dEjournals Home Page
Teaching Mathematics and Computer Science
Home Current Archives Search
Register Login

Teaching Mathematics and Computer Science

Vol. 22 No. 1 (2024)
Articles

Computer cooking vs. problem solving

Published:
2024-07-12
DOI Linkhttps://doi.org/10.5485/TMCS.2024.13454
Authors
Mária Csernoch,
Tímea Nagy,
Júlia Csernoch
View
PDF
Keywords
informatics curricula knowledge-transfer subject integration course books problem solving
License

Copyright (c) 2024 Mária Csernoch, Tímea Nagy, Júlia Csernoch

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How To Cite
Selected Style: APA
Csernoch, M., Nagy, T., & Csernoch, J. (2024). Computer cooking vs. problem solving. Teaching Mathematics and Computer Science, 22(1), 35-58. https://doi.org/10.5485/TMCS.2024.13454
Abstract

Computer cooking is a task-related phenomenon where students (end-users) must blindly follow a long list of orders without any connection to the content of the problem, if there is any. Despite its low efficacy, this method is widely used and accepted in informatics both in the learning-teaching process and testing. The National Base Curriculum 2020 in Hungary is in complete accordance with the ‘Informatics Reference Framework for Schools’, but the course books hardly use the latest results of computer education research. The present paper provides examples of how the results of computer education research can be integrated into teaching-learning materials and classroom practices and discusses the effectiveness and consequences of the different solutions, where tool-centred approaches are compared to problem-focused solutions.

Subject Classification: 94-01

References
  1. Abonyi-Tóth, A., Farkas, Cs., & Varga, P. (2022a). Digitális kultúra 7. [Digital Culture 7]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG07TA_teljes.pdf
  2. Abonyi-Tóth, A., Farkas, Cs., & Varga, P. (2023). Digitális kultúra 8. [Digital Culture 8]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG08TA_teljes.pdf
  3. Abonyi-Tóth, A., Farkas, Cs., Fodor, Zs., Jeneiné Horváth, K., Reményi, Z., Siegler, G., & Varga, P. (2022b). Digitális kultúra 11. [Digital Culture 11]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG11TA_teljes.pdf
  4. Abonyi-Tóth, A., Farkas, Cs., Reményi, Z., & Jeneiné Horváth, K. (2021b). Digitális kultúra 10. [Digital Culture 10]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG10TA_teljes.pdf
  5. Abonyi-Tóth, A., Farkas, Cs., Turzó-Sovák, N., & Varga, P. (2021a). Digitális kultúra 6. [Digital Culture 6]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG06TA_teljes.pdf
  6. Baranyi, P., & Gilányi, A. (2013). Mathability: Emulating and enhancing human mathematical capabilities. In 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom) (Budapest, Hungary) (pp. 555–558). IEEE. https://doi.org/10.1109/coginfocom.2013.6719309
  7. Baranyi, P., Csapó, Á., & Sallai, Gy. (2015). Cognitive Infocommunications (CogInfoCom). Springer Cham. https://doi.org/10.1109/coginfocom36872.2015
  8. Ben-Ari, M. (1999). Bricolage Forever! In Psychology of Programming Interest Group, PPIG 1999 – 11th Annual Workshop Proceedings (pp. 53–57). University of Leeds.
  9. Ben-Ari, M., & Yeshno, T. (2006). Conceptual models of software artifacts. Interacting with Computers, 18 (6), 1336–1350. https://doi.org/10.1016/j.intcom.2006.03.005
  10. Caspersen, M. E., Diethelm, I., Gal-Ezer, J., McGettrick, A., Nardelli, E., Passey, D., Rovan, B., & Webb, M. (2022). Informatics Reference Framework for School. informatics for all. https://www.informaticsforall.org/wp-content/uploads/2022/03/Informatics-Reference-Framework-for-School-release-February-2022.pdf
  11. Csernoch, M, & Biró, P. (2015). Wasting human and computer resources. International Journal of Social, Behavioral, Educational, Economics and Management Engineering, 9 (2), 573–581.
  12. Csernoch, M. (2009). Teaching word processing – the theory behind. Teaching Mathematics and Computer Science, 7 (1), 119–137. https://doi.org/10.5485/tmcs.2009.0212
  13. Csernoch, M. (2010). Teaching word processing – the practice. Teaching Mathematics and Computer Science, 8 (2), 247–262. https://doi.org/10.5485/tmcs.2010.0252
  14. Csernoch, M. (2017). Thinking fast and slow in computer problem solving. Journal of Software Engineering and Applications, 10 (1), 11–40. https://doi.org/10.4236/jsea.2017.101002
  15. Csernoch, M., Máth, J., & Nagy, T. (2022). The interpretation of graphical information in word processing. Entropy (Basel), 24 (10), 1492. https://doi.org/10.3390/e24101492
  16. Csernoch, M., Nagy, K., & Nagy, T. (2023). The entropy of digital texts – the mathematical background of correctness. Entropy, 25 (2), 302. https://doi.org/10.3390/e25020302
  17. Csernoch, M., Nagy, T., Nagy, K., Csernoch, J., & Hannusch, C. (2024). Humancentered digital sustainability: Handling enumerated lists in digital texts. In IEEE Access, Volume 12, 30544–30561. https://doi.org/10.1109/access.2024.3369587
  18. Csernoch, Mária. (2019). Do you speak and write in Informatics? In Proceedings of IMCIC 2019, the 10th International Multi-Conference on Complexity, Informatics and Cybernetics (Orlando, Florida) (pp. 147–152). International Institute of Informatics and Cybernetics.
  19. Hattie, J. (2012). Visible learning for teachers: Maximizing Impact on learning. Routledge/Taylor & Francis Group.
  20. Kahneman, D. (2011). Thinking, fast and slow. Penguin Books.
  21. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41 (2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
  22. Krafcik, J. F. (1988). Triumph of the lean production system. Sloan Management Review, 30 (1), 41–52.
  23. Lénárd, A., Abonyi-Tóth, A., Turzó-Sovák, N., & Varga, P. (2020). Digitális kultúra 5. [Digital Culture 5]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG05TA_teljes.pdf
  24. Lénárd, A., Sarbó, Gy., Tarné Éder, M., & Turzó-Sovák, N. (2022). Digitális kultúra 3. [Digital Culture 3]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG03TA_teljes.pdf
  25. Lénárd, A., Turzó-Sovák, N., Tarné Éder, M., & Sarbó, Gy. (2023). Digitális kultúra 4. [Digital Culture 4]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG04TA_teljes.pdf
  26. Lister, R. (2008). After the gold rush: Toward sustainable scholarship in computing. In Proceedings of ACE2008, the Tenth Australasian Computing Education Conference (Wollongong, Australia) (Vol. 78, (pp. 3–17). Australian Computer Society.
  27. Malmi, L., Sheard, J., Kinnunen, P., Simon, & Sinclair, J. (2019). Computing education theories: What are they and how are they used? in Proceedings of the 2019 ACM Conference on International Computing Education Research (Toronto ON, Canada) (pp. 187–197). ACM (Association for Computing Machinery). https://doi.org/10.1145/3291279.3339409
  28. McLaren, B. M., Adams, D., Durkin, K., Goguadze, G., Mayer, R. E., Rittle-Johnson, B., Sosnovsky, S., Isotani, S., & van Velsen, M. (2012). To err is human, to explain and correct is divine: A study of interactive erroneous examples with middle school Math students. In A Ravenscroft et al. (Eds.), 21st Century Learning for 21st Century Skills (EC-TEL 2012), Lecture Notes in Computer Science, Volume 7563 (pp. 222–235). Springer. https://doi.org/10.1007/978-3-642-33263-0_18
  29. Metcalfe, J. (2017). Learning from errors. Annual Review of Psychology, 68, 465–489. https://doi.org/10.1146/annurev-psych-010416-044022
  30. Modig, N., & Ahlström, P. (2018). This is lean – Resolving the efficiency paradox. Rheologica Publishing.
  31. Nagy, K., & Csernoch, M. (2023). Pre-testing erroneous text-based documents: Logging end-user activities. Frontiers in Education, 7, pp. 1–11. https://doi.org/10.3389/feduc.2022.958635
  32. NBC2012 (2012). Nemzeti alaptanterv [National Base Curriculum], 110/2012. (VI. 4.) Korm. rendelete a Nemzeti alaptanterv kiadásáról, bevezetéséről és alkalmazásáról. http://ofi.hu/sites/default/files/attachments/mk_nat_20121.pdf
  33. NBC2020 (2020). Nemzeti alaptanterv [National Base Curriculum], A Nemzeti alaptanterv kiadásáról, bevezetéséről és alkalmazásáról szóló 110/2012. (VI. 4.) Korm. rendelet módosításáról (pp. 290–446). Retrieved March 22, Computer cooking vs. problem solving 57 2024. https://magyarkozlony.hu/dokumentumok/3288b6548a740b9c8daf918a399a0bed1985db0f/letoltes (Accessed April 11, 2022)
  34. OFI (2020a). Digitális Kultúra. Kerettanterv az általános iskola 1–4. évfolyama számára [Frame Curricula for Digital Culture 1–4]. https://www.oktatas.hu/pub_bin/dload/kozoktatas/kerettanterv/Digitalis_kultura_A.docx (Accessed April 11, 2022)
  35. OFI (2020b). Digitális Kultúra. Kerettanterv az általános iskola 5–8. évfolyama számára [Frame Curricula for Digital Culture 5–8]. https://www.oktatas.hu/pub_bin/dload/kozoktatas/kerettanterv/Digitalis_kultura_F.docx (Accessed April 11, 2022)
  36. OFI (2020c). Digitális Kultúra. Kerettanterv a gimnáziumok 9–12. évfolyama számára [Frame Curricula for Digital Culture 9–12]. https://www.oktatas.hu/pub_bin/dload/kozoktatas/kerettanterv/Digitalis_kultura_K.docx (Accessed April 11, 2022)
  37. OFI (2020d). Közismereti érettségi vizsgatárgyak 2024. május-júniusi vizsgaidőszaktól érvényes vizsgakövetelményei (2020-as NAT-ra épülő vizsgakövetelmények). Részletes vizsgakövetelmények és vizsgaleírások. https://www.oktatas.hu/pub_bin/dload/kozoktatas/erettsegi/vizsgakovetelmenyek2024/dig_kult_2024_e.pdf
  38. Ohno, T. (1988). Toyota production system: Beyond large-scale production. Productivity Press.
  39. Polya, G. (1945). How to solve it. Princeton University Press.
  40. Rattenbury, T., Hellerstein, J., Heer, J., Kandel, S., & Carreras, C. (2017). Principles of data wrangling: Practical techniques for data preparation. O’Reilly Media.
  41. Sebestyén, K., Csapó, G., Csernoch, M., & Aradi, B. (2022). Error recognition model: High-mathability end-user text management. Acta Polytechnica Hungarica, 19 (1), 151–170. https://doi.org/10.12700/aph.19.1.2022.1.10
  42. Skemp, R. R. (1971). The psychology of learning mathematics. Penguin Books.
  43. Smalley, A. (2018). Four types of problems – from reactive troubleshooting to creative innovation. Lean Enterprise Institute.
  44. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory in perspective. In Cognitive load theory (pp. 237–242). https://doi.org/10.1007/978-1-4419-8126-4_18
  45. Sweller, J., Kirschner, P. A., & Clark, R. E. (2007). Why minimally guided teaching techniques do not work: A reply to commentaries. Educational Psychologist, 42 (2), 115–121. https://doi.org/10.1080/00461520701263426
  46. Tulis, M., Steuer, G., & Dresel, M. (2016). Learning from errors: A model of individual processes. Frontline Learning Research, 4 (2), Special Issue, 12–26. https://doi.org/10.14786/flr.v4i2.168
  47. Varga, P., Jeneiné Horváth, K., Reményi, Z., Farkas, Cs., Takács, I., Siegler, G., & Abonyi-Tóth, A. (2020). Digitális kultúra 9. [Digital Culture 9]. Oktatási Hivatal. https://www.tankonyvkatalogus.hu/storage/pdf/OH-DIG09TA teljes.pdf
  48. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49 (3), 33–35. https://doi.org/10.1145/1118178.1118215
  49. Wolfram, C. (2015, May 22). Evidence: let’s promote not stifle innovation in education. conradwolfram.com. https://www.conradwolfram.com/writings/2015/5/21/role-of-evidence-in-education-innovation
  50. Wolfram, C. (2020). The Math(s) FIX: An education blueprint for the AI age. Wolfram Media.
  51. Womack, J. P., & Jones, D. T. (2003). Lean thinking: Banish waste and create wealth in your corporation. Simon & Schuster.
Most read articles by the same author(s)