Open Access Peer-reviewed Research Article

Students' Pre-Instruction Programming Perceptions in Upper-Secondary School: Findings from a Diagnostic Pilot

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Item-Level Accuracy
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Submitted   Nov 23, 2025
Published   Feb 26, 2026
Issue    Vol 6 No 1 (2026)
DOI   10.25082/AMLER.2026.01.010

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Sofia Kasotaki corresponding author
Department of Informatics, School of Sciences, University of Western Macedonia, Kastoria, Greece

Abstract

 Understanding students' initial conceptions of programming can help teachers prioritize core ideas and anticipate misconceptions before instruction begins. This pilot study reports on a pre-instruction diagnostic administered to Grade 11 students in one upper-secondary school. The instrument, composed of multiple-choice and True/False items aligned with five conceptual clusters (definition of a program, language recognition, variables and data, basic conditionals, and elementary loop semantics), was designed to reveal common novice difficulties documented in the literature. Analyses of students' responses indicated partial familiarity with simple control constructs but persistent weaknesses in foundational areas, including distinguishing a program from an algorithm, understanding variables as memory locations, and recognizing the role of guard change in loop termination. A consistent format effect favored recognition-based True/False items over multiple-choice discrimination, suggesting that early instruction should bridge from recognition to explanation and short code construction. Although limited by its single-site scope, the pilot provides a practical baseline for refining diagnostic tools and informing initial instructional sequencing in upper-secondary programming.

Keywords
pre‑instruction diagnostics, novice misconceptions, introductory programming, upper‑secondary education, computing education

Article Details

How to Cite
Kasotaki, S. (2026). Students’ Pre-Instruction Programming Perceptions in Upper-Secondary School: Findings from a Diagnostic Pilot. Advances in Mobile Learning Educational Research, 6(1), 1759-1767. https://doi.org/10.25082/AMLER.2026.01.010
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References

  1. Ali, M., Ghosh, S., Rao, P., Dhegaskar, R., Jawort, S., Medler, A., Shi, M., & Dasgupta, S. (2023). Taking Stock of Concept Inventories in Computing Education: A Systematic Literature Review. Proceedings of the 2023 ACM Conference on International Computing Education Research V.1, 397–415. https://doi.org/10.1145/3568813.3600120
  2. Altadmri, A., & Brown, N. C. C. (2015). 37 Million Compilations. Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 522–527. https://doi.org/10.1145/2676723.2677258
  3. Brown, N. C. C., & Altadmri, A. (2014). Investigating novice programming mistakes. Proceedings of the Tenth Annual Conference on International Computing Education Research, 43–50. https://doi.org/10.1145/2632320.2632343
  4. European Schoolnet. (2021). Supporting digital education in Greece: Towards a digitally enhanced educational ecosystem. European Schoolnet.
  5. Eurydice. (2025). Teaching and learning in general upper secondary education (Greece). Education, Audiovisual and Culture Executive Agency.
  6. Kalogiannakis, M., & Papadakis, S. (2019). Pre-service kindergarten teachers’ acceptance of “ScratchJr” as a tool for learning and teaching computational thinking and science education. The Journal of Emergent Science, 15, 31–34.
  7. Kecskemety, K., Barach, A., Jenkins, C., & Gunawardena, S. (n.d.). Using Programming Concept Inventory Assessments: Findings in a First-year Engineering Course. 2021 ASEE Virtual Annual Conference Content Access Proceedings. https://doi.org/10.18260/1-2-37995
  8. Lavidas, K., Petropoulou, A., Papadakis, S., Apostolou, Z., Komis, V., Jimoyiannis, A., & Gialamas, V. (2022). Factors Affecting Response Rates of the Web Survey with Teachers. Computers, 11(9), 127. https://doi.org/10.3390/computers11090127
  9. Luxton-Reilly, A., Simon, Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., & Szabo, C. (2018). Introductory programming: a systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 55–106. https://doi.org/10.1145/3293881.3295779
  10. Papadakis, S. (2018a). Gender stereotypes in Greek computer science school textbooks. International Journal of Teaching and Case Studies, 9(1), 48. https://doi.org/10.1504/ijtcs.2018.090196
  11. Papadakis, S. (2018b). Is Pair Programming More Effective than Solo Programming for Secondary Education Novice Programmers? International Journal of Web-Based Learning and Teaching Technologies, 13(1), 1–16. https://doi.org/10.4018/ijwltt.2018010101
  12. Papadakis, S., & Orfanakis, V. (2017). The Combined Use of Lego Mindstorms NXT and App Inventor for Teaching Novice Programmers. Educational Robotics in the Makers Era, 193–204. https://doi.org/10.1007/978-3-319-55553-9_15
  13. Papadakis, S., & Orfanakis, V. (2018). Comparing novice programing environments for use in secondary education: App Inventor for Android vs. Alice. International Journal of Technology Enhanced Learning, 10(1/2), 44. https://doi.org/10.1504/ijtel.2018.088333
  14. Parker, M. C., Davidson, M. J., Kao, Y. S., Margulieux, L. E., Tidler, Z. R., & Vahrenhold, J. (2023). Toward CS1 Content Subscales: A Mixed-Methods Analysis of an Introductory Computing Assessment. Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, 1–13. https://doi.org/10.1145/3631802.3631828
  15. Petousi, V., & Sifaki, E. (2020). Contextualising harm in the framework of research misconduct. Findings from discourse analysis of scientific publications. International Journal of Sustainable Development, 23(3/4), 149. https://doi.org/10.1504/ijsd.2020.115206
  16. Qian, Y., & Lehman, J. (2017). Students’ Misconceptions and Other Difficulties in Introductory Programming. ACM Transactions on Computing Education, 18(1), 1–24. https://doi.org/10.1145/3077618
  17. Qian, Y., & Lehman, J. D. (2019). Using Targeted Feedback to Address Common Student Misconceptions in Introductory Programming: A Data-Driven Approach. Sage Open, 9(4). https://doi.org/10.1177/2158244019885136
  18. Sorva, J. (2013). Notional machines and introductory programming education. ACM Transactions on Computing Education, 13(2), 1–31. https://doi.org/10.1145/2483710.2483713
  19. Wilson, G. (2016, June 9). Novice programming mistakes. It Will Never Work in Theory. https://neverworkintheory.org