16 SES 01, Implementing ICT in Educational Practice - The Influence of Teachers and School Leaders
Digital competence is regarded as a key competence for lifelong learning (European Commission, 2006) and schools bear the responsibility to prepare students for life in the digital age. This results in specific challenges for teachers.
So far, research attested that teachers’ attitudes in addition to ICT-equipment/infrastructure and self-reported level of competency can be expected to be important factors for ICT use in instruction (Drent & Meelissen, 2008; Hew & Brush, 2007; Petko, 2012). The International Computer and Information Literacy Study (ICILS) 2013 (Fraillon, Ainley, Schulz, Friedman & Gebhardt, 2014) revealed that in international comparison teachers in Germany have less positive attitudes towards ICT use in instruction. Therefore, the Germany-wide representative study this paper is based on took into account teachers’ attitudes towards the use of ICT in instruction in order to investigate whether various types of teachers can be identified that differ in terms of their attitudes towards ICT use as well as their actual frequency of ICT use.
Aiming at predicting use behavior the advanced Technological Acceptance Model (TAM3) is applied as theoretical framework (Prasse, 2012; Venkatesh & Bala, 2008). However, since the TAM3 was developed for organizational contexts rather than the specific context of ICT in instruction, the original model has been adapted and abridged. Focusing on the dependent outcome variable use behavior, here measured as frequency of ICT use, three predictors from TAM3 are taken into account: perceived usefulness, job-relevance and computer self-efficacy. In TAM3 all of these are modelled as indirect predictors of use behavior, whereas here we investigate the direct effects of these factors on frequency of ICT use. Also, in derogation from the originally rather abstract terminology we defined the predictors more precisely for the context of ICT use in instruction. Therefore, perceived usefulness is understood as perceived possible potentials and risks coming along with ICT use in instruction, treated as separate item scales. Job-relevance originally referring to an “individual's perception regarding the degree to which the target system is relevant to his or her job” (Venkatesh & Bala, 2008, 277) is defined as the importance teachers attribute to using ICT in their subject of reference. Lastly, computer self-efficacy is measured using self-assessment of teachers’ competence to use ICT in instruction.
International research has been conducted to portray the effect different attitude variables have on ICT use in instruction (Chen, 2010; Teo, 2011, 2015; Teo & Noyes, 2011; van Braak, Tondeur & Valcke, 2004), some of them are also based on the original Technology Acceptance Model (TAM; Davis, Bagozzi, & Warshaw, 1989). Overall, research findings show that attitudes play an important role for integrating ICT in instruction. However, the connection between teachers’ attitudes and their actual frequency of ICT use in instruction has not yet been investigated in-depth in the German school system. Against this backdrop we focus the following research questions:
- Can different types of teachers be identified according to their attitudes towards ICT use in instruction?
- Do different attitudes towards ICT lead to more or less frequent use of ICT in instruction?
Chen, R.-J. (2010). Investigating models for preservice teachers’ use of technology to support Student-centered learning. Computers & Education, 55(1), 32–42. Davis, F.D., Bagozzi, R.P. & Warshaw, P.R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982-1003. Drent, M. & Meelissen, M. (2008). Which factors obstruct or stimulate teacher educators to use ICT innovatively? Computers & Education, 51(1), 187–199. European Commission. (2006). Recommendation 2006/962/EC of the European Parliament and of the Council of 18 December 2006 on key competences for lifelong learning. Luxembourg Brussels: Author. 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. Cahm: Springer. Hagenaars, J., & McCutcheon, A. (Eds.). (2002). Applied latent class analysis models. New York: Cambridge University Press. Hew, K. F. & Brush, T. (2007). Integrating technology into K-12 teaching and learning: current knowledge gaps and recommendations for future research. Educational Technology Research & Development 55, 223–252. Magidson, J., & Vermunt, J. (2004). Latent class models. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 175–198). Newbury Park, CA: Sage. Petko, Dominik (2012): Teachers’ pedagogical beliefs and their use of digital media in classrooms: Sharpening the focus of the ‘will, skill, tool’ model and integrating teachers’ constructivist orientations. In: Computers & Education 58(4), 1351–1359. Prasse, D. (2012). Bedingungen innovativen Handelns in Schulen. Funktion und Interaktion von Innovationsbereitschaft, Innovationsklima und Akteursnetzwerken am Beispiel der IKT-Integration an Schulen. Münster: Waxmann. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. Teo, T. (2015). Comparing pre-service and in-service teachers’ acceptance of technology: Assessment of measurement invariance and latent mean differences. Computers & Education, 83, 22–31. Teo, T. & Noyes, J. (2011). An assessment of the infl uence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education, 57(2), 1645–1653. van Braak, J., Tondeur, J. & Valcke, M. (2004). Explaining different types of computer use among primary school teachers. European Journal of Psychology of Education, 19(4), 407–422. Venkatesh, V. & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2) , 273–315.
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