FACTORS RELATING TO THE DEVELOPMENT OF ICT COMPETENCES OF PRESERVICE PRESCHOOL TEACHERS
Suzana M. Đorđević
The aim of this study is to investigate factors correlated with the development of ICT competencies of future preschool teachers. There are a lot of factors that can be in correlation with the level of ICT competencies of preschool teachers and their intentions to use ICT in education. Based on different research, we extracted some of these factors, such as perceived usefulness, subjective norm, perceived ease of use, self-efficacy, and attitudes towards computer use. Data were collected from 48 students from the Faculty of Education in Jagodina, using a survey questionnaire during the school year of 2018/2019, from which 15 students were master pre-service preschool teachers (5th year), and others were in the first year of study. Results indicated that there was a significant correlation between the mentioned factors. Also, from the results, the master preschool teacher level of ICT competencies are higher than bachelor preschool teachers’ level of ICT competencies, although not significantly. On the other hand, master students’ intentions to use ICT in education, perceived usefulness, perceived ease of use, self-efficacy, and attitudes towards computer use were on a significantly higher level, compared with bachelor students. The results of the research can be useful in the development of curriculum at the faculties for future preschool teachers. Improving preschool teachers ICT competencies could be achieved with new elective ICT courses for preschool teacher students, practice in preschool institutions and personal professional development.
Keywords: ICT competencies, preschool teacher, preschool teachers’ education, preschool teachers’ competences.
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
Allan, Y. & Will, M. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36, 229‒243. https://doi.org/10.1080/13598660802232779
Anderson, R. (2008). Implications of the information and knowledge society foreducation. In J. Voogt, G. Knezek (Eds.), International handbook of information technologyin primary and secondary education (pp. 5‒22). New York: NY: Springer.
Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191‒215. https://doi.org/10.1016/0146-6402(78)90002-4
Barak, M. (2014). Closing the Gap Between Attitudes and Perceptions. Journal of-Science, 1‒14. 4 https://doi.org/10.1007/s10956-013-9446-8
Brun, M. & Hinostroza, J. E. (2014). Learning to become a teacher in the 21st century: ICT integration in Initial. Educational Technology & Society, 222‒238.
Cheung, R. & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers&Education, 63, 160–175. https://doi.org/10.1016/j.compedu.2012.12.003
Chien, Y.-T., Chang, C.-Y., Yeh, T.-K. & Chang, K.-E. (2012). Engaging pre-service science teachers to act as active designers of technology integration: A MAGDAIRE framework. Teaching and Teacher Education, 28, 578‒588. https://doi.org/10.1016/j.tate.2011.12.005
Compeau, D. R. & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189‒211. https://doi.org/10.2307/249688
Davis, F. (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., Bagozzi, R. & Warshaw, P. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 982‒1003 https://doi.org/10.1287/mnsc.35.8.982
De Vellis, R. (2003). Scale development: Theory and applications (2nd ed.). California: Thousand Oaks, California: Sage.
Ertmer, P. (2005). Teacher Pedagogical Beliefs: The Final Frontier in Our Quest for Technology Integration? Educational Technology Research and Development, 53(4),25–39.
Gao, P., Wong, A., Choy, D. & Wu, J. (2011). Beginning teachers’ understanding performances of technology integration. Asia Pacific Journal of Education, 31(2), 211‒223. https://doi.org/10.1080/02188791.2011.567003
Hermans, R., Tondeur, J., Van Braak, J. & Valcke, M. (2008). The impact of primaryschool teachers’ educational beliefs on the classroom use of computers. Computers & Education, 51(4), 1499‒1509. https://doi.org/10.1016/j.compedu.2008.02.001
Holden, H. & Rada, R. (2011). Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers’ Technology Acceptance. Journal of Researchon Technology in Education, 43(4), 343‒367. https://doi.org/10.1080/15391523.2011.10782576
Holland, D. & Piper, R. (2016). A Technology Integration Education (TIE) Model for Millennial Preservice Teachers: Exploring the Canonical Correlation Relationships Among Attitudes, Subjective Norms, Perceived Behavioral Controls, Motivation, and Technological, Pedagogical, and Content. Journal of Research on Technology in Education, 48, 212‒226.
Kasuya, E. (2001). Mann-Whitney U test when variances are unequal. Animal Behaviour, 61(6), 1247‒1249. https://doi.org/10.1006/anbe.2001.1691
Kay, R. (2006). Evaluating Strategies Used To Incorporate Technology Into Preservice Education. Journal of Research on Technology in Education, 38, 383‒408. https://doi.org/10.1080/15391523.2006.10782466
Kennisnet (2012). ICT proficiency of teachers (ICT-bekwaamheid van leraren). Netherlands: Kennisnet.
Milutinović, V. (2009). Factors of ICT application in education: Mentors and student teachers. Promoting Teacher Education – From Intake System To Teaching Practice: Proceedings of the international conference, 1, 175–187.
Milutinović, V. R. (2016). Faktori koju utiču na nameru budućih učitelja da koriste računar u nastavi. Uzdanica, 97‒98.
Motaghian, H., Hassanzadeh, A. & Moghadam, D. K. (2013). Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Computers & Education, (6)1), 158–167. https://doi.org/10.1016/j.compedu.2012.09.016
Nachar, N. (2008). The Mann‒Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution. Tutorials in Quantitative Methods for Psychology, 4(1), 13‒20. https://doi.org/10.20982/tqmp.04.1.p013
Nam, C. S., Bahn, S. & Lee, R. (2013). Acceptance of assistive technology by special education teachers: A structural equation model approach. International Journal of Human-Computer Interaction, 29(5), 365‒377. https://doi.org/10.1080/10447318.2012.711990
Ottenbreit-Leftwich, A., Glazewski, K., Newby, T. & Ertmer, P. (2010). Teacher value beliefs associated with using technology: Addressing professional and studentneeds. Computers & Education, 55, 1321‒1335. https://doi.org/10.1016/j.compedu.2010.06.002
Pallant, J. (2007). SPSS Survival Manual: A step by step guide to data analysis using-SPSS. Crows Nest: Allen & Unwin.
Polly, D., Mims, C., Shepherd, C. E. & Inan, F. (2010). Evidence of impact: Transforming teacher education with preparing tomorrow’s teachers to teach with technology (PT3) grants. Teaching and Teacher Education, 26, 863‒870. https://doi.org/10.1016/j.tate.2009.10.024
Russell, M., Bebell, D., O’Dwyer, L. & Duffany, O. (2003). Examining Teacher Technology Use. Journal of Teacher Education, 54, 297‒310. https://doi.org/10.1177/0022487103255985
Sanchez-Prieto, J. C., Olmos-Miguelanez, S. & Garcia-Penalvo, F. J. (2016). Mlearning and pre-service teachers: An assessment of the behavioral. Computers in Human Behavior, 1‒11. https://doi.org/10.1016/j.chb.2016.09.061
Sang, G., Tondeur, J., Ching, S. & Dong, Y. (2015). Validation and Profile of Chinese Pre-service Teachers’ Technological Pedagogical Content Knowledge Scale. Asia-Pacific Journal of Teacher Education, 44, 1‒17. https://doi.org/10.1080/1359866X.2014.960800
Schepers, J. & Wetzels, M. (2007). A meta-analysis of the technology acceptancemodel:investigating subjective norm and moderation effects. Information & Management,(44), 90–103. https://doi.org/10.1016/j.im.2006.10.007
Schumacker, R. & Lomax, R. (2010). A beginner’s guide to structural equation modeling(3rd ed.). New York: New York: Routledge.
Tarhini, A., Hone, K. & Liu, X. (2014). A cross-cultural examination of the impactof social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739‒755. https://doi.org/10.1111/bjet.12169
Taylor, S. & Todd, P. (1995). Understanding information technology usage: A testof competing models. Information Systems Research, 6, 144‒176. https://doi.org/10.1287/isre.6.2.144
Teo, T. (2009a). Evaluating the intention to use technology among student teachers: A structural equation modeling approach. International Journal of Technology in-Teaching and Learning, 5(2), 106‒118.
Teo, T. (2009b). Modeling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52, 302–312. https://doi.org/10.1016/j.compedu.2008.08.006
Teo, T. (2010). Path analysis of pre-service teachers’ attitudes to computer use:Applying and extending the Technology Acceptance Model in an educational context. Interactive Learning Environments, 18(1), 65‒79. https://doi.org/10.1080/10494820802231327
Teo, T. & Milutinović, V. (2015). Modeling the intention to use technology forteaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 363‒380. https://doi.org/10.14742/ajet.1668
Thompson, R., Higgins, C. & Howell, J. (1991). Personal computing: toward aconceptual model of utilization. MIS Quarterly, 15, 124–143. https://doi.org/10.2307/249443
Tondeur, J., Aesaert, K., Prestridge, S. & Consuegra, E. (2018). A multilevel analysis of what matters in the training of pre-service. Computers & Education, 122, 32‒42. https://doi.org/10.1016/j.compedu.2018.03.002
Van Dinther, M., Dochy, F., Segers, M. R. & Braeken, J. (2013). The construct validityand predictive validity of a self-efficacy measure for student teachers in competence-based education. Studies In Educational Evaluation, 39(3), 169‒179. https://doi.org/10.1016/j.stueduc.2013.05.001
Venkatesh, V. & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision science, 39(2), 273‒315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V. & Davis, F. (2000). A theoretical extension of technology acceptance model: Four longitudinal field studies. Management Science, (46), 186–204. https://doi.org/10.1287/mnsc.220.127.116.1126
Venkatesh, V., Morris, M., Davis, G. & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478. https://doi.org/10.2307/30036540
Wong, G. (2015). Understanding technology acceptance in pre-service. Australasian Journal of Educational, 31, 713–735. https://doi.org/10.14742/ajet.1890
Wong, K.-T., Teo, T. & Russo, S. (2012). Influence of gender and computer teachingefficacy on computer acceptance among Malaysian student teachers: An extendedtechnology acceptance model. Australasian Journal of Educational Technology, 28(7), 1190‒1207. https://doi.org/10.14742/ajet.796