• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.371-378
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• 2013

Video-demand learning through E-learning continuously increases on these days. However, not all video-demand learning systems can be utilized properly. When students study by education videos not matched to level of their own, it is possible for them to lose interest in learning. It causes to reduce the learning efficiency. In order to solve the problem, we need to develop a recommendation system which recommends customized education videos according the study levels of students. In this paper, we estimate the study level based on the history of students' watching behaviors such as average watching time, skipping and rewinding of videos. In the experimental section, we demonstrate our recommendation system using real students' video watching history to show that our system is feasible in a practical environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.174-185
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• 2018

This study was conducted to explore the levels of Middle East Respiratory Syndrome (MERS)-related knowledge, attitudes and preventive behaviors performance and to identify predictors of MERS-related preventive behaviors performance among clinical practice students in a tertiary hospital. The participants were 480 nursing and medical clinical practice students. Data collection was conducted using self-reported questionnaires in June of 2015 and were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, and hierarchical regression using the SPSSWIN 24.0 program. The MERS-related knowledge (9.56 out of 13 points) was high, attitudes towards MERS, such as severity cognition and prevention about MERS was positive (4.15 out of 5 points), and MERS-related preventive behaviors performance level was moderate (3.02 out of 5 points). Female students, having education experience regarding MERS, taking vaccination for influenza H1N1 infection in the last year, having the intention of taking influenza H1N1 in the current year, having fear of MERS infection, higher knowledge and more positive attitudes about MERS were predictors of better MERS-related preventive behaviors performance. These results show that general characteristics associated with MERS-related preventive behaviors performance should be considered to improve preventive behaviors of clinical practice students. Furthermore, this study highlights the need to develop effective and useful MERS education programs that provide essential knowledge and attitude about MERS that clinical practice students must acquire to promote the MERS-related preventive behaviors performance.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.566-575
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• 2019

This study examines university students' awareness and preparedness for anticipated social problems of the Fourth Industrial Revolution and deteremines if there exist differences between gender, major fields of study and school years of students. Based on surveys with 122 university students in Seoul, students majoring in technology-engineering showed more positive outlook than students majoring in humanity-social science. Female students and humanity-social science students showed higher levels of concerns with social problems than male and technology-engineering students. More than three out of five students anticipated that they will experience the influence of the Fourth Industrial Revolution within the next five years. Two out of five students assessed their preparedness to be inadequate. Given the overarching social influence, it is necessary to develop convergent education of technology and social science that raises students' understanding and preparedness without differences in major fields of study and gender for the Fourth Industrial Revolution.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.126-131
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• 2004

Students, who are studying the field of accounting, have to learn and practice statistics in order to analyze the current social status and to predict the Suture trend. however, students have a strong tendency to avoid the statistics lessons, which are dealing with complicated numbers, mathematics, various equations, and so on. This paper aims to construct a stimulating environment for learning statistics and develop an actual lesson on the Internet, Which students can study by practicing directly. The lesson is fully interactive and visualized by using multimedia technologies. The effectiveness of the experiment is measured by survey questions to the students who used the lesson.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1115-1121
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• 2018

In this paper, we have studied a data analysis method by deep learning to predict learning achievements based on accumulated data in cyber university learning management system. By predicting learner's academic achievement, it can be used as a tool to enhance learner's learning and improve the quality of education. In order to improve the accuracy of prediction of learning achievements, the autoencoder based attendance prediction method is developed to improve the prediction performance and deep learning algorithm with ongoing evaluation metrics and predicted attendance are used to predict the final score. In order to verify the prediction results of the proposed method, the final grade was predicted by using the evaluation factor attendance data of the learning process. The experimental result showed that we can predict the learning achievements in the middle of semester.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.73-82
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• 1998

본 연구의 목적은 일주일간 조사된 개인통행행태를 고려한 각 특성별 통행발생예측 방법을 제시하는데 있다. 이를 위하여 일주일간 통행빈도수의 차이를 고려한 집단간 차이를 검정하고, 그 원인을 분석하여 이에 따른 특성별 개인 통행발생예측 모형을 정립하였다. 전체 표본의 각 특성별 개인 내부 변이성을 분석해 본 결과 기간의 차이에 따른 개인 통행행태의 변화는 직업별, 나이별, 성별, 차량소유 유무, 주택소유 형태, 통행목적, 통행수단, 가구원수에 따라 집단간 차이를 보여주었다. 이러한 변수를 이용한 통행발생 예측모형의 분석결과 개인소득이 높을수록, 주책을 자가로 소유한 경우, 자동차를 소유한 경우, 학생일수록, 유직일수록 개인 통행발생량이 많은 것으로 분석되었다. 반면, 아니는 연령대가 높아질수록 통행수가 적어졌다.

• School Mathematics
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• v.18 no.4
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• pp.773-791
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• 2016

Teacher noticing ability has been considered as one of important elements influencing a quality of teaching. Noticing is closely related to teachers' in the moment decision making in a class, and teachers notice things as they create and interact with their classroom setting. Mathematics teachers as an expert should notice students' mathematics learning during a class. The aim of this study was to analyze how mathematics teacher's pre-noticing activity that the teacher anticipated students' typical strategies and difficulties in learning targeted mathematics knowledge and prepared appropriate responses worked in practice. As a result, the teacher conducted three types of noticing in her classes: noticing shaping students' understanding by using students' misconceptions or errors; noticing creating students' learning opportunities based on their prior knowledge; noticing improving students' informal reasoning. This study concluded with discussion about the positive effect of teacher's pre-noticing activity on her actual noticing in practice, as well as implications for teacher education.

• The Mathematical Education
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• v.63 no.2
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• pp.209-231
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• 2024

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).

• Journal of the Korean Chemical Society
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• v.46 no.3
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• pp.265-278
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• 2002

A concept test was administered to college students and teachers to identify their understanding of chemical equilibrium and equilibrium shift. The subjects were 53 freshmen in the General Chemistry class, 28 juniors in the Physical Chemistry class and 26 seniors from a university and 10 high school teachers in Seoul. Test items include the calculations of partial pressure and concentration of the gas in the mixture, the equilibrium constant cal-culation and the prediction of equilibrium shift when an inert gas is added to the gaseous reaction system, and the equilibrium concentration calculation and the prediction of equilibrium shift when water or common ion is added to the weak acid solution. The test was focused to identify whether the subjects can predict equilibrium shift using the reaction quotient change for the situations in which Le Chatelier principle is difficult to apply. The results showed that the achievements of teachers and juniors were significantly higher than those of freshmen and seniors. Many stu-dents had difficulties in predicting equilibrium shift using the reaction quotient while they could calculate partial pres-sure and concentration for the same situation. It means they are lack of conceptual understanding of chemical equilibrium shift.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.