• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.21-28
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• 2018

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.

• Journal of Engineering Education Research
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• v.24 no.3
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• pp.70-76
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• 2021

As intellectual property has become an important competitive element in the industry, government-led patent education is rapidly expanding. It is important to look at whether each patent education course is organically linked to each other under a life-long patent education system from elementary school to college. This study focused on the link between high school education and college education to analyze statistically how invention activities in high school affect patent education achievement at college. Statistical analysis of a survey of students taking a basic course of intellectual property revealed no statistically significant correlation between invention experience in high school and college students' patent class total score, contrary to popular perception. These results are believed to be due to the distribution of data on the number of invention contest participations or the number of invention class participations, which does not form a normal distribution and has a relatively large skewness. Rather, the attendance to the patent class showed a relatively distinct positive linear correlation with the patent class total score, indicating that the student's attitude in class was important for the patent education achievement.

• Journal of the Korea society of information convergence
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• v.1 no.1
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• pp.47-54
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• 2008

This study analyzed the status and causes for the dropouts of college based on the survey conducted among 14,210 freshmen attending D College, other than the supernumerary special selection, from 2001 through 2005. A significant difference was shown in all items of general characteristics. The dropout rate of women, generally selected and general highschool graduated were higher than for men, specially selected and special high school graduated, respectively. The most dropouts were due to Not Return(40.16%), followed by Unenrolled(32.98%), Voluntary Leave(26.05%) and Expelled(0.81%) in order. In the distribution of the central tendency values measured from the entire subjects, the high school records and the days of absence showed a positive skewness, while the college records showed a negative skewness with the data mostly around a higher grade. The standard deviation indicating that the dropouts got the scores higher than those of the continuing students demonstrated that there was relatively insignificant difference in scores between two groups. It was demonstrated that both the high school records and the days of absence affected the dropout. The lower the high school records were, and the more the days of absence were, the more influence both items had on the dropout. The influence degree of each item was similar. Lower the scores were in terms other than the first term in the freshmen year, the more influence it had on the dropout. The most dropouts were influenced by the scores of the freshmen year, followed by the credits of the second term, the scores of the first term, the scores of the second term, and the credits of the first term in the freshmen year. Among the general characteristic items, the most dropouts were influenced by the course of study, followed by the gender. The effect of other items was insignificant.