Articles

Removing the burden of syntax: developing computational thinking and algorithmic skills of STEM students

Published:
2026-06-04
Authors
View
Keywords
License

Copyright (c) 2026 Ádám Gulácsi, Maria Csernoch

Creative Commons License

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

How To Cite
Selected Style: APA
Gulácsi, Á., & Csernoch, M. (2026). Removing the burden of syntax: developing computational thinking and algorithmic skills of STEM students. Teaching Mathematics and Computer Science, 24(1), 29-49. https://doi.org/10.5485/TMCS.2026.15760
Abstract

In higher education, solving programming exercises using a high-level programming language is a standard approach for developing computational thinking and algorithmic skills. However, this method has its limitations: learning the syntax of a high-level programming language puts an extra cognitive load on students, preventing them from focusing on problem-solving. Furthermore, computational thinking is not limited to programming: STEM students can benefit more from solving problems within their own discipline, in different environments. This practical article proposes a collection of unplugged, semi-unplugged and plugged-in alternatives that can be used to develop the computational thinking and algorithmic skills of students.

Subject Classification: 97P99

References
  1. Abonyi-Tóth, A., Farkas, Cs., Jeneiné Horváth K., Reményi Z., Siegler G., Takács I., Varga P. (2020). Digitális kultúra tankönyv 9. [Digital Culture 9]. Oktatási Hivatal.
  2. AlgoRythmics (2024). AlgoRythmics – YouTube. Retrieved December 14, 2024. https://www.youtube.com/@AlgoRythmics
  3. Bell, T., & Newton, H. (2013). Unplugging computer science. In D. M. Kadijevich, C. Angeli, & C. Schulte (Eds.). Improving computer science education (pp. 75–90), Routledge.
  4. Bell, T., & Vahrenhold, J. (2018). CS unplugged – how is it used, and does it work? In H. J. Böckenhauer, D. Komm, & W. Unger, Adventures between lower bounds and higher altitudes: essays dedicated to Juraj Hromkovič on the occasion of his 60th birthday (pp. 497–521). Springer.
  5. Biró, P., & Csernoch, M. (2017). Unplugged tools for building algorithms with Sprego. In C. Mafalda. Education and New Developments (END 2017) (pp. 401–405). InScience Press.
  6. Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87 (4), 834–860. https://doi.org/10.3102/0034654317710096
  7. Cook, D. (2024). Flowgorithm – Flowchart Programming Language. Retrieved December 14, 2024. http://flowgorithm.org/
  8. Csapó, G., & Sebestyén, K. (2017). Educational software for the Sprego method. Turkish Online Journal of Educational Technology, Special Issue for INTE 2017, October 2017, 986–999. http://www.tojet.net/special/2017_10_1.pdf
  9. Csapó, G., Csernoch, M., & Abari, K. (2020a). Sprego: Case study on the effectiveness of teaching spreadsheet management with schema construction. Education and Information Technologies, 25 (3), 1585–1605. https://doi.org/10.1007/s10639-019-10024-2
  10. Csapó, G., Sebestyén, K., Csernoch, M., & Abari, K. (2020b). Case study: Developing long-term knowledge with Sprego. Education and Information Technologies, 26 (1), 965–982. https://doi.org/10.1007/s10639-020-10295-0
  11. Csernoch, M. (2014). Programozás táblázatkezelő függvényekkel – Sprego: Táblázatkezelés csupán egy tucat függvénnyel [Programming with spreadsheet functions – Sprego]. Műszaki Könyvkiadó.
  12. Csernoch M., & Biró P. (2015). Sprego programming. Spreadsheets in Education (eJSiE), 8 (1), 40 pp.
  13. Csernoch, M., Biró, P., Máth, J., & Abari, K. (2015). Testing algorithmic skills in traditional and non-traditional programming environments. Informatics in Education, 14 (2), 175–197. https://doi.org/10.15388/infedu.2015.11
  14. Csernoch, M. (2017). Thinking fast and slow in computer problem solving. Journal of Software Engineering and Applications, 10 (1), 11–40.
  15. Csernoch, M., Nagy, K., & Nagy, T. (2023). The entropy of digital texts – the mathematical background of correctness. Entropy, 25 (2), Art. ID 302, 34 pp.
  16. Csernoch, M., Hannusch, C., & Biró, P. (2024). Modification of erroneous and correct digital texts. Entropy, 26 (12), Art. ID 1015, 21 pp.
  17. Csernoch, M. (2025). Lean digital education to resolve the paradox of the illusion of digital prosperity. Journal of Innovation & Knowledge, 10 (2), Art. ID 100676, 27 pp.
  18. Dowek, G., Archambault, J.-P., Baccelli, E., Cimelli, C., Cohen, A., Eisenbeis, C., Viéville, T., Wack, B., & Berry, G. (2013). Informatique et sciences du numérique: Spécialité ISN en terminale S: Avec des exercices corrigés et idées de projets. Eyrolles.
  19. Gulácsi Á., Dienes N., & Csernoch M. (2019). Sprego toolbox: A way to teach spreadsheeting meaningfully. Turkish Online Journal of Educational Technology, Special Issue for IETC-ITEC-INTE (Vol. 2), December 2019, 296–302.
  20. Guzdial, M., & Soloway, E. (2002). Teaching the Nintendo generation to program. Communications of the ACM, 45 (4), 17–21. https://doi.org/10.1145/505248.505261
  21. Halim, S. (2024). VisuAlgo – visualising data structures and algorithms through animation. Retrieved December 14, 2024. https://visualgo.net/en
  22. Kahneman, D. (2011). Thinking, fast and slow. Penguin Books.
  23. Kátai, Z., Osztián, P.-R., & Iclanzan, D. (2024). Enacting algorithms: Evolution of the AlgoRythmics storytelling. Education and Information Technologies, 29, 19197–19228.
  24. Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142. https://doi.org/10.1016/j.tate.2017.06.001
  25. 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
  26. Lister, R. (2008). After the Gold Rush: Toward sustainable scholarship in computing. In S. Hamilton, & M. Hamilton (Eds.), Proceedings of the Tenth Australasian Computing Education Conference (ACE’08) (pp. 3–17). Australian Computer Society.
  27. Lister, R., Simon, B., Thompson, E., Whalley, J. L., & Prasad, C. (2006). Not seeing the forest for the trees: Novice programmers and the SOLO taxonomy. ACM SIGCSE Bulletin, 38 (3), 118–122. https://doi.org/10.1145/1140123.1140157
  28. Ministry of Justice (Ed.). (2020). Magyar Közlöny, 2020 (17). Retrieved December 14, 2024. https://magyarkozlony.hu/dokumentumok/3288b6548a740b9c8daf918a399a0bed1985db0f/letoltes
  29. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
  30. Pólya, G. (1957). How to solve it. Doubleday.
  31. Reynolds, G. (2008). Presentation zen: Simple ideas on presentation design and delivery. New Riders Pub.
  32. Sebestyén, K., Csapó, G., & Csernoch, M. (2018). Visualising Sprego inequality problems with 2D representations. Turkish Online Journal of Educational Technology, Special Issue for INTE-ITICAM-IDEC (Vol. 2), November 2018, 888–898. http://www.tojet.net/special/2018_12_3.pdf
  33. Sebestény, K., Csapó, G., Csernoch, M., & Aradi, B. (2022). Error recognition model: High-mathability end-user text management. Acta Polytechnica Hungarica, 19 (1), 151–170.
  34. Soloway, E. (1993). Should we teach students to program? Communications of the ACM, 36 (10), 21–24. https://doi.org/10.1145/163430.164061
  35. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.
  36. Takács, R., T. Kárász, J., Takács, Sz., Horváth, Z., & Oláh, A. (2022). Successful steps in higher education to stop computer science students from attrition. Interchange, 53, 637–652. https://doi.org/10.1007/s10780-022-09476-2
  37. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49 (3), 33–35. https://doi.org/10.1145/1118178.1118215
  38. Wing, J. M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25 (2), 7–14. https://doi.org/10.17471/2499-4324/922
  39. Wolfram, C. (2020). The math(s) fix: An education blueprint for the AI age. Wolfram Media.
Database Logos

Keywords