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http://dx.doi.org/10.15207/JKCS.2018.9.10.021

Development of Personalized Learning Course Recommendation Model for ITS  

Han, Ji-Won (Dept. of Computer Science and Engineering, Korea University)
Jo, Jae-Choon (Dept. of Computer Science and Engineering, Korea University)
Lim, Heui-Seok (Dept. of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.10, 2018 , pp. 21-28 More about this Journal
Abstract
To help users who are experiencing difficulties finding the right learning course corresponding to their level of proficiency, we developed a recommendation model for personalized learning course for Intelligence Tutoring System(ITS). The Personalized Learning Course Recommendation model for ITS analyzes the learner profile and extracts the keyword by calculating the weight of each word. The similarity of vector between extracted words is measured through the cosine similarity method. Finally, the three courses of top similarity are recommended for learners. To analyze the effects of the recommendation model, we applied the recommendation model to the Women's ability development center. And mean, standard deviation, skewness, and kurtosis values of question items were calculated through the satisfaction survey. The results of the experiment showed high satisfaction levels in accuracy, novelty, self-reference and usefulness, which proved the effectiveness of the recommendation model. This study is meaningful in the sense that it suggested a learner-centered recommendation system based on machine learning, which has not been researched enough both in domestic, foreign domains.
Keywords
TF-IDF; Similarity; Intelligence Tutoring System; Individualization; Recommendation System;
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Times Cited By KSCI : 3  (Citation Analysis)
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