KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Comparative Study on Game-Score Prediction Models Using Compuational Thinking Education Game Data

컴퓨팅 사고 교육 게임 데이터를 사용한 게임 점수 예측 모델 성능 비교 연구

  • Received : 2021.10.12
  • Accepted : 2021.10.27
  • Published : 2021.11.30
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Abstract

Computing thinking is regarded as one of the important skills required in the 21st century, and many countries have introduced and implemented computing thinking training courses. Among computational thinking education methods, educational game-based methods increase student participation and motivation, and increase access to computational thinking. Autothinking is an educational game developed for the purpose of providing computational thinking education to learners. It is an adaptive system that dynamically provides feedback to learners and automatically adjusts the difficulty according to the learner's computational thinking ability. However, because the game was designed based on rules, it cannot intelligently consider the computational thinking of learners or give feedback. In this study, game data collected through Autothikning is introduced, and game score prediction that reflects computational thinking is performed in order to increase the adaptability of the game by using it. To solve this problem, a comparative study was conducted on linear regression, decision tree, random forest, and support vector machine algorithms, which are most commonly used in regression problems. As a result of the study, the linear regression method showed the best performance in predicting game scores.

컴퓨팅 사고는 21세기에 필요한 중요한 소양 중 하나로 여겨지면서 여러 국가에서 컴퓨팅 사고 교육 과정을 도입하여 시행하고 있다. 컴퓨팅 사고 교육 방법 중 교육용 게임 기반 방법은 학생들의 참여와 동기를 증대시키고 컴퓨팅 사고에 대한 접근성을 높여준다. Autothinking은 학습자들에게 컴퓨팅 사고 교육을 제공하기 위한 목적으로 개발한 교육용 게임으로 학습자들에게 동적으로 피드백을 제공하고, 학습자의 컴퓨팅 사고 능력에 따라서 난이도를 자동으로 조절하는 적응적 시스템이다. 하지만 규칙기반으로 게임을 디자인하여 지능적으로 학습자들의 컴퓨팅 사고를 고려하거나 피드백을 주지 못한다. 본 연구에서는 Autothikning을 통해 수집한 게임 데이터를 소개하고, 이를 활용하여 해당 게임의 적응성을 높이기 위해 컴퓨팅 사고를 반영하는 게임 점수의 예측을 수행한다. 이 문제를 해결하기 위해 회귀 문제에 가장 많이 사용되는 선형 회귀, 결정 트리, 렌덤 포레스트, 서포트 벡터 머신 알고리즘에 대한 비교연구를 수행하였다. 연구 수행결과 선형회귀 방법이 게임 점수 예측에 가장 좋은 성능을 보여주었다.

Keywords

Acknowledgement

이 논문(작품)은 한신대학교 학술연구비 지원에 의하여 연구(창작)되었음.

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