From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education

Filipe Dwan Pereira, Maely Moraes, Marcelo Henklain, Arto Hellas, Elaine Oliveira, Dragan Gasevic, Raimundo Barreto, Rafael Mello

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

Abstract

Online Judges (OJs) have gained substantial traction in programming education due to their ability to simultaneously present problem-solving challenges to students while offering instant feedback and correction. Such technologies are also essential to allow students in remote areas to access quality and equitable education. Nonetheless, OJ systems often lack sufficient amounts of annotated data (i.e., labelled data) about the topics of the problems that they aim to support, which makes choosing appropriate problems hard. Topic annotations hold significant value for instructors when selecting problems for assignments and for novice students seeking independent use of OJ systems. In this work, we propose and evaluate a pre-trained deep learning architecture and an active learning methodology to automatically annotate OJ problems in the context of introductory programming. Our results show that, when using a smaller amount of data, the methodology demonstrates performance comparable to those of the existing state-of-the-art methods for the identical task.

Original languageEnglish
Title of host publication19th European Conference on Technology Enhanced Learning, EC-TEL 2024 Krems, Austria, September 16–20, 2024 Proceedings, Part I
EditorsRafael Ferreira Mello, Nikol Rummel, Ioana Jivet, Gerti Pishtari, José A. Ruipérez Valiente
Place of PublicationCham Switzerland
PublisherSpringer
Pages359-374
Number of pages16
ISBN (Electronic)9783031723155
ISBN (Print)9783031723148
DOIs
Publication statusPublished - 2024
EventEuropean Conference on Technology Enhanced Learning (EC-TEL) 2024 - Krems, Austria
Duration: 16 Sept 202420 Sept 2024
Conference number: 19th
https://link.springer.com/book/10.1007/978-3-031-72315-5 (Proceedings)
https://ea-tel.eu/ectel2024 (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume15159
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Technology Enhanced Learning (EC-TEL) 2024
Abbreviated titleEC-TEL 2024
Country/TerritoryAustria
CityKrems
Period16/09/2420/09/24
Internet address

Keywords

  • Computer Education
  • Educational Text Mining
  • Online Judges
  • Resource-limited

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