Analyzing the Quality of Submissions in Online Programming Courses

Maria Tigina, Anastasiia Birillo, Yaroslav Golubev, Hieke Keuning, Nikolay Vyahhi, Timofey Bryksin

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Programming education should aim to provide students with a broad range of skills that they will later use while developing software. An important aspect in this is their ability to write code that is not only correct but also of high quality. Unfortunately, this is difficult to control in the setting of a massive open online course. In this paper, we carry out an analysis of the code quality of submissions from JetBrains Academy — a platform for studying programming in an industry-like project-based setting with an embedded code quality assessment tool called Hyperstyle. We analyzed more than a million Java submissions and more than 1.3 million Python submissions, studied the most prevalent types of code quality issues and the dynamics of how students fix them. We provide several case studies of different issues, as well as an analysis of why certain issues remain unfixed even after several attempts. Also, we studied abnormally long sequences of submissions, in which students attempted to fix code quality issues after passing the task. Our results point the way towards the improvement of online courses, such as making sure that the task itself does not incentivize students to write code poorly.
Original languageEnglish
Title of host publication2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)
PublisherIEEE
Pages271-282
Number of pages12
ISBN (Electronic)9798350322590
ISBN (Print)979-8-3503-2260-6
DOIs
Publication statusPublished - 20 May 2023
Event2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) - Melbourne, Australia
Duration: 14 May 202320 May 2023

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)
Period14/05/2320/05/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Training
  • Java
  • Codes
  • Electronic learning
  • Software
  • Quality assessment
  • Task analysis
  • learning programming
  • programming education
  • code quality
  • MOOC
  • refactoring
  • large-scale analysis

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