Teachers’ Attitudes Towards ICT Use in Instruction as Predictors of Frequency of ICT Use – Findings from a German representative Study
Author(s):
Manuela Endberg (presenting / submitting) Ramona Lorenz
Conference:
ECER 2016
Format:
Paper

Session Information

16 SES 01, Implementing ICT in Educational Practice - The Influence of Teachers and School Leaders

Paper Session

Time:
2016-08-23
13:15-14:45
Room:
OB-H1.49 (ALE 2)
Chair:
Ed Smeets

Contribution

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?

Method

Data was gathered from a representative sample of 1250 secondary education teachers in Germany using computer assisted personal interviews. Distributions of gender, age and type of school are consistent with the overall distribution of these factors in the population of secondary school teachers in Germany. To identify different teacher types according to their answer patterns to the indicators of attitudes towards ICT integration in instruction (RQ 1), a Latent Class Analysis (LCA) was conducted by using the statistical software Mplus 7. Indicators for possible potentials as well as risks attributed to the use of ICT in instruction have been included in the LCA, as these had been identified as contrasting aspects in the results of an exploratory factor analysis conducted beforehand using the software SPSS 22. Overall 9 indicators (5 for potentials, 4 for risks) have been included in the LCA as dichotomous variables coded as “agree”/“disagree”. According to scientific standard the number of classes fitting best with the data was decided by the BIC-value (Hagenaars & McCutcheon, 2002; Magidson & Vermunt, 2004), meaning that the class solution with the smallest BIC-value was chosen for further analyses. Thus, four types of teachers with clearly differentiable attitudes towards potentials and risks of ICT use in instruction could be identified. To find out whether the four teacher types differ in terms of ICT use (RQ 2) they have been included in a linear regression model in which frequency of ICT use in instruction is applied as the dependent variable. Contrasting the other types of teachers against one reference class using dummy coding a three-step linear regression model was computed. First, only the teacher types were included, next gender and age were included in the second step. Lastly, in accordance to the aforementioned abridged form of the TAM3 the adapted variables for job-relevance, i.e. the attributed importance of using ICT in the subject of reference as well as for computer self-efficacy, i.e. self-assessed competence in using ICT in instruction, were included in the analysis.

Expected Outcomes

By conducting a LCA we were able to identify four types of teachers which clearly differ in terms of attitudes towards ICT use in instruction: ICT-enthusiasts constitute the largest group and ascribe almost entirely potentials to using ICT in instruction. As opposed to this the ICT-skeptics mostly consider potential risks when evaluating the use of ICT in instruction and represent the smallest group. The other two types are the careful ICT-optimists tending to have rather positive attitudes towards ICT use and the reflective ICT-users which consider both potentials and risks with slightly more substance given to the potentials of ICT use. With the linear regression analysis it could be shown that teacher types who have more positive attitudes towards ICT use in instruction are actually more frequent users of ICT in instruction as well. Both additional variables for the perceived importance of ICT use in the reference subject as well as the self-assessed competence to use ICT in instruction could be identified as substantial predicators to more frequent ICT use. Thus, it can be concluded from our results that teachers, who have positive views towards ICT use, who consider ICT-use an important addition to teaching their subject and who assess themselves as rather competent users of ICT in instruction are more likely to frequently use ICT in instruction. From these findings significant implications can be derived for teacher education and professional development courses since strengthening of ICT competences and reflected discourse of potentials and risks of ICT use in instruction as part of teacher education courses can be expected to be essential factors for teachers’ actual use of ICT in instruction.

References

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.

Author Information

Manuela Endberg (presenting / submitting)
TU Dortmund University
Institute for School Development Research
Dortmund
TU Dortmund University
Institute for School Development Research
Dortmund

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