From Bebras Tasks to Lesson Plans – Graph Data Structures

  • Lucia BudinskáEmail author
  • Karolína Mayerová
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11913)


In this paper we focused on graph tasks from Slovak Bebras Challenge with the intent to use them as a teaching and learning material. Based on qualitative categorisation of tasks together with quantitative analysis of contestants results we chose three tasks that were the most suitable for lower secondary schools Informatics in Slovakia. We used qualitative research methods to better understand what had caused the most significant problems. Based on these results we have prepared lesson plans with objective to teach pupils to understand, read, edit and to create specific graph structures. Taking Bloom taxonomy into account, worksheets were created and for each learning objective, there is at least one subtask in a worksheet. The main parts of this paper are pre-research and preliminary results of testing worksheets with pupils in the 5th and 6th year. We describe differences between groups based on gender and age. These results help us understand the reasons of contestants’ mistakes in the original tasks and of gender- and grade-specific performance in these tasks. We plan to further develop the lesson plans as we found them valuable not only as a method of research but also as proof that tasks from Bebras Challenge could be used for learning and for teaching.


Graph data structure Graph task Bebras challenge Lesson plans Worksheets Qualitative research 



This research was supported by the Comenius University in Bratislava Grant UK/373/2019.


  1. 1.
    Slovak innovated National Educational Programme in Informatics for lower secondary eduaction Last accessed 25 May 2019
  2. 2.
    Budinská, L., Mayerová, K.: Graph tasks in bebras contest: what does it have to do with gender? In: Proceedings of the 6th Computer Science Education Research Conference, pp. 83–90. ACM (2017)Google Scholar
  3. 3.
    Román-González, M., Moreno-León, J., Robles, G.: Combining assessment tools for a comprehensive evaluation of computational thinking interventions. In: Kong, S.C., Abelson, H. (eds.) Comput. Think. Educ., pp. 79–98. Springer, Singapore (2019). Scholar
  4. 4.
    Dagienė, V., Sentance, S.: It’s computational thinking! bebras tasks in the curriculum. In: Brodnik, A., Tort, F. (eds.) ISSEP 2016. LNCS, vol. 9973, pp. 28–39. Springer, Cham (2016). Scholar
  5. 5.
    Calcagni, A., Lonati, V., Malchiodi, D., Monga, M., Morpurgo, A.: Promoting computational thinking skills: would you use this bebras task? In: Dagiene, V., Hellas, A. (eds.) ISSEP 2017. LNCS, vol. 10696, pp. 102–113. Springer, Cham (2017). Scholar
  6. 6.
    Dagiene, V., Stupuriene, G.: Short Tasks - Big Ideas: Constructive Approach for Learning and Teaching of Informatics Concepts in Primary Education. In: Dagiene, V., Jasute, E. (eds.) Constructionism 2018. Vilius (2018)Google Scholar
  7. 7.
    Anderle, M.: Transformation of tasks from competition to high school lessons - Binary search trees. In: ICERI2018 Proceedings, pp. 6549–6557 (2018)Google Scholar
  8. 8.
    Creswell, J.W.: Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Pearson Education Inc., London (2015)Google Scholar
  9. 9.
    Agresti, A.: Categorical Data Analysis. John Wiley & Sons Inc., Hoboken (2002)CrossRefGoogle Scholar
  10. 10.
    Bloom, B.: Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook 1 Cognitive Domain McKay. New York (1956)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Didactics in Mathematics, Physics and InformaticsComenius UniversityBratislavaSlovakia

Personalised recommendations