• 컴퓨터교육학회논문지
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• 제9권4호
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• pp.1-14
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• 2006

본 연구의 목적은 컴퓨터 교과에서 요구되는 정보소양 뿐 아니라 문제해결능력을 함양시킬 수 있는 방안을 모색하는 것이다. 이를 위하여 컴퓨터 교과의 대안적 교수 학습 방법으로 주목을 반기 시작한 문제중심학습(PBL)을 중학교 컴퓨터 교과에서 적용가능한 웹기반 수업을 설계하며, PBL 수업의 구체적인 실제를 살펴보기 위하여 중학교 2학년 PBL 수업에서 다루어진 과제에 대한 학습지의 문제해결 과정을 분석하는 것이다.

• 한국컴퓨터정보학회논문지
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• 제28권5호
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• pp.189-196
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• 2023

본 연구는 정보통신기술(IT)의 발전으로 외국어 교육과 학습에서 기존의 전통적인 '훈련과 연습(drill and practice)'의 방법에서 벗어난 '컴퓨터 보조언어학습(Computer-assisted learning: CALL)'과 '모바일 지원 언어학습(Mobile-based Language Learning: MALL)'이 실제로 어학 교육에 미치는 영향을 분석한다. 연구 대상은 비교적 영어 능력이 상대적으로 우수하지 않고, 또한 영어학습에 대한 동기부여를 충분히 느끼지 못하는 전문대학 1학년 학생들을 대상으로 진행하였다. 연구 방법은 멀티미디어 저작 도구를 이용하여 디지털 콘텐츠를 제작하여 이러닝(e-learning) 시스템에 등재하여 교육보조재료로 한 학기 동안 활용한다. 동영상 콘텐츠는 컴퓨터, 스마트폰, 테블릿, 노트북 등 다양한 매체로 접근하여 활용할 수 있도록 구성한다. 동영상 콘텐츠에 대한 설문조사를 통하여 콘텐츠의 구성, 학습에 도움 여부, 보완해야 할 부분, 콘텐츠 접근 빈도, 학습경로 등을 분석한다. 분석의 결과를 통하여 정보통신기술을 활용한 어학 학습의 효과, 교육용 콘텐츠의 개선해야 할 부분 및 교육에 어떻게 활용해야 하는지에 대해 제언한다. 궁극적으로는 '플립러닝((Flipped Learning)'을 위한 컴퓨터 및 모바일 매체에 대한 효용성 분석과 활용방안을 제언한다.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.247-255
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• 2019

In this paper, we propose the effect of project-based robot education program on the interest toward robots and the computational thinking of elementary school students. Software education is being actively carried out around the world in order to cultivate software talents in accordance with the 4th industrial revolution. As a result, the importance of robots in education has increased, and education using robots has been actively introduced. However, the activities of simply assembling and repeating robots in schools were not effective in enhancing elementary school students' interest toward robots and computational thinking. Therefore, it is necessary to overcome traditional teaching-learning methods and to develop robot education. So, in this study, the robot education program that introduces project-based learning was developed for improvement of interest toward robots and computational thinking of elementary school students. In order to verify the developed education program, 114 elementary six grade students were selected as research subjects and the traditional teaching-learning method and project-based learning were applied to the experimental and control group. As a result, project-based learning was more effective for elementary school students' interest toward robot than traditional teaching-learning method. In computing thinking, the experimental group showed a significant improvement, but there was no statistically significant difference in the post-test.

• ETRI Journal
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• 제38권6호
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• pp.1207-1217
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• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.149-156
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• 2016

This paper is concerned with education courses operating practices for basic computer literacy training. In this study, we propose an example for students to effectively measure and evaluate the achievement of defined ability in the performance measurement and analysis, learning objectives and learning outcomes set in operation throughout the course. Through research and development use case presents the tools for teaching method and effective and objective measurement of the related subjects. And based on the results, we propose the possibility of utilizing NCS-based course operation and education certification. In this study, the measurement process is based on the association with the objective of the development and operation, and measurement tools, measuring tools for measuring learning outcomes associated with the curriculum design methods for the measurement and evaluation of the case of the operation of the course units of learning outcomes and the method proposed.

• 대한수학교육학회지:수학교육학연구
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• 제11권1호
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• pp.89-102
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• 2001

Computer has been regarded as an alternative that could overcome the difficulties in the teaching and learning of mathematics. But the didactical problems of the computer-based environment for mathematics education could give us new obstacles. In this paper, first of all, we examined the application of the learning theories of mathematics to the computer environment. If the feedbacks of the computer are too immediate, students would have less opportunity to reflect on their thinking and focus their attention on the visual aspects, which leads to the simple abstraction rather than the reflective abstraction. We also examined some other Problems related to cognitive obstacle to learn the concepts of geometric figure and the geometric knowledge. Based on the analysis on the problems related to the computer-based environment of mathematics teaching and learning, we tried to find out the direction to use computer more adequately in teaching and learning geometry.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• International Journal of Contents
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• 제8권2호
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• pp.23-27
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• 2012

The successful use of digital textbooks (DTs) in schools requires the development of various teaching and learning methods that are appropriate for DTs. However, recent DT studies have focused mainly on the implementation of DT features and formats. The objective of this study was to investigate a problem-based learning instructional model appropriate to DTs and to present the experimental results using the problem-based DT to demonstrate its educational effectiveness. Two learning-achievements tests were conducted to analyze the learning experience and effectiveness of the problem-based DT after it had been used in a high school for a certain period. The experimental results indicated that the students who used the DT, especially lower-level students, exhibited improved problem-solving ability and demonstrated a better practical understanding of the subject than students who used printed textbooks.

• Journal of Information Processing Systems
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• 제4권4호
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• pp.159-168
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• 2008

Human-Robot Interaction (HRI), based on already well-researched Human-Computer Interaction (HCI), has been under vigorous scrutiny since recent developments in robot technology. Robots may be more successful in establishing common ground in project-based education or foreign language learning for children than in traditional media. Backed by its strong IT environment and advances in robot technology, Korea has developed the world's first available e-Learning home robot. This has demonstrated the potential for robots to be used as a new educational media - robot-learning, referred to as 'r-Learning'. Robot technology is expected to become more interactive and user-friendly than computers. Also, robots can exhibit various forms of communication such as gestures, motions and facial expressions. This study compared the effects of non-computer based (NCB) media (using a book with audiotape) and Web-Based Instruction (WBI), with the effects of Home Robot-Assisted Learning (HRL) for children. The robot gestured and spoke in English, and children could touch its monitor if it did not recognize their voice command. Compared to other learning programs, the HRL was superior in promoting and improving children's concentration, interest, and academic achievement. In addition, the children felt that a home robot was friendlier than other types of instructional media. The HRL group had longer concentration spans than the other groups, and the p-value demonstrated a significant difference in concentration among the groups. In regard to the children's interest in learning, the HRL group showed the highest level of interest, the NCB group and the WBI group came next in order. Also, academic achievement was the highest in the HRL group, followed by the WBI group and the NCB group respectively. However, a significant difference was also found in the children's academic achievement among the groups. These results suggest that home robots are more effective as regards children's learning concentration, learning interest and academic achievement than other types of instructional media (such as: books with audiotape and WBI) for English as a foreign language.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.