Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Content Recommendation Techniques for Personalized Software Education

개인화된 소프트웨어 교육을 위한 콘텐츠 추천 기법

  • 김완섭 (숭실대학교 베어드교양대학)
  • Received : 2019.07.02
  • Accepted : 2019.08.20
  • Published : 2019.08.28

Abstract

Recently, software education has been emphasized as a key element of the fourth industrial revolution. Many universities are strengthening the software education for all students according to the needs of the times. The use of online content is an effective way to introduce SW education for all students. However, the provision of uniform online contents has limitations in that it does not consider individual characteristics(major, sw interest, comprehension, interests, etc.) of students. In this study, we propose a recommendation method that utilizes the directional similarity between contents in the boolean view history data environment. We propose a new item-based recommendation formula that uses the confidence value of association rule analysis as the similarity level and apply it to the data of domestic paid contents site. Experimental results show that the recommendation accuracy is improved than when using the traditional collaborative recommendation using cosine or jaccard for similarity measurements.

Keywords

Software education;Coding education for non-major;Personalized recommendation;Collaborative filtering;Item-based recommendation

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References

  1. G. J. Park & Y. J. Choi. (2018). Exploratory study on the direction of software education for the non-major undergraduate students. Journal of Education & Culture, 24(4), 273-292. DOI : 10.24159/joec.2018.24.4.273
  2. K. M. Lee. (2019). Computational Thinking Education Teaching Method Research for Non-Major Subjects. Korean Journal of General Education, 13(1), 321-343.
  3. J. E. Nah. (2017). Software Education Needs Analysis in Liberal Arts. Korean Journal of General Education, 11(3), 63-89.
  4. W. S. Kim. (2017). A Study on establishing software essential courses for non-specialists. The Korean Association of General Education Conference, 110-115.
  5. K. M. Kim, H. S. Kim. (2017). A Study on Customized Software Education method using Flipped Learning in the Digital Age. Journal of Digital Convergence, 15(7), 55-64. DOI : 10.14400/JDC.2017.15.7.55 https://doi.org/10.14400/JDC.2017.15.3.55
  6. Y. J. Ahn. (2015). A Study on The Improvement of Computer Programming Ability for The Learners Participated in Custom Learning Programs. The Korean Society Of Computer And Information Conference, 23(1), 295-296.
  7. S. H. Kim, S. M. Lim & S. S. Song. (2015). Analysis about User Log for Development of Online SW Education Platform in Korea. The Korean Association Of Computer Education Symposium, 63-67.
  8. Wing M. Jeannette. (2006). Computational Thinking, Communications of the ACM, 49(3), 33-35. DOI : 10.1145/1118178.1118215
  9. S. H. Kim, B. H. Oh, M. J. Kim & J. H. Yang. (2012). A Movie Recommendation Algorithm Combining Collaborative Filtering and Content Information. Journal of KISS : Software and Applications, 39(4), 261-268.
  10. S. J. Lee(2018). A New Similarity Measure using Fuzzy Logic for User-based Collaborative Filtering. The Journal of Korean Association of Computer Education, 21(5), 61-68. DOI : 10.32431/kace.2018.21.5.006 https://doi.org/10.32431/kace.2018.21.5.006
  11. Y. S. Kim. (2012). Research Trend of Recommendation System for Personalization Service. ie Magazine, 19(1), 37-42.
  12. B. C. Kim & S. Y. Lee. (2018). Used Textbook Trading Platform to Recommend University Textbooks. Journal of Digital Convergence, 16(4), 329-334. DOI : 10.14400/JDC.2018.16.4.329
  13. S. H. Han, Y. H. Oh & H. J. Kim. (2013). Personalized TV Program Recommendation in VOD Service Platform Using Collaborative Filtering. JOURNAL OF BROADCAST ENGINEERING, 18(1), 88-97. DOI : 10.5909/JBE.2013.18.1.88 https://doi.org/10.5909/JBE.2013.18.1.88
  14. D. K. Kim. (2017). Personal Recommendation Service Design Through Big Data Analysis on Science Technology Information Service Platform. The Korean Biblia Society For Library And Information Science, 28(4), 501-518. DOI : 10.14699/kbiblia.2017.28.4.501
  15. H. J. Han, Y. S. Choi & S. P. Choi. (2018). A Study on Personalization of Science and Technology Information by User Interest Tracking Technique. JOURNAL OF THE KOREAN SOCIETY FOR LIBRARY AND INFORMATION SCIENCE, 52(3), 5-33. DOI : 10.4275/KSLIS.2018.52.3.005
  16. E. M. Sung, Y. J. Chae & S. H. Lee. (2018). Analysis of Types and Characteristics of Self-Directed Learning of Learners in Online Software Education. The Journal of Korean Association of Computer Education, 22(1), 31-46. DOI : 10.32431/kace.2019.22.1.004
  17. Suresh K. Gorakala & Michele Usuelli. (2015). Building a Recommendation System with R, Acorn Publishing.
  18. S. J. Lee. (2016). Collaborative filtering system examples and issues. A Journal of Education, 36(1), 1-22. DOI : 10.25020/je.2016.36.1.1 https://doi.org/10.1016/j.jaccedu.2016.03.003
  19. Y. Kim. (2012). A Study on Design and Implementation of Personalized Information Recommendation System based on Apriori Algorithm. The Korean Biblia Society For Library And Information Science, 23(4), 283-308. https://doi.org/10.14699/kbiblia.2012.23.4.283
  20. J. S. Han. (2013). Intelligent Recommendation Processor Simulation using Association Relationship. Journal of Digital Convergence, 11(12), 431-438. DOI : 10.14400/JDPM.2013.11.12.431 https://doi.org/10.14400/JDPM.2013.11.12.431
  21. S. K. Kim. (2014). A Study of the Personalization Service and Privacy Paradox in the Big Data Era. Journal of the Korean Cadastre Information Association, 16(2), 193-207.
  22. J. W. Han, J. C. Jo & H. S. Lim. (2018). Development of Personalized Learning Course Recommendation Model for ITS. Journal of the Korea Convergence Society, 9(10), 21-28. DOI : 10.15207/JKCS.2018.9.10.021
  23. S. H. Ju, M. Y. Song & B. K. Kim. (2018). The Effect of Personal trait on Perceived Value and Recommendation Intention : Focus on one-person media contents. Journal of the Korea Convergence Society, 9(12), 159-167. DOI : 10.15207/JKCS.2018.9.12.159
  24. S. J. Park, Y. M. Kim & J. J. Ahn. (2019). Development of Product Recommender System using Collaborative Filtering and Stacking Model. Journal of Convergence for Information Technology, 9(6), 83-90. DOI : 10.22156/CS4SMB.2019.9.6.083
  25. J. S. Kang, J. W. Baek & K. Y. Chung. (2019). Multimodal Media Content Classification using Keyword Weighting for Recommendation. Journal of Convergence for Information Technology, 9(5), 1-6. DOI : 10.22156/CS4SMB.2019.9.5.001