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Python Programming Topics That Pose a Challenge for Students

Authors Justyna Szydłowska , Filip Miernik , Marzena Sylwia Ignasiak , Jakub Swacha

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Author Details

Justyna Szydłowska
  • University of Szczecin, Poland
Filip Miernik
  • University of Szczecin, Poland
Marzena Sylwia Ignasiak
  • University of Szczecin, Poland
Jakub Swacha
  • University of Szczecin, Poland

Funding

The work described in this paper was achieved within two projects supported by the European Union’s Erasmus Plus programme: the Framework for Gamified Programming Education (2018-1-PL01-KA203-050803) and FGPE Plus: Learning tools interoperability for gamified programming education (2020-1-PL01-KA226-HE-095786).

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Justyna Szydłowska, Filip Miernik, Marzena Sylwia Ignasiak, and Jakub Swacha. Python Programming Topics That Pose a Challenge for Students. In Third International Computer Programming Education Conference (ICPEC 2022). Open Access Series in Informatics (OASIcs), Volume 102, pp. 7:1-7:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/OASIcs.ICPEC.2022.7

Abstract

Learning programming is often considered as difficult, but it would be wrong to assume that all programming topics are equally tough to learn. In this paper, we make use of a gamified programming learning environment submission repository containing records of over 9000 attempts of solving Python exercises to identify topics which pose the largest challenge for students. By comparing students' effort and progress among sets of exercises assigned to respective topics, two topics emerged as especially difficult (Object-oriented programming and Classic algorithms). Also interesting are the identified differences between genders (indicating female students to fare better than male at the initial topics, and the opposite for the most advanced topics), and the scale of effort some students put to succeed with the most difficult exercises (sometimes solved only after tens of failed attempts).

Subject Classification

ACM Subject Classification
  • Applied computing → Interactive learning environments
  • Applied computing → E-learning
Keywords
  • learning programming
  • programming exercises
  • gamified learning environment
  • learning Python

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