Applying Decision Tree Algorithms for Analyzing HS-VOSTS Questionnaire Results

• 발행기관: 한국공학교육학회
• 저자명: Dae-Ki Kang
• 간행물 정보: 『공학교육연구』제15권 제4호, 1~7쪽, 전체 7쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2012.07.31

영문 초록

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students’ survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students’ views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students’ views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students’ survey data.

목차

I. Introduction

II. Decision Tree Induction

III. High School Students Views On ScienceTechnology-Society (HS-VOSTS)

IV. Experiments

V. Related Work

VI. Conclusion and Future Work

키워드

HS-VOSTS Decision tree induction algorithms Science-Technology-Society Questionnaire analysi