRETİM DERGİSİ (E-AJI)

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