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Generative AI parameter tuning for online self-directed learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(4), pp.31-38
  • DOI : 10.9708/jksci.2024.29.04.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 22, 2024
  • Accepted : March 18, 2024
  • Published : April 30, 2024

Jin-Young Jun 1 Youn-A Min 1

1한양사이버대학교

Accredited

ABSTRACT

This study proposes hyper-parameter settings for developing a generative AI-based learning support tool to facilitate programming education in online distance learning. We implemented an experimental tool that can set research hyper-parameters according to three different learning contexts, and evaluated the quality of responses from the generative AI using the tool. The experiment with the default hyper-parameter settings of the generative AI was used as the control group, and the experiment with the research hyper-parameters was used as the experimental group. The experiment results showed no significant difference between the two groups in the “Learning Support” context. However, in other two contexts (“Code Generation” and “Comment Generation”), it showed the average evaluation scores of the experimental group were found to be 11.6% points and 23% points higher than those of the control group respectively. Lastly, this study also observed that when the expected influence of response on learning motivation was presented in the ‘system content’, responses containing emotional support considering learning emotions were generated.

Citation status

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