Applying decision tree algorithms for analyzing HS-VOSTS questionnaire results

다국어 초록 (Multilingual Abstract)

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.

참고문헌 (Reference)

1 이유종, "정보통신공학 전공 대학생의 정보통신윤리 행동과 관련 변수" 한국공학교육학회 13 (13): 68-77, 2010

2 이유종, "정보통신공학 전공 대학생들의 정보통신산업 종사자 윤리의식 연구" 한국공학교육학회 13 (13): 96-103, 2010

3 김완섭, "의사결정트리를 이용한 교육성과 요인에 관한 연구" 한국공학교육학회 13 (13): 51-59, 2010

4 이용길, "공학 및 사회 계열 대학생들의 STS 소양에 대한 견해 분석" 한국공학교육학회 13 (13): 43-50, 2010

5 Karl Popper, "The logic of scientific discovery" Basic Books 1959

6 Barry Robson, "The engines of Hippocrates: From the Dawn of Medicine to Medical and Pharmaceutical Informatics" John Wiley & Sons 2009

7 C. P. Snow, "The Two Cultures" Cambridge University Press 1959

8 Thomas. S. Kuhn, "The Structure of Scientific Revolutions" Univ. of Chicago Press 1970

9 T. Kuhn, "The Structure of Scientific Revolutions" University of Chicago Press 1962

10 R. P. Feynman, "The Pleasure of Finding Things Out: The Best Short Works of Richard P. Feynman" Basic Books 2000

11 I. Lakatos, "The Methodology of Scientific Research Programmes: Philosophical Papers Volume 1" Cambridge University Press 1978

12 A. Ertas, "The Engineering Design Process" John Wiley & Sons, Inc. 1996

13 Larry Laudan, "The Demise of the Demarcation Problem. In Physics, Philosophy, and Psychoanalysis: Essays in Honor of Adolf Grünbaum" Springer 1983

14 R. Carnap, "The Continuum of Inductive Methods" University of Chicago Press 1952

15 Corinna Cortes, "Support-Vector Networks, In Machine Learning, In Introduction to the Theory of Computation" PWS Pub. Co. 1995

16 W. Bauchspies, "Science, Technology, and Society" A Sociological Approach 2005

17 남정희, "STS에 대한 고등학생들의 견해에 관한 평가도구 개발" 한국과학교육학회 24 (24): 1143-1157, 2004

18 강대기, "Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers" PERGAMON-ELSEVIER SCIENCE LTD 38 (38): 12739-12746, 201109

19 C. Hempel, "Philosophy of Natural Science"

20 Richard O. Duda, "Pattern classification" Wiley 2001

21 강대기, "Learning decision trees with taxonomy of propositionalized attributes" Elsevier B.V. 42 (42): 84-92, 200901

22 "Is See5/C5. 0 Better Than C4. 5?"

23 Ross Quinlan, "Improved use of continuous attributes in C4.5" 4 : 77-90, 1996

24 Srinivas Aluru, "Handbook of Computational Molecular Biology" Chapman & Hall/Crc 2006

25 Jiawei Han, "Data Mining: Concepts and Techniques" 2006

26 K. Popper, "Conjectures and Refutations" The Growth of Scientific Knowledge 1963

27 L. Breiman, "Classification and Regression Trees" Chapman& Hall 1984

28 Judea Pearl, "Causality: Models, Reasoning, and Inference" Cambridge University Press 2000

29 J. S. Lyons, "CQI processes, results, and program improvements for engineering design" 43 (43): 174-181, 2000

30 J. R. Quinlan, "C4.5: Programs for Machine Learning" Morgan Kaufmann Publishers 1993

31 Ross Quinlan, "C4.5: Programs for Machine Learning" Morgan Kaufmann Publishers 1993

32 Christopher M., "Bishop Neural Networks for Pattern Recognition" Oxford: Oxford University Press 1995

33 Judea Pearl, "Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning" 329-334, 1985

34 G. V. Kass, "An exploratory technique for investigating large quantities of categorical data" 29 (29): 119-127, 1980

35 Paul Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge" 1975

36 P. Feyerabend, "Against Method" Outline of an Anarchistic Theory of Knowledge 1975

37 Bernhard E. Boser, "A training algorithm for optimal margin classifiers" 144-152, 1992