A Stduy on Learning Model for Effective Coding Education

효과적인 코딩교육을 위한 학습 모델에 대한 연구

  • Kim, Si-Jung (Talmage Liberal Arts College, Hannam University) ;
  • Cho, Do-Eun (Division of Information and Communication Convergence Engineering, Mokwon University)
  • 김시정 (한남대학교 탈메이지교양교육대학) ;
  • 조도은 (목원대학교 정보통신융합공학부)
  • Received : 2017.12.13
  • Accepted : 2018.02.20
  • Published : 2018.02.28


With our society entering the Fourth Industrial Revolution, there has been heightened interest in coding education, which has led to an increased number of coding classes offered in schools. Once catered to degree holders only, coding courses are now being offered as liberal arts courses to even non-majors. As the importance of computing abilities and creativity-oriented education through software learning becomes increasingly pronounced, the need for research on effective coding learning is growing more urgent. The present study sought an effective coding education model that would encourage and enhance learners' participation and interest in coding. The proposed learning model is designed to invoke learner's recognition of various coding grammars and data search in the process of designing and performing their own unique project. Application of the proposed learning model and analysis of such case studies showed improvement in learning outcomes. One can expect improved performance among learners if the proposed learning model is applied to various coding courses.


  1. S. H. Park. (2016). Study of SW Education in University to enhance Computational Thinking. Journal of Digital Convergence, 14(4), 1-10. DOI : 10.14400/JDC.2016.14.4.1
  2. S. H. Kim. (2015). Analysis of Non Computer Majors Difficulties in Computational Thinking Education. The Journal of Korean association of computer education, 18(3), 49.
  3. J. M. Wing. (2006). Computational thinking. Computations of the ACM, 49(3), 33-35. DOI : 10.1145/1118178.1118215
  4. P. J. Denning. (2010). The Great Principles of Computing. The Scientific Research Society 98(5), 369-372. DOI : 10.1145/948383.948400
  5. H. S. Yeo & Y. T. Park. (2017). The Effects of Flipped Learning and Mind-Wandering on Idea Generation : Focusing on the use of SIT & BCC. Journal of Engineering Education Research, 20(5), 23-33.
  6. K. H, Park, C. H, Park, W. J, Chung & C. J, Yoo. (2010). Implementation of a problem-based learning program combined with team-based learning. Korean Journal of Medical Education 22(3), 225-230. DOI : 10.3946/kjme.2010.22.3.225
  7. T. Jenkins. (2002). On the difficulty of learning to program. In Proceedings of the 3rd Annual Conference of the LTSN Centre for Information and Computer Sciences, 4(2002), 53-58. DOI : 10.3946/kjme.2010.22.3.225
  9. Y. J. Park. (2017). A Theoretical Exploration of Pedagogical Meaning of Flipped Learning from the Perspective of Dialogism. Journal of the Korea Convergence Society, 8(1), 173-179
  10. Y. S. Lee & Y. E. Lee. (2016). The Effect of the Flipped Learning on Self-efficacy, Critical Thinking Disposition, and Communication Competence of Nursing Students. Journal of Korean Academic Society of Nursing Education, 22(4), 567-576. DOI : 10.5977/jkasne.2016.22.4.567
  11. S. Y. Pi. (2016). A Study on Coding Education of Non-Computer Majors for IT Convergence Education. Journal of Digital Convergence, 14(10), 1-8.
  12. Y. S. Son & K. J. Lee. (2016). Computational Thinking Teaching Model Design for Activating IT Convergence Education. Journal of the Korea institute of electronic communication sciences, 11(5), 511-522. DOI : 10.13067/JKIECS.2016.11.5.511
  13. B. C. Czerkawski & E. W. Lyman. (2015). Exploring issues about computational thinking in higher education, Tech Trends, 59(2), 57.
  14. S. H. Park. (2015). The Effectiveness of Learning Community for the Development of Convergence of University Students. Journal of Digital Convergence, 13(9), 29-37.
  15. S. J. Lee, & K. H. Kim. (2017). The effect of college students' individual traits on learning activity participation. Journal of the Korea Convergence Society, 8(11), 249-256.