Construction, factor structure and reliability of an instrument to investigate teachers’ perceptions and attitudes towards the use of digital technologies in teaching
DOI:
https://doi.org/10.7346/sird-022022-p62Keywords:
Information and Communication Technology, Educational Technology, Technology Acceptance Model, In-service teachers, Scale developmentAbstract
The paper presents the process of constructing and examining the factor structure and reliability of an instrument (“TECNOINS”) to investigate in-service teachers’ perceptions and attitudes towards the use of digital technologies in teaching. The exploration of the factor structure - in a sample of 165 primary and secondary school teachers - yielded three factors corresponding to the three theoretical constructs considered within the Technology Acceptance Model: attitude, perceived usefulness and perceived ease of use. In addition, each factor demonstrated reliability estimates in the acceptable to good range of internal consistency and showed adherence to the theoretical constructs. Although some psychometric characteristics remain to be assessed, the instrument “TECNOINS” can represent a valuable research tool for monitoring and evaluating initial and continuous teacher education and training, Action-Research or Training-Research, as well as teaching-technological innovation practices in the school context.
References
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control (pp. 11-39). New York, NY, USA: Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ, USA: Prentice Hall.
Bandalos, D. L., & Gerstner, J. J. (2016), Using factor analysis in test construction. In K. Schweizer & C. DiStefano (Eds.), Principles and methods of test construction: Standards and recent advances (pp. 26-51). Boston, MA, USA: Hogrefe.
Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Statistical Psychology, 3, 77-85. https://doi.org/10.1111/j.2044-8317.1950.tb00285.x
Bartlett, M. S. (1954). A note on the multiplying factors for various χ2 approximations. Journal of the Royal Statistical Society, Series B (Methodological), 16, 296-298. https://doi.org/10.1111/j.2517-6161.1954.tb00174.x
Baydas, O., & Goktas, Y. (2017). A model for preservice teachers’ intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930-945. https://doi.org/10.1080/10494820.2016.1232277
Beller, M. (2013). Technologies in large-scale assessments: New directions, challenges, and opportunities. In M. v. Davier, E. Gonzalez, I. Kirsch, & K. Yamamoto (Eds.), The role of international large-scale assessments: Perspectives from technology, economy, and educational research (pp. 25-45). Dordrecht, NL: Springer Science + Business Media. https://doi.org/10.1007/978-94-007-4629-9_3
Berrett, B., Murphy, J., & Sullivan, J. (2012). Administrator insights and reflections: Technology integration in schools. The Qualitative Report, 17(1), 200-221. https://doi.org/10.46743/2160-3715/2012.1815
Bishop, M. J., & Spector, J. M. (2014). Technology integration. In J. M. Spector, D. Merrill, J. Elen & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed.) (pp. 817-818). New York, NY, USA: Springer Science + Business Media.
Briggs, N. E., & MacCallum, R. C. (2003). Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38(1), 25-56. https://doi.org/10.1207/s15327906mbr3801_2
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY, USA: Guilford Press.
Cattell, R. B. (1966). The scree test for the number of factors. Multivariate behavioral research, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10
Child, D. (2006). The essentials of factor analysis (3rd ed.). New York, NY, USA: Continuum.
Chiorri, C. (2011). Teoria e tecnica psicometrica: Costruire un test psicologico. Milano, IT: McGraw-Hill Education.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, MI, USA: Erlbaum.
Commissione Europea (2018). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions “on the Digital Education Action Plan”. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52018DC0022&from=it
Commissione Europea (2020). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions “on achieving the European Education Area by 2025”. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52020DC0625&from=EN
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
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS quarterly, 22(2), 145-158. https://doi.org/10.2307/249749
Consiglio Europeo (2014). Council conclusions of 20 May 2014 on effective teacher education. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52014XG0614(05)&from=EN
Consiglio Europeo (2017a). Council Conclusions on school development and excellent teaching. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX%3A52017XG1208%2801%29&from=EN
Consiglio Europeo (2017b). Conclusioni del Consiglio Europeo del 19 ottobre 2017. Retrieved from https://www.consilium.europa.eu/media/21608/19-euco-final-conclusions-it.pdf
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/bf02310555
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological methods, 1(1), 16-29. https://doi.org/10.1037/1082-989x.1.1.16
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), 982-1002. https://doi.org/10.1287/mnsc.35.8.982
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (PhD thesis). Cambridge, MA, USA: Massachusetts Institute of Technology. Retrieved from https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf?sequence=2
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
Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration?. Educational technology research and development, 53(4), 25-39. https://doi.org/10.1007/bf02504683
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989x.4.3.272
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA, USA: Addison-Wesley.
Flórez, F. B., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation's way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860. https://doi.org/10.3102/0034654317710096
Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age - the IEA international computer and information literacy study international report. Heidelberg, New York, Dordrecht, London: Springer International Publishing.
George, A., & Sanders, M. (2017). Evaluating the potential of teacher-designed technology-based tasks for meaningful learning: Identifying needs for professional development. Education and Information Technologies, 22(6), 2871-2895. https://doi.org/10.1007/s10639-017-9609-y
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston, MA, USA: Allyn & Bacon.
Gilbert, A. D. (2015). An exploration of the use of and the attitudes toward technology in first-year instrumental music. (PhD thesis). Lincoln, NE, USA: University of Nebraska-Lincoln. Retrieved from http://digitalcommons.unl.edu/musicstudent/79
Guttman, L. (1954). Some necessary conditions for common-factor analysis. Psychometrika, 19(2), 149-161. https://doi.org/10.1007/bf02289162
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ, USA: Pearson Prentice Hall.
Hogarty, K. Y., Hines, C. V., Kromrey, J. D., Ferron, J. M., & Mumford, K. R. (2005). The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination. Educational and psychological measurement, 65(2), 202-226. https://doi.org/10.1177/0013164404267287
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185. https://doi.org/10.1007/bf02289447
Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co- citation analysis. International Journal of Information Management, 31(2), 128-136. https://doi.org/10.1016/j.ijinfomgt.2010.07.003
Ingoglia, S. (2013). L’analisi fattoriale confermativa e le sue applicazioni ai problemi della misurazione. In C. Barbaranelli & S. Ingoglia (Eds.), I modelli di equazioni strutturali: Temi e prospettive (pp. 59-110). Milano, IT: LED.
Jennrich, R. I., & Sampson, P. F. (1966). Rotation for simple loadings. Psychometrika, 31(3), 313-323. https://doi.org/10.1007/bf02289465
Jöreskog, K. G., & van Thillo, M. (1972). LISREL: A general computer program for estimating a linear structural equation system involving multiple indicators of unmeasured variables. Princeton, NJ, USA: Educational Testing Service.
Kaiser, H. F. (1970). A second generation Little Jiffy. Psychometrika, 35(4), 401-416. https://doi.org/10.1007/bf02291817
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/bf02291575
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
Kunce, J. T., Cook, W. D., & Miller, D. E. (1975). Random variables and correlational overkill. Educational and Psychological Measurement, 35(3), 529-534. https://doi.org/10.1177/001316447503500301
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. https://doi.org/10.1207/s15327906mbr3604_06
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological methods, 4(1), 84-99. https://doi.org/10.1037/1082-989X.4.1.84
Maggino, F., & Mola, T. (2007). Il differenziale semantico per la misura degli atteggiamenti: costruzione, applicazione e analisi. Presentazione di uno studio. Firenze, IT: Firenze University Press.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95. https://doi.org/10.1007/s10209-014-0348-1
Marascuilo, L. A., & Levin, J. R. (1983). Multivariate Statistics in the Social Sciences. Monterey, CA, USA: Brooks/Cole.
Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students' evaluations of university teaching. Structural equation modeling: A multidisciplinary journal, 16(3), 439-476. https://doi.org/10.1080/10705510903008220
McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale, NJ, USA: Lawrence Erlbaum.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222. https://doi.org/10.1287/isre.2.3.192
Nair, I., & Das, V. M. (2011). Analysis of recent studies undertaken for assessing acceptance of technology among teachers using TAM. International Journal of Computer Applications, 32(8), 38-46.
Nistor, N., & Heymann, J. O. (2010). Reconsidering the role of attitude in the TAM: An answer to Teo (2009a). British Journal of Educational Technology, 41(6), E142-E145. https://doi.org/10.1111/j.1467-8535.2010.01109.x
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York, NY, USA: McGraw Hill.
OCSE (2015). Students, computers and learning: Making the connection. Paris, FR: OECD Publishing.
OCSE (2019). TALIS 2018 Results (Vol. 1): Teachers and School Leaders as Lifelong Learners. Paris, FR: OECD Publishing.
Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. Urbana, IL, USA: University of Illinois press.
Parlamento Europeo e Consiglio (2006). Raccomandazione del Parlamento Europeo e del Consiglio del 18 dicembre 2006 relativa a competenze chiave per l'apprendimento permanente. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32006H0962&from=EN
Punie, Y., & Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Luxembourg, LU: Publications Office of the European Union.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & management, 44(1), 90-103. https://doi.org/10.1016/j.im.2006.10.007
Scherer, R., Siddiq, F., & Teo, T. (2015). Becoming more specific: Measuring and modeling teachers’ perceived usefulness of ICT in the context of teaching and learning. Computers & Education, 88, 202-214. https://doi.org/10.1016/j.compedu.2015.05.005
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Scherer, R., Tondeur, J., Siddiq, F., & Baran, E. (2018). The importance of attitudes toward technology for pre-service teachers' technological, pedagogical, and content knowledge: Comparing structural equation modeling approaches. Computers in Human Behavior, 80, 67-80. https://doi.org/10.1016/j.chb.2017.11.003
Shiue, Y. M. (2007). Investigating the sources of teachers' instructional technology use through the decomposed theory of planned behavior. Journal of Educational Computing Research, 36(4), 425-453. https://doi.org/10.2190/a407-22rr-50x6-2830
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625-649. https://doi.org/10.3102/0034654308325896
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967. https://doi.org/10.1016/j.promfg.2018.03.137
Taylor, S., & Todd, P. A. (1995a). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
Taylor, S., & Todd, P. A. (1995b). Assessing IT usage: The role of prior experience. MIS quarterly, 19(4), 561-570. https://doi.org/10.2307/249633
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006
Teo, T., Ursavaş, Ö. F., & Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre‐service teachers' intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93-101. https://doi.org/10.1108/10650741111117798
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 15(1), 125-143. https://doi.org/10.2307/249443
Thurstone, L. L. (1947). Multiple factor analysis. Chicago, IL, USA: University of Chicago Press.
van Braak, J. (2001). Individual characteristics influencing teachers' class use of computers. Journal of educational computing research, 25(2), 141-157. https://doi.org/10.2190/81yv-cgmu-5hpm-04eg
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Voogt, J., Shin, T., Mishra, P., Koehler, M., Schmidt, D., Baran, E., et al. (2011). Teachers’ assessment of TPACK: Where are we and what is needed?. In M. Koehler & P. Mishra (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 4422-4426). Chesapeake, VA, USA: Association for the Advancement of Computing in Education (AACE).
Zhang, P., Aikman, S. N., & Sun, H. (2008). Two types of attitudes in ICT acceptance and use. Intl. Journal of Human-Computer Interaction, 24(7), 628-648. https://doi.org/10.1080/10447310802335482
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