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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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