• Journal of Gifted/Talented Education
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• v.15 no.1
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• pp.11-36
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• 2005

Cooperative teaching emerged as one of the interesting topics on curriculum administration and teaching-learning method in BSA(Busan Science Academy). The purpose of this study is to do research on social studies learning with respect to cooperative teaching, and to develop the model of cooperative teaching. The results of this study are as follows: First, We surveyed both the concept of cooperative teaching in all aspects and the methodological application on cooperative teaching. Second, We searched all teaching-learning methods in BSA in terms of cooperative teaching. Third, We studied cooperative teaching system on social studies considering current environmental factors. Forth, We performed seminar class which is constructed as one of the cooperative teaching models. The topic of seminar was 'The distortion and falsification of Koguryeo history in China'. The participants of seminar were volunteer students and social studies teachers whose subjects were geography, history, social studies, and ethics. And the participants conducted the research and cooperative learning based on teacher's subjects and subtopics. Fifth, The interactions between teacher and teacher, student and student, and teacher and student in the process of seminar preparation and publication were conducted very excitedly. Especially we found the possibility of cooperative teaching by the interaction between teachers. Finally, students developed the mind-frame to participate in social studies learning actively, and learned the method to research social affairs for themselves, and extended the eyes to approach social affairs with different opinions.

• Journal of Science Education
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• v.44 no.3
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• pp.331-344
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• 2020

This study aims to find meaningful implications for the development of Korean elementary school math education courses and textbooks by comparing and analyzing the number and arithmetic areas of Korean and Canadian math textbooks in fifth and sixth grades. To this end, the textbook composition system of Korean and Canadian elementary schools was compared and analyzed, and the number and timing of introduction of math textbooks and math textbooks by grade, and the number in fifth and sixth grade and the learning contents of math textbooks were compared and analyzed. The following conclusions were obtained from this study: First, it is necessary to organize a textbook that can solve the problem in an integrated way by introducing the learned mathematical concepts and computations naturally in the context of problems closely related to real life, regardless of the type of private calculation or mathematics area. Second, it is necessary to organize questions using materials such as real photography and mathematics, science, technology, engineering, art, etc. and to organize textbooks that make people feel the necessity and usefulness of mathematics. Third, sufficient learning of the principles of mathematics through the use of various actual teaching aids and mathematical models, and the construction of textbooks focusing on problem-solving strategies using engineering tools are needed. Fourth, in-depth discussions are needed on the timing of learning guidance for fractions and minority learning or how to organize and develop learning content.

• 한국정보교육학회:학술대회논문집
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• 2007.08a
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• pp.45-50
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• 2007

최근 과학기술의 발달에 따른 컴퓨터 산업의 대중화로 각 가정마다 컴퓨터 1대씩은 필수로 보유하고 있다. 이러한 추세에 따라 초등학교 교육과정에도 재량활동으로 ICT 교육을 체계 있게 실시할 수 있도록 하고 있다. 하지만 컴퓨터를 가지고 정보를 활용하는 순기능을 잃어버리고 역기능이 학습지도와 생활지도의 문제로 등장했으며 컴퓨터 게임으로 인해 수많은 부정적인 영향을 끼치는 것으로 나타났다. 본 연구에서는 전국 여러 곳에서 초등학교의 고학년 학생들의 자료를 수집한 다음 이것을 특별시 광역시지역, 중 소도시지역, 읍 면지역으로 구분한 후 학생들의 컴퓨터 게임 사용 실태를 알아보고, 컴퓨터 게임 중독 측정도구를 사용하여 컴퓨터 게임 중독의 유무를 파악하며, 컴퓨터 게임 중독에 영향을 미치는 여러 요인들을 찾아보았다. 중독이 되는 요인으로는 남학생일수록, 학교 성적이 낮을수록, 게임 사용 경력이 오랠수록, 게임 사용 빈도가 높을수록, 게임 사용 시간이 많을수록, 공격성과 충동성이 높을수록, 자기통제가 안 될수록, 부모로부터 존중을 받지 못할수록, 공부스트레스가 많을수록, 대인불안이 높을수록, 친구 따라서 게임할수록 중독이 잘되는 것으로 나타났다. 소속감과 재미와 성취감이 관련이 높은 것으로 나타났다. 중독의 실태 및 요인을 알아 본 바에 따르면, 컴퓨터 중독을 비방하는 가장 큰 방법은 가정에서 부모가 자녀를 존중해주고, 바른 인성을 길러 공격성과 충동성이 높지 않고, 게임을 많이 좋아하지 않는 친구를 사귀는 것이 중요하다고 하겠다. 그리하여 가정과 학교와 사회가 올바로 역할을 수행한다면 정보화의 역기능인 컴퓨터 중독을 예방하고 정보 활용의 본래의 기능을 되살릴 수 있으리라 본다.

• Journal of KIISE
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• v.42 no.12
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• pp.1568-1574
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• 2015

Relation extraction plays a role in for the process of transforming a sentence into a form of knowledge base. In this paper, we focus on predicates in a sentence and aim to identify the relevant knowledge base properties required to elucidate the relationship between entities, which enables a computer to understand the meaning of a sentence more clearly. Distant Supervision is a well-known approach for relation extraction, and it performs lexicalization tasks for knowledge base properties by generating a large amount of labeled data automatically. In other words, the predicate in a sentence will be linked or mapped to the possible properties which are defined by some ontologies in the knowledge base. This lexical and ontological linking of information provides us with a way of generating structured information and a basis for enrichment of the knowledge base.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.655-661
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• 2021

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

• Journal of The Korean Association For Science Education
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• v.34 no.3
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• pp.285-294
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• 2014

This study investigates the effects of formative assessment utilizing mobile applications on interest and self-directedness in science instruction. The study subjects are two 6th grade classes from H elementary school located in Incheon, and the experimental group and the comparative group are composed of 21 students, respectively. The students from the experimental group have been taught with mobile devices while the comparative group has been taught in methods consistent with the current teaching standards. For the sake of research, the results of the method applied to the mobile device focus group have been edited using Google Drive Forms, entered as QR codes and stored in order for them to later be utilized for teaching and learning process. In the process, the teacher has provided the students with feedback based on their answers. The students of comparative group are to solve the same formative assessment in paper. As a result, the teacher of the mobile device focus group has been able to go through twenty-nine questions on formative assessment in the teaching and learning process, confirm the correct answers five times and provide feedback twenty-five times for additional explanation. In the inquiry about interest, the mobile device group scored 4.64 points and the standard one scored just 1.99 points (p<0.01). Fifteen students answered in the interview that and the major reason why they scored high has been because it was fun to study with mobile devices. When it comes to self-directedness over the process of teaching and learning, the mobile device focus group has answered positively but the standard group has scored relatively low (p<0.01).

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.19-35
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• 2000

Learning by examples has proven to be an efficient method in mastering various subjects including programming languages. This study hypothesizes that the number of examples and the type of examples are two significant dimensions that influence the performance of learning programming languages by examples. A set of experiments was conducted to investigate the impacts of the two dimensions in the domain of JAVA programming. The results showed that providing two examples is more effective than providing only one example even though significantly more explanations are attached to the single example. Among the 'two-example' groups, the group that was given functionally similar examples performed better than those with functionally dissimilar examples. Explanations for these results are provided in this paper based on the behavioral patterns of individual subjects in terms of time and frequency. This paper concludes with the implications of the study results for the development of effective tutoring systems for programming languages.

• Journal of KIISE
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• v.42 no.6
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• pp.817-822
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• 2015

Relation extraction is an important information extraction technique that can be widely used in areas such as question-answering and knowledge population. Previous studies on relation extraction have been based on supervised machine learning models that need a large amount of training data manually annotated with relation categories. Recently, to reduce the manual annotation efforts for constructing training data, distant supervision methods have been proposed. However, these methods suffer from a drawback: it is difficult to use these methods for collecting negative training data that are necessary for resolving classification problems. To overcome this drawback, we propose a one-class classification model that can be trained without using negative data. The proposed model determines whether an input data item is included in an inner category by using a similarity measure based on lexical information and syntactic patterns in a vector space. In the experiments conducted in this study, the proposed model showed higher performance (an F1-score of 0.6509 and an accuracy of 0.6833) than a representative one-class classification model, one-class SVM(Support Vector Machine).

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1054-1061
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• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• Convergence Security Journal
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• v.20 no.4
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• pp.81-90
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• 2020

In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.