Abstract
Technology is rapidly changing the world around us and thus, there is a need to adjust education by teaching children skills that are required in the fast-paced digital life. One problem-solving skillset, which has gained considerable attention in the last couple of years, is computational thinking (CT). Up to now, many countries have already implemented CT as an integral part of their education curricula, however, there is still often the misconception that teaching CT requires high technical effort and profound knowledge of computer science. Whereas CT is useful in any subject, it is not necessarily linked to technology and helps children to tackle problems by applying skills that are used in computer science. One effective hands-on approach to foster CT in every subject is modeling. A model is a simplified and reduced version of the real world and modeling is the process of creating it. In this paper, the authors focus on fostering CT skills with models from the field of computer science (CS) in foreign language teaching. The authors present several CS models, that have proven to be useful in language teaching, demonstrate how this approach can foster CT skills and give an insight into their research.
References
Bagui, S., & Earp, R. (2011). Database design using entity-relationship diagrams. Crc Press.10.1201/9781439861776Search in Google Scholar
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.Search in Google Scholar
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), 48-54.10.1145/1929887.1929905Search in Google Scholar
BMBWF Bundesministerium für Bildung, W. u. F. (2018). Digitale Grundbildung. In: Änderung der Verordnung über die Lehrpläne der Neuen Mittelschulen sowie der Verordnung über die Lehrpläne der allgemeinbildenden höheren Schulen. Retrieved from: https://www.ris.bka.gv.at/Dokumente/BgblAuth/BGBLA_2018_II_71/BGBLA_2018_II_71.pdfsigSearch in Google Scholar
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis,P. & Punie, Y. (2016). Developing computational thinking in compulsory education. European Commission, JRC Science for Policy Report. Luxembourg, Publications Office of the European Union.Search in Google Scholar
Chen, P. P. S. (1977, June). The entity-relationship model: a basis for the enterprise view of data. In Proceedings of the June 13-16, 1977, National Computer Conference (pp. 77-84).10.1145/1499402.1499421Search in Google Scholar
Council of Europe. Council for Cultural Co-operation. Education Committee. Modern Languages Division. (2001). Common European Framework of Reference for Languages: learning, teaching, assessment. Cambridge University Press.Search in Google Scholar
Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C., & Woollard, J. (2015). Computational thinking-A guide for teachers. Swindon: Computing at School.Search in Google Scholar
Demarle-Meusel, H., Rottenhofer, M., Albaner, B. & Sabitzer, B. (2020). Educational pyramid scheme – a sustainable way of bringing innovation to schools. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1-7). IEEE.10.1109/FIE44824.2020.9274172Search in Google Scholar
Eriksson, H. E., & Penker, M. (2000). Business Mdeling with UML. New York: John Wiley & Sons, Inc.Search in Google Scholar
Fleischmann, A., Oppl, S., Schmidt, W., & Stary, C. (2018). Modelle. In Ganzheitliche Digitalisierung von Prozessen: Perspektivenwechsel–Design Thinking–Wertegeleitete Interaktion (pp. 19-69). Wiesbaden: Springer Vieweg.Search in Google Scholar
Fowler, M. (2004). UML distilled: a brief guide to the standard object modeling language. Boston: Pearson Education, Inc.Search in Google Scholar
Hubwieser, P., Mühling, A., & Aiglstorfer, G. (2015). Fundamente der Informatik: Funktionale, imperative und objektorientierte Sicht, Algorithmen und Datenstrukturen. Oldenbourg: Walter de Gruyter.Search in Google Scholar
Knogler, M., Wiesbeck A. B. & CHU Research Group (2018). Lernen mit Concept Maps: Eine Bilanz nach 42 Jahren Forschung. Retreived from: www.clearinghouse-unterricht.de, Kurzreview 19.Larsen-Search in Google Scholar
Larsen-Freeman, D. (2011). Teaching and testing grammar. In M. Long and C. Doughty, (Eds.,) The Handbook of Language Teaching. (pp. 518-542) Oxford: Blackwell Publishers.Search in Google Scholar
Lu, J. J., & Fletcher, G. H. (2009). Thinking about computational thinking. In Proceedings of the 40th ACM technical symposium on Computer science education (pp. 260-264).10.1145/1508865.1508959Search in Google Scholar
Newport, E. L. (1990). Maturational constraints on language learning. In Cognitive science, 14(1) (pp. 11-28).10.1207/s15516709cog1401_2Search in Google Scholar
Sabitzer, B., Demarle-Meusel, H., & Jarnig, M. (2018, April). Computational thinking through modeling in language lessons. In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 1913-1919). IEEE.10.1109/EDUCON.2018.8363469Search in Google Scholar
Sabitzer, B., Demarle-Meusel, H., & Rottenhofer, M. (2020, February). Modeling as Computational Thinking Language: Developing a Reference Framework. In Proceedings of the 2020 9th International Conference on Educational and Information Technology (pp. 211-214).10.1145/3383923.3383960Search in Google Scholar
Sabitzer, B., & Pasterk, S. (2015, October). Modeling: A computer science concept for general education. In 2015 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.10.1109/FIE.2015.7344062Search in Google Scholar
Salbrechter, C., Kölblinger, I., &Sabitzer, B. (2015, March). Modeling – a computational thinking concept and tool for cross-curricular teaching. In INTED2015 Proceedings, ser. 9th International Technology, Educationand Development Conference.IATED, (pp.4280–4290)Search in Google Scholar
Seidl, M., Brandsteidl, M., Huemer, C., & Kappel, G. (2012). UML@ Classroom: Eine Einführung in die objektorientierte Modellierung. Heidelberg: dpunkt. verlag.Search in Google Scholar
Sousa, D. A. (2016). How the brain learns. Thousand Oaks: Corwin Press.Search in Google Scholar
Tinkham, T. (1997). The effects of semantic and thematic clustering on the learning of second language vocabulary. Second language research, 13(2), 138-163.10.1191/026765897672376469Search in Google Scholar
Wing, J. M. (2006). Computational thinking. Communications of the ACM,49(3), 33–35.10.1145/1118178.1118215Search in Google Scholar
© 2021 Marina Rottenhofer et al., published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.