Advertisement

Investigation of Turkish preservice teachers’ intentions to use IWB in terms of technological and pedagogical aspects

  • Salih Bardakcı
  • Muhammet Fatih AlkanEmail author
Article
  • 52 Downloads

Abstract

The purpose of this study was to investigate the technological and pedagogical constructs underlying Turkish preservice teachers’ behavioral intentions to use interactive whiteboard based on UTAUT model and TPACK using three structural equation models; technological framework, pedagogical framework, and integrated model. Within this scope, preservice teachers’ behavioral intentions to use IWB was defined as the dependent variable. Performance expectancy, effort expectancy, IWB self-efficacy, and technological knowledge were technological independent variables while individual innovativeness, technological pedagogical knowledge, pedagogical knowledge, and constructive and traditional teaching beliefs were pedagogical independent variables. Nine hypotheses were formulated based on the causal relationships between behavioral intentions to use IWB and independent variables. The proposed model was tested through SEM based on maximum likelihood estimation method using LISREL v.8.71 software. The significance of X2, the ratio of X2/df and other goodness of fit indices were used in the evaluation of the models’ fit. For hypothesis tests, path coefficients (β) and t values for each hypothesis were used. The findings showed that performance expectancy and technological pedagogical knowledge were the variables that significantly influenced the behavioral intention to use IWB in technological and pedagogical frameworks, respectively. Moreover, performance expectancy was the sole variable that significantly and positively influenced the preservice teachers’ behavioral intentions to use IWB in the integrated model. The main conclusion of this study was the revelation of teachers’ beliefs that IWB use would help them be better teachers by improving their performance independent from all other variables examined in the study.

Keywords

Interactive whiteboard Preservice teachers Path analysis UTAUT TPACK 

Notes

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.Google Scholar
  2. Aldunate, R., & Nussbaum, M. (2013). Teacher adoption of technology. Computers in Human Behavior, 29, 519–524.  https://doi.org/10.1016/j.chb.2012.10.017.Google Scholar
  3. Altun, M. (2016). The influence of using interactive whiteboard on learner achievement in the language classroom: A case study. Journal of Humanity Science, 20(4), 231–237.Google Scholar
  4. Angeli, C. (2005). Transforming a teacher education method course through technology: effects on preservice teachers’ technology competency. Computers & Education, 45(4), 383–398.  https://doi.org/10.1016/j.compedu.2004.06.002.Google Scholar
  5. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.Google Scholar
  6. Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287.Google Scholar
  7. Bardakcı, S., & Keser, H. (2017). Integration of information technologies into education. Ankara: Nobel Yayınevi.Google Scholar
  8. Batiibwe, M., & Bakkabulindi, F. (2016). Technological Pedagogical Content Knowledge (TPACK) as a theory on factors of the use of ICT in pedagogy: A review of literature. South Africa International Conference on Education: Proceedings (s. 228–241). South Africa: African Academic Research Forum.Google Scholar
  9. Baydaş, Ö., & Yılmaz, R. M. (2017). A model for preservice teachers’ intention to use interactive whiteboards in their future lessons. Journal of Higher Education and Science, 7(1), 59–66.  https://doi.org/10.5961/jhes.2017.184.Google Scholar
  10. Becit İşçitürk, G., & Kabakçı Yurdakul, I. (2014). Examining pre-service teachers’ use and acceptance of information and communication technologies in terms of certain variables. Journal of Theory and Practice in Education, 10(3), 684–702.Google Scholar
  11. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.Google Scholar
  12. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.Google Scholar
  13. Bhatiasevi, V. (2016). An extended UTAUT model to explain the adoption of mobile banking. Information Development, 32(4), 799–814.  https://doi.org/10.1177/0266666915570764.Google Scholar
  14. Birch, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295–315.Google Scholar
  15. Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods & Research, 17, 303–316.Google Scholar
  16. Brenner, A., & Brill, J. (2016). Investigating practices in teacher education that promote and inhibit technology integration transfer in early career teachers. TechTrends, 60(2), 136–144.  https://doi.org/10.1007/s11528-016-0025-8.Google Scholar
  17. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: The Guilford Press.Google Scholar
  18. 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). Newbury Park: Sage.Google Scholar
  19. Bunch, J. C., Robinson, S., & Edwards, C. (2012). Measuring the relationship between agriculture teachers’ self-efficacy, outcome expectation, interest, and their use of interactive whiteboards. Journal of Agricultural Education, 53(1), 67–80.  https://doi.org/10.5032/jae.2012.01067.Google Scholar
  20. Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications, and programming. New York: Routledge.Google Scholar
  21. Carlsson, C., Carlsson, J., Hyvönen, K., Puhakainen, J., & Walden, P. (2006). Adoption of mobile devices/services – searching for answers with the UTAUT. Proceedings of the 39th Hawaii International Conference on System Sciences. Hawaii.Google Scholar
  22. Chan, F.-M. (2002). ICT in Malaysian schools: Policy and strategies. Workshop on the Promotion of ICT in Education to Narrow the Digital Divide, (s. 15–22). Tokyo, Japan.Google Scholar
  23. Chen, C.-H. (2008). Why do teachers not practice what they believe regarding technology integration? The Journal of Educational Research, 102(1), 65–75.  https://doi.org/10.3200/JOER.102.1.65-75.Google Scholar
  24. Chen, H.-R., Chiang, C.-H., & Lin, W.-S. (2013). Learning effects of interactive whiteboard pedagogy for students in Taiwan from the perspective of multiple intelligences. Journal of Educational Computing Research, 49(2), 173–187.  https://doi.org/10.2190/EC.49.2.c.Google Scholar
  25. Cohen, L., Manion, L., & Morrison, K. (2005). Research methods in education (5th ed.). Boca Raton: Taylor & Francis.Google Scholar
  26. Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge: Harvard University Press.Google Scholar
  27. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.Google Scholar
  28. Deryakulu, D. (2000). Constructivist learning. In A. Şimşek (Ed.), Democracy in classroom (pp. 53–77). Ankara: Eğitimsen Yayınları.Google Scholar
  29. Drossel, K., Eickelmann, B., & Gerick, J. (2017). Predictors of teachers’ use of ICT in school – the relevance of school characteristics, teachers’ attitudes and teacher collaboration. Education and Information Technologies, 22(2), 551–573.  https://doi.org/10.1007/s10639-016-9476-y.Google Scholar
  30. Duru, S. (2006). Pre-service elementary education teachers’ beliefs about teaching and learning in Turkey. Unpublished doctoral dissertation, Curriculum and Instruction (Elementary Education) in the School of Education, Indiana University.Google Scholar
  31. Erbas, A. K., Ince, M., & Kaya, S. (2015). Learning mathematics with interactive whiteboards and computer-based graphing utility. International Forum of Educational Technology & Society, 18(2), 299–312.Google Scholar
  32. Ertmer, P., & Newby, T. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71.Google Scholar
  33. Ertmer, P., Ottenbreit-Leftwich, A., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59, 423–435.  https://doi.org/10.1016/j.compedu.2012.02.001.Google Scholar
  34. Evans, C., & Gibbons, N. (2007). The interactivity effect in multimedia learning. Computers & Education, 49, 1147–1160.  https://doi.org/10.1016/j.compedu.2006.01.008.Google Scholar
  35. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Boston: Addison-Wesley.Google Scholar
  36. Gil-Flores, J., Rodríguez-Santero, J., & Torres-Gordillo, J.-J. (2017). Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Computers in Human Behavior, 68, 441–449.  https://doi.org/10.1016/j.chb.2016.11.057.Google Scholar
  37. Godinho, T. (2015). Portugal: Country report on ICT in education. Brussels: European Schoolnet.Google Scholar
  38. Gürbüztürk, O., & Şad, N. (2009). Student teachers’ beliefs about teaching and their sense of self-efficacy: A descriptive and comparative analysis. Inonu University Journal of the Faculty of Education, 10(3), 201–226.Google Scholar
  39. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Upper Saddle River: Pearson Prentice Hall.Google Scholar
  40. Heemskerk, I., Kuipert, E., & Meijer, J. (2014). Interactive whiteboard and virtual learning environment combined: effects on mathematics education. Journal of Computer Assisted Learning, 30, 465–478.  https://doi.org/10.1111/jcal.12060.Google Scholar
  41. Heitink, M., Voogt, J., Verplanken, L., van Braak, J., & Fisser, P. (2016). Teachers’ professional reasoning about their pedagogical use of technology. Computers & Education, 101, 70–83.  https://doi.org/10.1016/j.compedu.2016.05.009.Google Scholar
  42. Higgins, S., Beauchamp, G., & Miller, D. (2007). Reviewing the literature on interactive whiteboards. Learning, Media and Technology, 32(3), 213–225.  https://doi.org/10.1080/17439880701511040.Google Scholar
  43. Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications. Thousand Oaks: Sage Publications.Google Scholar
  44. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.  https://doi.org/10.1080/10705519909540118.Google Scholar
  45. Hurt, H. T., Joseph, K., & Cook, C. D. (1977). Scales for the measurement of innovativeness. Human Communication Research, 4, 58–65.Google Scholar
  46. Ibieta, A., Hinostroza, E., Labbe, C., & Claro, M. (2017). The role of the Internet in teachers’ professional practice: activities and factors associated with teacher use of ICT inside and outside the classroom. Technology, Pedagogy and Education, 1–14.  https://doi.org/10.1080/1475939X.2017.1296489.
  47. Jackson, J., Yi, M., & Park, J. (2010). Effects of individual innovativeness on physician acceptance of information technology. International Journal of Services and Standards, 6(1), 21–42.Google Scholar
  48. Jackson, J., Yi, M., & Park, J. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50, 154–161.  https://doi.org/10.1016/j.im.2013.02.006.Google Scholar
  49. Jang, S.-J., & Tsai, M.-F. (2012). Exploring the TPACK of Taiwanese elementary mathematics and science teachers with respect to use of interactive whiteboards. Computers & Education, 59, 327–338.  https://doi.org/10.1016/j.compedu.2012.02.003.Google Scholar
  50. Jonassen, D. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm? ETR&D, 39(3), 5–14.Google Scholar
  51. Jöreskog, K. G., & Sörbom, D. (1988). LISREL 7: A guide to the program and applications. Chicago: SPSS.Google Scholar
  52. Karimzadeh, A., Richter, J., Basten, D., & Michalik, B. (2017). Acceptance and use of interactive whiteboards in schools: The teachers’ point of view. Thirty eighth International Conference on Information Systems, Seoul.Google Scholar
  53. Kılıç, E., Güler, Ç., Çelik, E., & Tatlı, C. (2015). Learning with interactive whiteboards: Determining the factors on promoting interactive whiteboards to students by Technology Acceptance Model. Interactive Technology and Smart Education, 12(4), 285–297.  https://doi.org/10.1108/ITSE-05-2015-0011.Google Scholar
  54. Kılıçer, K., & Odabaşı, H. F. (2010). Individual Innovativeness Scale (IS): The study of adaptation to Turkish, validity and reliability. H. U. Journal of Education, 38, 150–164.Google Scholar
  55. Kim, C. M., Kim, M. K., Lee, C., Spector, M., & DeMeester, K. (2013). Teacher beliefs and technology integration. Teaching and Teacher Education, 29, 76–85.  https://doi.org/10.1016/j.tate.2012.08.005.Google Scholar
  56. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford Publications.zbMATHGoogle Scholar
  57. Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.Google Scholar
  58. Lee, V., & Lin, S.-J. (2008). Podcasting acceptance on campus: An extension of the UTAUT model. DIGIT 2008 Proceedings (s. 2–15). Paris: Twenty Ninth International Conference on Information Systems.Google Scholar
  59. Levitt, L. (2001). An analysis of elementary teachers’ beliefs regarding the teaching and learning of science. Science Education, 86, 1–22.Google Scholar
  60. Ling, L. W., Ahmad, W. F., & Singh, T. K. (2014). Effects of the smart board on students’ achievement in moral education. Computer and Information Sciences (ICCOINS). Kuala Lumpur: IEEE.Google Scholar
  61. Ling, L. W., Ahmad, W. F., & Singh, T. K. (2016). Factors influencing behavioral intention to use the interactive white board among teachers. Journal of Theoretical and Applied Information Technology, 88(1), 145–153.Google Scholar
  62. Lu, J., Yao, J., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14, 245–268.  https://doi.org/10.1016/j.jsis.2005.07.003.Google Scholar
  63. Lumpe, A., Haney, J., & Czerniak, C. (2000). Assessing teachers’ beliefs about their science teaching context. Journal of Research in Science Teaching, 37, 275–292.Google Scholar
  64. Luo, Y.-F., & Yang, S. C. (2016). The Effect of the interactive functions of whiteboards on elementary students’ learning. Journal of Educational Computing Research, 54(5), 680–700.  https://doi.org/10.1177/0735633115628032.Google Scholar
  65. MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), 193–210.Google Scholar
  66. MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36(4), 611–637.Google Scholar
  67. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indices in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 410, 391–410.Google Scholar
  68. Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.Google Scholar
  69. MONE. (2017). FATIH Project. About Fatih Project: http://fatihprojesi.meb.gov.tr/en/?page_id=10. Accessed 3 Sept 2018.
  70. Moore, M. G. (1989). Editorial: three types of interaction. The American Journal of Distance Education, 3, 1–6.Google Scholar
  71. Mulaik, S. A., James, L. R., Alstine, J. V., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430.Google Scholar
  72. Mumtaz, S. (2000). Factors affecting teachers’ use of information and communications technology: a review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319–342.  https://doi.org/10.1080/14759390000200096.MathSciNetGoogle Scholar
  73. Muraina, I., Osman, W. R., Ahmad, A., Ibrahim, H. B., & Yusof, S. A. (2016). Modeling the behavioral intention of broadband technology usage among teenagers: Application of utaut model. Asian Journal of Information Technology, 15(3), 593–601.Google Scholar
  74. Öztürk, E., & Horzum, M. B. (2011). Adaptation of technological pedagogical content knowledge scale to Turkish. Ahi Evran University Journal of Education, 12(3), 255–278.Google Scholar
  75. Pardamean, B., & Susanto, M. (2012). Assessing user acceptance toward blog technology using the UTAUT model. International Journal of Mathematics and Computers in Simulation, 6(1), 203–212.Google Scholar
  76. Pitchanadejanant, K., & Nontakao, S. (2015). Revising and extending the determinants of the Unified Theory of Acceptance and Use of Technology (UTAUT) model in-service upper secondary level teachers’ perspectives. Journal of Education, 26(1), 25–46.Google Scholar
  77. Raman, A., Don, Y., Khalid, R., Hussin, F., Omar, M. S., & Ghani, M. (2014). Technology acceptance on smart board among teachers in Terengganu using UTAUT model. Asian Social Science, 10(11), 84–91.Google Scholar
  78. Rogers, M. (1995). Diffusion of innovations. New York: Free Press.Google Scholar
  79. Russell, M., Bebell, D., O’Dwyer, L., & O’Connor, K. (2003). Examining teacher technology use: Implications for preservice and inservice teacher preparation. Journal of Teacher Education, 54(4), 297–310.  https://doi.org/10.1177/0022487103255985.Google Scholar
  80. Sanders, M., & George, A. (2017). Viewing the changing world of educational technology from a different perspective: Present realities, past lessons, and future possibilities. Education and Information Technologies, 1–19.  https://doi.org/10.1007/s10639-017-9604-3.
  81. Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009). Technological pedagogical content knowledge (TPACK): The development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42(2), 123–149.Google Scholar
  82. Schrum, L., & Berge, Z. (1997). Creating student interaction within the educational experience: a challenge for online educators. Canadian Journal of Educational Communication, 26(3), 133–144.Google Scholar
  83. Shen, C.-C., & Chuang, H.-M. (2010). Exploring users’ attitudes and intentions toward the interactive whiteboard technology environment. International Review on Computers and Software, 5(2), 200–208.Google Scholar
  84. Shi, Y., Peng, C., Zhang, X., & Yang, H. H. (2017). Interactive whiteboard-based instruction versus lecture-based instruction: A study on college students’ academic self-efficacy and academic press. Blended Learning. New Challenges and Innovative Practices (s. 319–328). Hong Kong: International Conference on Blended Learning.Google Scholar
  85. Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–22.Google Scholar
  86. Stallard, C., & Cocker, J. (2001). The promise of technology in schools: The next 20 years. Lanham, MD: Scarecrow.Google Scholar
  87. Steiger, J. H. (1989). Causal modeling: A supplementary module for SYSTAT and SYGRAPH. Evanston: SYSTAT.Google Scholar
  88. Sumak, B., & Sorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602–620.  https://doi.org/10.1016/j.chb.2016.07.037.Google Scholar
  89. Sumak, B., Pusnik, M., Hericko, M., & Sorgo, A. (2017). Differences between prospective, existing, and former users of interactive whiteboards on external factors affecting their adoption, usage and abandonment. Computers in Human Behavior, 72, 733–756.  https://doi.org/10.1016/j.chb.2016.09.006.Google Scholar
  90. Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics. Boston: Pearson.Google Scholar
  91. Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon. Information Technology & People, 29(4), 830–849.  https://doi.org/10.1108/ITP-02-2014-0034.Google Scholar
  92. Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51–66.Google Scholar
  93. Thomson, M., & Gregory, B. (2013). Elementary teachers’ classroom practices and beliefs in relation to US science education reform: Reflections from within. International Journal of Science Education, 35(11), 1800–1823.Google Scholar
  94. Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology. Computers & Education, 81, 169–178.  https://doi.org/10.1016/j.compedu.2014.10.009.Google Scholar
  95. Uluyol, Ç., & Şahin, S. (2014). Elementary school teachers’ ICT use in the classroom and their motivators for using ICT. British Journal of Educational Technology, 47(1), 65–75.  https://doi.org/10.1111/bjet.12220.Google Scholar
  96. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.Google Scholar
  97. Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.Google Scholar
  98. Vidaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8(1), 16–37.Google Scholar
  99. Wang, Y.-S., & Shih, Y.-W. (2009). Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, 26, 158–165.  https://doi.org/10.1016/j.giq.2008.07.001.Google Scholar
  100. Wong, K.-T., Teo, T., & Goh, P. S. (2015). Understanding the intention to use interactive whiteboards: model development and testing. Interactive Learning Environments, 23(6), 731–747.  https://doi.org/10.1080/10494820.2013.806932.Google Scholar
  101. Woolley, C. A., Benjamin, W. J., & Woolley, A. W. (2004). Construct validity of a self-report measure of teacher beliefs related to constructivist and traditional approaches to teaching and learning. Educational and Psychological Measurement, 64(2), 319–331.MathSciNetGoogle Scholar
  102. Wu, D., Hiltz, S. R., & Bieber, M. (2010). Acceptance of educational technology: Field studies of asynchronous participatory examinations. Communications of the Association for Information Systems, 26(21), 451–476.Google Scholar
  103. Yi, M. 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.  https://doi.org/10.1016/S1071-5819(03)00114-9.Google Scholar
  104. Yuen, H. K., & Ma, W. K. (2002). Gender differences in teacher computer acceptance. Journal of Technology and Teacher Education, 10(3), 365–382.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer and Instructional TechnologiesTokat Gaziosmanpaşa UniversityTokatTurkey
  2. 2.Department of Educational SciencesTokat Gaziosmanpaşa UniversityTokatTurkey

Personalised recommendations