16 SES 11 B, ICT Competencies
Education systems worldwide currently face changes brought on by the near ubiquitous availability of information and communication technology (ICT) and the need for all generations to be able to competently use it to allow participation in the digital age and avoid exclusion. Schools, especially, face challenges regarding the fostering of students’ competencies to safely, autonomously, productively and reflectively use ICT (Barr, & Stephenson, 2011; Fraillon et al., 2014; Galan, 2015). As those competencies accommodate different aspects, several frameworks can be used as reference to define the skills students need to acquire.
One approach was established through the International Computer and Information Literacy Study (ICILS) 2013, which aimed at measuring 8th grade students’ computer and information literacy (CIL). CIL is defined as “an individual’s ability to use computers to investigate, create, and communicate in order to participate effectively at home, at school, in the workplace, and in society” (Fraillon, Schulz, & Ainley, 2013, p. 17). The ICILS framework depicts CIL along two strands (Collecting and managing information and Producing and exchanging information) which both encompass several aspects which add detail to the range of CIL. To assess and compare students’ CIL, five proficiency levels were defined, ranging from below Level 1 to Level 4 (Fraillon et al., 2014).
The notion of CIL underlies a number of restrictions mostly due to the challenge of guaranteeing international measurability and comparison. Another approach is established in the concept of Media Literacy (ML) which oftentimes is used synonymously to Media Education(Galan, 2015). Media literacy can be defined as “the ability to access the media, to understand and critically evaluate media contents and different aspects of the media and to create communications in a variety of contexts” (Zacchetti, 2007, p. 10). It is important to notice that media literacy “relates to all media including television and film, radio and recorded music, print media, the Internet and all other digital communication technologies” (Hartai, 2014, p. 16).
Still a different approach lies in the definition of Computational Thinking (CT), which “involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science” (Wing, 2006, p. 33). Characteristics of CT to be considered in an educational context are defined by Rode et al. (2015) and range from the ability to analyze and organize data logically, to the ability to generalize and apply a problem solving process to other kind of problems.
As all three frameworks address important aspects of being able to successfully participate in the 21st century societies, schools face the challenge to foster those skills and competencies. Of late, a national policy is in place in Germany which defines digital competency standards all students should have acquired by the end of compulsory education (KMK, 2016). As one constituent of these standards the ICILS-framework was used. One aim of this paper is to reveal how teachers foster students’ competencies in CIL, ML and CT and to show how teachers’ efforts of fostering in these three domains might be correlated. Also, teachers’ efforts of fostering ML and CT as important domains only marginally covered in the national policy are analyzed with regard to different fostering patterns.
The research questions considered in this paper are:
1) How do teachers foster students’ competencies in CIL, ML and CT?
2) Are teachers’ fostering activities concerning CIL, ML and CT correlated?
3) Can different types of teachers be identified according to their efforts of addressing aspects of ML and CT?
Data was gathered from a representative sample of 1218 secondary school 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. Three domains of students’ competencies to be actively fostered by teachers during instruction were differentiated: CIL (5 items), ME (5 items) and CT (4 items). All indicators were developed within the frame of the study “Schule digital – der Länderindikator” (Lorenz et al., 2017) and have undergone descriptive analyses, comprising means and standard deviations. Concerning CIL, teachers were asked to indicate whether or not they use computers in instruction in order to foster students’ competencies according to different proficiency levels as defined in ICILS 2013. Indicators denoting the fostering of ME and CT referred to how often specific aspects of either are addressed in instruction and were to be rated on a five-point frequency scale (Never, Less than once a month, At least once a month, but not every week, At least once a week, but not every day, Every day). All three scales underwent confirmatory factor analysis (CFA) by use of the statistical software SPSS 25 which resulted in good model fits. Bivariate correlation analyses were computed with SPSS 25 to address the second research question. To identify different teacher types according to their fostering activities regarding ME and CT a Latent Class Analysis (LCA) was conducted by using the statistical software Mplus 7. Therefore, 9 indicators were included in the LCA coded by the aforementioned 5-point-Likert scale. The number of latent classes was decided by considerations of different methods. As the information criteria (AIC and BIC) did not decisively indicate the number of classes fitting best with the data (Hagenaars, & McCutcheon, 2002), different tests of the data were undertaken to find the best-fitting as well as the best-interpretable solution (e.g. Asparouhov, & Muthén, 2012). Post-hoc application of the split-half reliability test was undertaken resulting in the replication of the found solution for both randomly-split halves of the overall sample.
Descriptive analyses reveal that at least three fifth of the teachers in Germany state to foster students’ CIL according to all five proficiency levels. However, less than four percent of the teachers address aspects of ML and CT in instruction every day, while about 40 percent regarding ML and more than 50 percent regarding CT affirm to never approach aspects of either during instruction. The results of the correlation analyses disclose statistical significant correlations (p = .01) between all three scales. The coefficient calculated between CIL and ML was highest, while ML and CT were shown to be least correlated. Analyzing teachers’ efforts to foster students’ skills concerning ML and CT five types of teachers with clearly differentiable patterns of fostering efforts could be identified. (1) About a third of the teachers show barely any fostering efforts. (2) The largest proportion of teachers (36 %) rarely addresses aspects of ML, but hardly ever aspects of CT; (3) about ten percent of the teachers show a pattern mirrored to this in focusing aspects of CT in instruction. (4) The fourth teacher type actively fosters students’ ML on a regular basis, but scarcely ventures into teaching CT. (5) The smallest percentage of teachers (8 %) focuses on both ML and CT as essential topics to be regularly addressed in instruction. To avoid exclusion in the digital age, students need to acquire multifaceted ICT-related competencies which, as our findings show, can be fostered by teachers. The results, however, also indicate that fostering efforts are not equally distributed regarding the three domains. Thus, a more comprehensive professionalization of (pre-service) teachers involving methods of fostering CIL, ML and CT as well as the implementation of structured teacher collaboration in schools may aid to appropriately prepare students for participation in the digital age.
Asparouhov, T., & Muthén, B. (2012). Using Mplus TECH11 and TECH14 to test the number of latent classes. Mplus Web Notes, 14. Barr, V., & Stephenson, C. (2011). Bringing Computational Thinking to K-12: What is Involved and What is the Role of the Computer Science Education Community? acm Inroads, 2(1), 48–54. Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (Eds.). (2014). Preparing for Life in a Digital Age. The IEA International Computer and Information Literacy Study. International Report. Fraillon, J., Schulz, W., & Ainley, J. (2013). International Computer and Information Literacy Study assessment framework. Amsterdam, the Netherlands: International Association for the Evaluation of Educational Achievement (IEA). Galan, J. G. (2015). Media Education as theoretical and practical paradigm for digital literacy. An interdisciplinary analysis. European Journal of Science and Theology, 11(3), 31–44. Hagenaars, J., & McCutcheon, A. (2002). Applied latent class analysis models. New York: Cambridge University Press. Hartai, L. (2014). Report on Formal Media Education in Europe. KMK [The Standing Conference of the Ministers of Education and Cultural Affairs of the Länder in the Federal Republic of Germany]. (2016). Bildung in der digitalen Welt. Strategie der Kultusministerkonferenz. Available at https://www.kmk.org/aktuelles/thema-2016-bildung-in-der-digitalen-welt.html Lorenz, R., Bos, W., Endberg, M., Eickelmann, B., Grafe, S., & Vahrenhold, J. (Eds.). (2017). Schule digital – der Länderindikator 2017. Schulische Medienbildung in der Sekundarstufe I mit besonderem Fokus auf MINT-Fächer im Bundesländervergleich und Trends von 2015 bis 2017. Münster: Waxmann. Rode, J. A., Weibert, A., Marshall, A., Aal, K., von Rekowski, T., el Mimoni, H., & Booker, J. (2015). From Computational Thinking to Computational Making. UbiComp '15 Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 239–250). Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33–36. Zacchetti, M. (2007). Media Literacy: A European approach. medienimpulse, 61, 10–13.
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