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http://dx.doi.org/10.14400/JDC.2019.17.8.095

Content Recommendation Techniques for Personalized Software Education  

Kim, Wan-Seop (Baird University College, Soongsil University)
Publication Information
Journal of Digital Convergence / v.17, no.8, 2019 , pp. 95-104 More about this Journal
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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Times Cited By KSCI : 12  (Citation Analysis)
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