• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.299-300
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• 2020

본 논문에서는 머신러닝, 데이터 처리 학습을 위한 EPL 기반 D.I.Y 실습 플랫폼을 통한 학생들의 학습을 통합 관리, 학습 능률 향상, 학습 흥미 유도하고 나아서 학생의 학습 패턴을 분석해 그에 적절한 강의 추천을 목표로 하는 지능형 통합 학습 관리 플랫폼을 제안한다.

• The Mathematical Education
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• v.59 no.4
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• pp.373-387
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• 2020

The purpose of this study is to verify the effects of personalized learning platforms applied with machine learning recommendation modules that upgrade recommended algorithms by themselves through learning big data analysis on students' learning time, self-directed learning ability, mathematics achievement, and attitudes toward mathematics, and the correlation between them. According to the study, customized learning affected learning time, self-directed learning ability and mathematics attitude, while learning time affected self-directed learning ability. Self-directed learning ability has had a significant impact on the attitude of mathematics and mathematical achievements. As a result of the mediated effectiveness test, the indirect impact of customized learning on mathematics attitude and mathematics performance was significant through the medium of learning time and self-directed learning ability.

• Journal of Korea Game Society
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• v.14 no.6
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• pp.69-78
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• 2014

The purpose of this paper is to analyze a developing process of 3D STEAM based G learning contents. STEAM means Science, Technology, Engineering, Art, Mathematics. It is focused on to raise students interest on science and technology through integrating the subjects together. Also it's purpose is to raise students understanding capabilities on technology convergence and problem solving. Therefore, this paper focused on the analysis of the development of 3D STEAM based G learning contents. Science topic, 'solar system and stars' was decided as a G learning based contents. Students showed higher immersion and motivation on the contents.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.804-806
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• 2020

딥러닝(deep learning)은 기계학습 알고리즘 중 가장 널리 활용되고 있는 알고리즘이다. 딥러닝 기술은 산업, 과학, 국방 및 공공 부문을 비롯하여 거의 모든 분야에서 폭넓게 확산되고 있다. 그러나 기계학습 기술에 대한 이해와 프로그래밍 지식이 부족할 경우 자유롭게 활용하는 데는 제약이 따르고 있으며 빅데이터를 활용하여 일반 이용자들이 직접 분산 학습 모형을 개발하고 배포하는 데 어려움이 발생하고 있다. 이러한 요구를 충족시키기 위해 딥러닝 프레임워크의 저수준 API를 추상화하여 고수준 분석과 분산 딥러닝을 지원하고 일반 이용자들이 실무적으로 복잡한 딥러닝 기술을 활용할 수 있는 기술을 개발하였다. 플랫폼 개발과 함께 중요하게 고려해야 하는 요소 중 하나로 플랫폼의 배포와 확장성 역시 고려되어야 한다. 본 플랫폼은 조직 내 계산 자원을 이용하여 플랫폼을 배포할 수 있으며 상용 클라우드 서비스와 연동하여 배포할 수 있도록 설계됨에 따라 환경의 제약 없이 유연한 서비스 제공이 가능하다.

• Proceedings of The KACE
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• 2017.08a
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• pp.63-64
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• 2017

정보 기술의 끊임없는 발전에 따라 광범위한 분야에서 방대한 양의 데이터가 발생하게 되면서 이를 처리하기 위한 빅 데이터에 대한 연구 및 교육이 활발히 진행되고 있다. 이를 위하여 데이터 분석 및 처리를 위한 고성능의 서버 및 분산 처리를 위한 다수의 컴퓨터가 필요하며 이는, 개인 혹은 저사양의 수업 환경에서 빅 데이터를 학습하는 데에 어려움을 겪게 한다. 때문에 가상 환경에서 원활한 빅 데이터 학습을 위한 클라우드 기반의 시스템이 필요하다. 이에 본 논문에서는, 빅 데이터 처리 기술의 하나인 Spark를 이용한 빅 데이터 플랫폼 구축에 대하여 기술한다.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.77-84
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• 2010

Children, who have a language disorder, have difficulty in expressing their reaction about stimulus of sound and vision. So it is very hard to grasp that they recognize external stimulus or not. For solving these problem, we can check response time and make them to choose stimulus by giving stimulus of sound and vision to them through Audio and Visual Stimulus and Reaction Meter System. Additionally, We can help them by improving response time by repeated study based on the results and making them to recognize and choose stimulus faster without aversion about external stimulus. It would make them not to feel uncomfortable and isolated because they are unfamiliar with external stimulus.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.11-22
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• 2022

Recently, research and development using various Artificial Intelligence (AI) technologies are being conducted in the field of education. Among the AI in Education (AIEd), conversational agents are not limited by time and space, and can learn more effectively by combining them with various AI technologies such as voice recognition and translation. This paper conducted a trend analysis on platforms that have a large number of users and used conversational agents for English learning among commercialized application. Currently commercialized educational platforms using conversational agent through trend analysis has several limitations and problems. To analyze specific problems and limitations, a comparative experiment was conducted with the latest pre-trained large-capacity dialogue model. Sensibleness and Specificity Average (SSA) human evaluation was conducted to evaluate conversational human-likeness. Based on the experiment, this paper propose the need for trained with large-capacity parameters dialogue models, educational data, and information retrieval functions for effective English conversation learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.768-769
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• 2013

e-learning platform need some Online's merit. Nowadays e-learning is widely using. at this time, It need some various educational model. futhermore, It is essential that korean competitive educational environment. one of the solution, suggest the 'QBS' system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.180-185
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• 2012

Social Learning is a new study model of future knowledge information society. In different existing study, it concentrate on relationship with others and design to connect studying with social effect as a study platform using social media such as Blog, SNS, UCC, Microblog. In my paper, social learning characteristics are described to understand social learning, that is 3 keyword such as context, connectivity, collaboration. Also we investigate social media characteristics and social media how to be used social learning. Also social learning system building method using facebook is presented.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.27-28
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• 2018

최근 기계학습에 대한 연구들이 사회적으로 이슈가 되고 있다. 하지만 기계학습은 기계학습 모델을 만들고 세밀히 조정해야하는 복잡한 작업을 수행할 수 있는 전문 지식을 가진 사용자가 필요하다. 따라서 기계학습 과정에서 사용자가 수행하여야 하는 다양한 작업을 자동으로 수행할 수 있는 자율 기계학습이 연구되고 있다. 본 논문에서는 고성능 자율 기계학습을 위한 인텔리전트 데이터베이스 플랫폼을 제안한다.