Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Clustering Analysis of Science and Engineering College Students' understanding on Probability and Statistics

Robust PCA를 활용한 이공계 대학생의 확률 및 통계 개념 이해도 분석

  • Yoo, Yongseok (Department of Electronics Engineering, Incheon National University)
  • 유용석 (인천대학교 전자공학과)
  • Received : 2022.02.20
  • Accepted : 2022.03.20
  • Published : 2022.03.28
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Abstract

In this study, we propose a method for analyzing students' understanding of probability and statistics in small lectures at universities. A computer-based test for probability and statistics was performed on 95 science and engineering college students. After dividing the students' responses into 7 clusters using the Robust PCA and the Gaussian mixture model, the achievement of each subject was analyzed for each cluster. High-ranking clusters generally showed high achievement on most topics except for statistical estimation, and low-achieving clusters showed strengths and weaknesses on different topics. Compared to the widely used PCA-based dimension reduction followed by clustering analysis, the proposed method showed each group's characteristics more clearly. The characteristics of each cluster can be used to develop an individualized learning strategy.

본 연구에서는 실제 대학의 소규모 강좌에서 확률과 통계에 대한 수강생들의 이해도를 쉽고 빠르게 분석하기 위한 방법을 제안한다. 95명의 이공계 대학생을 대상으로 확률과 통계에 대한 컴퓨터 기반 검사를 시행하였다. 학생들의 응답을 Robust PCA와 가우시안 혼합 모델을 사용하여 7개의 군집으로 나눈 뒤, 각 군집 별로 주제별 성취도를 분석하였다. 상위권 군집은 통계적 추정을 제외한 다른 주제들에 대해서 대체로 높은 성취도를 보였으며, 저성취 군집들은 서로 다른 주제에 대해서 강약점을 보였다. 제안하는 기법은 기존에 널리 쓰이는 PCA를 사용하여 차원 축소 후 군집 분석을 수행한 것 보다 각 군집들의 특성이 더 분명하게 나타냈다. 이는 각 군집 별 특징에 따른 개별화된 학습 전략을 개발하는 데 활용될 수 있다.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1G1A1011136) and Incheon National University Research Grant in 2020.

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