Journal of Industrial Convergence (산업융합연구)

DAEHAN Society of Industrial Management (대한산업경영학회)
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The Meta-Analysis on Effects of Education of Python for Elementary School Students

초등학생 대상 파이썬(Python) 활용 교육의 효과에 대한 메타분석

  • Yoon, So Hee (College of Basic and General Education, Dongshin University) ;
  • Jang, Bong Seok (Department of Education, Mokpo National University)
  • 윤소희 (동신대학교 기초교양대학) ;
  • 장봉석 (국립목포대학교 교육학과)
  • Received : 2020.09.22
  • Accepted : 2020.10.20
  • Published : 2020.10.31
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Abstract

This study intended to analyze effects of education of python through meta-analysis. The researcher selected five primary studies reporting statistical data after implementing education of python in elementary classroom settings. Three research questions were stated. What is the total effect size of education of python? What are effect sizes of publication type, dependent variable, and etc.? What are results of meta-regression analysis by grade level, period, and etc.? Findings are as follows. The overall effect size was .598, which is medium. For categorical variables, the effect size of peer-reviewed journal articles was larger than theses. The effect size of affective domain was larger than student achievement and cognitive domain. For meta-regression analysis, education of python was more effective as the period and duration of the program increased. Finally, discussions and recommendations including qualitative investigation on affective domain and program management considering characteristics were presented regarding research findings.

이 연구는 초등학생 대상 파이썬 활용 교육의 효과를 메타분석을 통해 분석하기 위해 실시되었다. 연구를 위해 파이썬을 활용하여 교육을 실시한 후 효과에 관해 보고한 선행연구 논문 5편을 선정하여 분석하였다. 설정된 연구문제는 다음과 같다. 전체 효과크기는 무엇인가? 출판 여부, 종속변인 유형 등의 범주형 변인에 따른 효과크기는 무엇인가? 학년, 운영기간 등의 연속형 변인에 따른 효과크기는 무엇인가? 연구 결과로써 파이썬 활용 교육의 전체 효과크기는 중간 효과인 .598로 나타났다. 범주형 변수에 따라, 출판된 연구물의 효과크기가 학위 논문보다, 정의적 영역의 효과크기가 학업성취도, 인지적 영역 보다 효과크기가 큰 것으로 나타났다. 메타회귀분석 결과 교육 운영기간이 길수록, 프로그램 운영시간이 클수록 더 큰 효과가 나타났다. 마지막으로 논의 및 정의적 영역에 대한 질적 탐색, 프로그램 특징을 고려한 운영 등을 중심으로 제언을 제시하였다.

Keywords

References

  1. Sharp, J. H. (2019). Using Codecademy Interactive Lessons as an Instructional Supplement in a Python Programming Course. Information Systems Education Journal, 17(3), 20-28.
  2. Department for Education. (2015). 2015 National revised curriculum. Sejong; Department for Education.
  3. Department for Education. (2013). National curriculum in England: computing programmes of study. UK Department for Education.
  4. Lutz, M. (2013). Learning Python. O'Reilly Media.
  5. Slatkin, B. (2019). Effective Python: 90 Specific Ways to Write Better Python. Addison-Wesley Professional.
  6. Lubanovic, B. (2019). Introducing Python: Modern Computing in Simple Packages. O'Reilly Media.
  7. Deitel, P. & Deitel, H. (2019). Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud. Pearson.
  8. Cooper, H. M. (2009). Research synthesis and meta-analysis: A step by step approach (4th ed.). Thousand Oaks, CA: Sage.
  9. Cooper, H. M., Hedges, L. V. & Valentine, J. (2008). The handbook of research synthesis and meta-analysis. NY: Russell Sage Foundation.
  10. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  11. Hedge, L. V. & Olkin, I. (1985). Statistical Methods for Meta-analysis. San Diego, CA: Academic Press.