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A Study on the Intelligent Online Judging System Using User-Based Collaborative Filtering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(1), pp.273-285
  • DOI : 10.9708/jksci.2024.29.01.273
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 2, 2023
  • Accepted : December 14, 2023
  • Published : January 31, 2024

Hyun Woo Kim 1,  Hye Jin Yun 2,  Kim Kwi-Hoon 2

1송학초등학교
2한국교원대학교

Accredited

ABSTRACT

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

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* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.
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