• Journal of Korean Elementary Science Education
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• v.33 no.1
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• pp.129-139
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• 2014

This study was conducted to examine the effect of elementary science class using name card method on scientific learning motivation and academic achievement of elementary students. Two sixth grade classes were divided into experimental group and comparison group to treat the experimental group with elementary science class using name card method. General class according to teacher manual was implemented for the comparison group. Elementary science class applying name card method was conducted for 10 sessions throughout the experimental period of 8 weeks. The results of this study were as follows. First, elementary science class with name card method was effective in improving scientific learning motivation. Second, elementary science class with name card method had significant effect on improvement of scientific learning academic achievement. The study results showed that elementary science class with name card method was effective for scientific learning motivation and academic achievement of elementary students.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.49-55
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• 2018

In this paper, we propose a design and development of instructional cards to understand the grammar of coding, solving the problems and extending the computational thinking in the robot-driven environment. First, we designed the input/output module of the robot to process the coding grammar through the function analysis of the robot. And we designed the module of command card to learn coding grammar using color sensors. We have proven the validity of the designed instruction card by examining the experts to see if it is suitable for coding grammar learning. Designed robot and command card were developed with 28 cards and sensor robot. After applying the developed robot and command card to the elementary school students, the questionnaire showed that students grow the understanding and confidence of coding. In addition, students showed an increased need for programming learning.

• Human Ecology Research
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• v.54 no.3
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• pp.333-349
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• 2016

This study develops and evaluates a game-based lesson plan applied to the 'Food Culture' unit of a high school Home Economics class. We developed, implemented, and evaluated lesson plans for seven periods that contained 'the Korean food table setting card,' 'the world's food culture card,' and the procedure for cards games according to the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. 'The Korean food table setting card' consisted of 'the Korean food table setting order card' to easily understand 10 types of Korean traditional daily meals based on pictures and 'the Korean food table setting food card' to easily understand Korean traditional food based on 104 kinds of food picture and quick response (QR) code. 'The world's food culture card' consisted of 'the world's food culture quiz card' to help learners easily understand influential food culture formation factors, features of food culture, typical foods from 16 countries, and 'the world's traditional food card' to help learners easily understand typical foods from 16 countries through 63 kinds of pictures. Respective 'game guides' were also developed. High school students who studied the game-based Home Economics classes and who participated in the 'Food Culture' unit, could easily and enjoyably learn the food culture of Korea (and other countries), actively participate in learning activities, and understood the content of food culture. In addition, they evaluated that the game-based instruction was easy to remember with minimal memorizing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.398-401
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• 2012

본 논문에서는 유비쿼터스 기반의 스마트카드를 이용하여 다양한 사용자의 이용과 함께, 타 시스템과 연동을 통한 사용자 인증 및 교통, 유통, 학생증 기반을 제공하는 다기능 스마트 E-Learning 시스템을 구축하여, 다양한 제휴를 제공하고, 미래교육인프라를 활용함과 동시에 첨단 IT 환경인 스마트카드를 적용할 수 있는 종합적인 정보 서비스를 제공함으로써 유비쿼터스 E-Learning 문화를 체험하고, 보다 높은 수준의 교육환경을 제공하고자 한다. 또한 미래지향적 첨단 E-Learning 생활을 유도함과 동시에 각종 시설물 이용 및 다양한 생활에 이용되어 더 나은 Smart E-Learning을 제공한다. 이는 다양한 실적 및 통계자료의 활용을 통해 현황 파악의 신속성, 정확성을 기하여 21 세기 E-Leaning로서 경쟁력 강화 및 이미지 제고를 위하여, 스마트카드 시스템을 활용한 스마트 E-Learning 모델에 대하여 제시하고자 한다.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• Journal of Korean Elementary Science Education
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• v.24 no.3
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• pp.281-291
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• 2005

The purpose of this study was to investigate elementary school pupil's classification ability that appears in classification activity. For this study, we developed 2 suitable tools in classification activity achievement. One is artificial stimulus card that comes into view clearly. The other is natural stimulus card that does not come into view well. The test was administrated to 376 pupils of 2, 4, and 6 grade in D elementary School in Yeongdeungpo-gu, Seoul. The result proved in this study was as following. First, elementary school pupil's classification ability showed the developmental change as the grade level rises. Second, there was no statistical difference between boys and girls. Third, there was high correlation between sort artificial category and natural category in their ability. Fourth, classification achievement rate of constant level by grade was seen regardless of the items. The findings above gives following guidance in science classification learning. First, if teacher understands the development of students' classification ability, more effective classification guidance is available. Second, to cultivate students' classification ability, we should devise and apply program depending on their classification ability by grade.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.545-551
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• 2005

The main objective of fraud detection is to minimize costs or losses that are incurred due to fraudulent transactions. Because of the problem's nature such as highly skewed, overlapping class distribution and non-uniform misclassification costs, it is, however, practically difficult to generate a classifier that is near-optimal in terms of classification costs at a desired operating range of rejection rates. This paper defines a performance measure that reflects classifier's costs at a specific operating range and offers a cost-sensitive learning approach that enables us to train classifiers suitable for real-world credit card fraud detection by directly optimizing the performance measure with evolutionary programming. The experimental results demonstrate that the proposed approach provides an effective way of training cost-sensitive classifiers for successful fraud detection, compared to other training methods.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.513-520
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• 2019

The general learning method aims at conveying knowledge by conveying the contents of the learning set to numerous of learners. However, such a method is difficult to induce the interest of the learner, and the unilateral delivery method has a disadvantage in that the concentration of the learner can be lowered and the overall academic achievement can be lowered. In order to solve this problem, the gay learning method which induces the interest of the students themselves is studied, and the gay learning game which combines the education and the game can influence the learning by inducing the interest of the student. In this paper, we propose a method to prevent the forgery and falsification of the blockchain, which has been widely discussed by the 4th Industrial Revolution, as a card game, And suggests ways to contribute to the development of the process.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.222-232
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• 2021

The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.