Верица Р. Милутиновић, Универзитет у Крагујевцу, Факултет педагошких наука, Јагодина, Србија, имејл: verica.milutinovic@pefja.kg.ac.rs
Данимир П. Мандић, Универзитет у Београду, Учитељски факултет, Београд, Србија
Иновације у настави, XXXV, 2022/2, стр. 71–88

| PDF | | Extended summary PDF |
DOI: 10.5937/inovacije2202071M

Резиме: Циљ овог рада је утврђивање прихватања и предвиђање употребе рачунара на традиционалном и иновативном нивоу у настави математике код наставника и будућих наставника у Србији. Досадашња истраживања су показала да намера наставника да користе рачунаре у настави има највећи утицај на прихватање. У овој студији коришћен је модел прихватања технологије за испитивање предиктора намере, проширен екстерним варијаблама преузетим из одговарајућих теорија и прилагођеним настави математике. Испитани су следећи предиктори намере: ставови испитаника према рачунарима, њихов доживљај лакоће употребе, доживљај корисности, технолошко-педагошко познавање садржаја математике, субјективна норма и технолошка комплексност. Анализа моделовања структуралним једначинама, обављена на узорку од 228 наставника и будућих наставника математике, указала је да предложени модел има добру подесност и да су дате варијабле значајни предиктори намере употребе рачунара на традиционалном и иновативном нивоу у настави математике. Са друге стране, налази указују да постоје разлике у значају, како директних, тако и индиректних предиктора код наставника и будућих наставника математике. Технолошко-педагошко познавање садржаја математике је на основу резултата доминантни предиктор намере употребе рачунара на свим нивоима осим код иновативног коришћења рачунара наставника из праксе. Њихову намеру да употребљавају рачунаре на иновативном нивоу првенствено одређују ставови према рачунарима. У складу са налазима дате су препоруке за стручно усавршавање наставника математике као и импликације за образовање будућих наставника математике.

Кључне речи: иновативни ниво употребе рачунара, модел прихватања технологије, намера употребе рачунара, настава математике, традиционални ниво употребе рачунара.

Summary: The aim of this paper is to determine whether pre-service and in-service teachers in Serbia accept to use computers in teaching mathematics and to predict their use of computers at traditional and innovative levels. Research to date has shown that teachers’ intention to use computers in their lessons has the greatest impact on their readiness or acceptance to use them. In this research we used the technology acceptance model to examine the predictors of intention, expanded with external variables taken from relevant theories and adapted for mathematics instruction. The following predictors of intention were examined: respondents’ attitudes towards computers, their experience with computers in terms of the relative easiness or difficulty of use, their opinions regarding the usefulness of computers, technological-pedagogical familiarity with the content of mathematics, сubjective norm and technological complexity. The analysis of modelling using structural equations, conducted on the sample of 228 in-service and pre-service teachers of mathematics, showed that the proposed model is appropriate and that the given variables are important predictors of teachers’ intention to use computers at both traditional and innovative levels in mathematics instruction. On the other hand, the findings indicate that there are differences among in-service and pre-service teachers in terms of the importance of direct and indirect predictors. The technological-pedagogical knowledge of the mathematics content is the dominant predictor of the intention to use computers at all levels, with the exception of the in-service teachers’ innovative use of computers. Their intention to use computers in an innovative way is primarily conditioned by their attitudes towards computers. Based on the research results, we offer recommendations for professional development of mathematics teachers, as well as implications for pre-service teachers’ education.

Кeywords: innovative level of using computers, technology acceptance model, intention to use computers, mathematics instruction, traditional level of using computers.

Литература

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
• Anderson, R. (2008). Implications of the information and knowledge society for education. In: Voogt, J. & Knezek, G. (Eds.). International handbook of information technology in primary and secondary education (5–22). NewYork, NY: Springer.
• Angeli, C. & Valanides, N. (2009). Epistemological and methodological issues for the conceptualization, development, and assessment of ICT-TPCK: advances in technological pedagogical content knowledge (TPCK). Computers & Education, 52, 154–168.
• Barak, M. (2014). Closing the Gap Between Attitudes and Perceptions About ICT-Enhanced Learning Among Pre-service STEM Teachers. J Sci Educ Technol, 23, 1–14.
• Bennison, A. & Goos, M. (2010). Learning to teach mathematics with technology: A survey of professional development needs, experiences and impacts. Mathematics Education Research Journal, 22 (1), 31–56.
• 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.
• Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35 (8), 982–1003.
• Drent, M. & Meelissen, M. (2008). Which factors obstruct or stimulate teacher educators to use ICT innovatively? Computers & Education, 51 (1), 187–199.
• Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
• Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 48, 39–50.
• Hair, J. F., Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis (seventh ed.). New Jersey: Prentice-Hall International.
• Hermans, R., Tondeur, J., Van Braak, J. & Valcke, M. (2008). The Impact of Primary School Teachers’ Educational Beliefs on The Classroom Use of Computers. Computers & Education, 51 (4), 1499–1509.
• Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
• International Society for Technology in Education (ISTE) (2016). ISTE Standards for Students. Retrieved January 11, 2022. from https://www.iste.org/standards/for-students
• Jan, A. U. & Contreras, V. (2011). Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 27, 845–851.
• Kline, R. B. (2011). Principles and practice of structural equation modelling (3rd ed). New York: Guilford Press.
• Mandić, D., Jauševac, G., Jotanović, G., Bešić, C., Vilotijević, N. & Ješić, D. (2017). Educational Innovations in the Function of Improving Students’ ICT Competences, Croatian journal of education, 19 (3), 61–74.
• Milutinović, V. (2016a). Ispitivanje prihvatanja inovativne upotrebe računara u nastavi matematike kod budućih učitelјa i nastavnika matematike. Zbornik Instituta za pedagoška istraživanja, 48 (2), 339–366. http://www.doi.org/ 10.2298/ZIPI1602339M
• Milutinović, V. (2016b). Modeling the Acceptance of the Computer Use in Teaching, In: Teodorović, J. (Ed.). Proceedings of International scientific conference Improving quality of education in elementary schools (249–264). Belgrade – Jagodina: Institute for Educational Research, Institute for Improvement of Education – Faculty of Education, University of Kragujevac.
• Milutinović, V. (2022). Examining the influence of pre-service teachers’ digital native traits on their technology acceptance: A Serbian perspective. Education and Information Technologies, 1–29. http://www.doi.org/10.1007/s10639-022-10887-y
• Miščević-Kadijević, G. M., Mandić, D. P. & Bojanić, J. B. (2019). Preschool teachers’ assessment of the integrative approach to Environmental Education. Inovacije u nastavi, 32 (3), 90–99.
• Mishra, P. & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. The Teachers College Record, 108 (6), 1017–1054.
• Niess, M.L. (2005). Preparing teachers to teach science and mathematics with technology: Developing a technology pedagogical content knowledge. Teaching and Teacher Education, 21, 509–523.
• Pierce, R. & Ball, L. (2009). Perceptions that may affect teachers’ intention to use technology in secondary mathematics classes. Educational Studies in Mathematics, 71, 299–317
• Russel, M., Bebell, D., O’Dwyer, L. & O’Connor, K. (2003). Examinig teacher technology use implications for preservice and inservice teacher preparation. Journal of Teacher Education, 54 (4), 297–310.
• Ruthven, K. (2009). Towards a naturalistic conceptualisation of technology integration in classroom practice: the example of school mathematics. Education & Didactique, 3, (1), 131–152.
• Schumacker, R. E. & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling (3rd ed.). New York: Routledge.
• Shulman, L. S. (1986). Those who understand: knowledge growth in teaching. Educational Researcher, 15, 4–14.
• Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42, 893–898.
• Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52, 302–312.
• Teo, T. & Milutinovic, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31 (4), 363–380. http://www.doi.org/ 10.14742/ajet.1668
• Teo, T., Milutinović, V. & Zhou, M. (2016). Modelling Serbian pre-service teachers’ attitudes towards computer use: A SEM and MIMIC approach. Computers & Education, 94, 77–88. http://www.doi.org/ 10.1016/j.compedu.2015.10.022
• Teo, T., Milutinović, V., Zhou, M. & Banković, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25 (7), 811–827. http://www.doi.org/ 10.1080/10494820.2016.1189943
• Thompson, R. L., Higgins, C. A. & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15 (1), 124–143.
• Venkatesh, V., Morris, M., Davis, G. & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478.
• Wachira, P. & Keengwe, J. (2011). Technology Integration Barriers: Urban School Mathematics Teachers Perspectives. J Sci Educ Technol, 20, 17–25.
• Wong, G. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong, Australasian Journal of Educational Technology, 31 (6), 713–735.
• Wong, K. T., Teo, T. & Russo, S. (2012). Influence of gender and computer teaching efficacy on computer acceptance among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of Educational Technology, 28 (7), 1190–1207.
• Wong, S. L. (2010). Guest Editorial: Impact of ICT on teaching and learning in Asia: Focusing on emerging trends, patterns and practice Siew Ming THANG The National University of Malaysia, Malaysia. International Journal of Education and Development using Information and Communication Technology, 6 (3), 3–6.
• Zakon o osnovama sistema obrazovanja i vaspitanja (2021). Službeni glasnik RS, br. 88/2017, 27/2018 – dr. zakon, 10/2019, 27/2018 – dr. zakon, 6/2020 i 129/2021.

Copyright © 2022 by the authors, licensee Teacher Education Faculty University of Belgrade, SERBIA. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original paper is accurately cited