• 한국정보교육학회:학술대회논문집
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• 2007.08a
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• pp.233-239
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• 2007

최근 정보통신기술의 발달로 학교 교육 현장에서는 미래형 교육을 위한 새로운 노력들이 시도되고 있다. u-러닝 (Ubiquitous Learning)으로 일컬어지는 이러한 교육을 이끌어가는 도구로서 UMPC (Ultra Mobile Personal Computer)는 기존 PC로 할 수 있는 모든 것을 가능케 하면서 나아가 휴대성과 이동성의 장점까지 겸비해 최근 급부상하고 있다. 본 연구에서는 이러한 동향을 반영하여 평생학습 사회에서 누적된 학습을 효과적으로 관리할 수 있도록 UMPC를 기반으로 한 학습 관리 시스템을 설계하였다. 본 시스템의 장점은 다음과 같다. 첫째, 자기조절학습 이론의 자기조절학습 전략을 적용함으로써 학습자가 능동적이고 자기주도적으로 학습을 점검하고 관리할 수 있도록 하였다. 둘째, 복잡하고 불필요한 기능을 배제하고 기존의 학습 자원과 도구를 최대한 활용하여 시스템의 편의성을 높임으로써 바쁜 일상에서도 간단하게 사용할 수 있는 학습 관리 및 생활 관리 도우미의 역할을 할 수 있도록 하였다. 셋째, 자기 학습에 대한 피드백을 지속적으로 할 수 있도록 도와줌으로써 궁극적으로 학업 성취 향상에 기여할 수 있도록 하였다.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.105-112
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• 2022

Recently, many studies using reinforcement learning-based autoscaling have been performed to make autoscaling policies that are adaptive to changes in the environment and meet specific purposes. However, training the reinforcement learning-based Horizontal Pod Autoscaler(HPA) policy in a real environment requires a lot of money and time. And it is not practical to retrain the reinforcement learning-based HPA policy from scratch every time in a real environment. In this paper, we implement a reinforcement learning-based HPA in Kubernetes, and propose a transfer leanring technique using a queuing model-based simulation to accelerate the training of a reinforcement learning-based HPA policy. Pre-training using simulation enabled training the policy through simulation experience without consuming time and resources in the real environment, and by using the transfer learning technique, the cost was reduced by about 42.6% compared to the case without transfer learning technique.

• Information and Communications Magazine
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• v.31 no.12
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• pp.81-88
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• 2014

본고에서는 교육내용 성취기준 링크드데이터 프로파일 설계 및 활용에 대해 소개한다. 성취기준은 학생들이 학습의 결과로 반드시 알아야 할 지식, 능력 및 태도를 구체적으로 명시한 것으로서 국가 교육과정의 중요한 요소로 인식되고 있다. 따라서 교수학습지원시스템은 성취기준 링크드데이터 프로파일을 기반으로 교육과정, 수럽계획, 학습자원, 평가 등을 연계할 수 있도록 구현되어야 한다.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.497-506
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• 2013

The purpose of this study is to explore and develop a new instructional approach to a technology-enhanced, collaborative learning environment called Smart technology-enhanced Team-Based Learning (S-TBL). We designed a novel instructional model that combines mobile technology, collaborative teamwork, a problem-solving process, and a variety of evaluation techniques from the viewpoint of a conventional team-based model. Based on the traditional TBL model, we have integrated smart learning technologies: 1) to provide a holistic learning environment that integrates learning resources, assessment tools, and problem solving spaces; and 2) to enhance collaboration and communication between team members and between an instructor and his or her students. The S-TBL instructional approach combines: 1) individual learning and collaborative team learning; 2) conceptual learning and problem-solving & critical thinking; 3) both individual and group assessment; 4) self-directed learning and teacher-led instruction; and 5) personal reflection and publication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.577-578
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• 2021

Recently, artificial intelligence systems are being used in various fields. The accuracy of the decision algorithm of artificial intelligence is greatly affected by the amount of learning and the accuracy of the learning data. In the case of the amount of learning, a large amount of data is required because it has a decisive effect on the performance of AI. In this paper, we propose a data collection system for constructing a system that analyzes future conditions and changes in infants' conditions based on the body temperature data of infants and toddlers. The proposed system is a system that collects and transmits data, and it is believed that it can minimize the resource consumption of the server system in existing big data analysis and training data construction.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.155-160
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• 2007

A learning resource is a SCO or a collection of on or more assets in the SCORM. A storage policy is required to search rapidly and reuse assets in e-learning environment. However there are not research results about it. In this paper, We propose a storing method for assets based on cluster and define the mathematical formulation of it. Also, we present criteria for assets evaluation and describe procedure to evaluate each asset. We show that the search based on proposed cluster storing method increase performance than the categorization search through performance evaluation.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• The Journal of Korean Association of Computer Education
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• v.16 no.5
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• pp.69-83
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• 2013

This study aims towards developing design guidelines for teachers' professional development programs (PDP) that help to improve self-regulated learning skills through OER (Open Educational Resources). To achieve the research objective, first, literature review and case studies about famous OER collections were conducted, and from these reviews, twenty-six guidelines according to ten categories suggested by Zimmerman & Martinez-Pons were derived and finalized into fifteen items in nine categories through review of five experts. Based on the guidelines, a prototype of a teacher's PDP was implemented and evaluated by seven teachers. Results of the usability evaluation proved that the design strategies implemented in the prototype might contribute to improving teacher's self-regulated learning skills and helped to find the teacher's needs based on their PDP.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.425-426
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• 2019

전통적인 마이닝 기법은 다양한 디지털 매체와 센서 등에서 생산되는 빅데이터를 처리하기 어려울 뿐 아니라 신규 데이터 누적시 전체 데이터를 재분석 해야하는 비효율성과 대용량의 문서를 학습함에 있어 메모리부족 문제, 학습 소요시간 문제 등이 있다. 이러한 문제를 해결하기 위하여 본 논문에서는 자질축소 기법에 의존하지 않고 대량의 문서를 자유롭게 학습하고 부분적인 자질 추가 변경 시에 변경요소만을 추가 반영할 수 있는 범용적이고 일반적인 분류기의 구조설계 방법을 설계 및 구현하였다. 점진적 학습 모듈은 일반적인 학습 방법이 데이터의 추가 및 변동시마다 모든 데이터를 재학습하는 데 반해, 기존의 학습 결과에 증분된 데이터만 재처리 없이 추가적으로 학습한다. 재학습을 위해 사용자는 작업 수행 중 자원 관리를 통해 기존에 처리된 데이터를 자유롭게 가져와서 새로운 데이터와 병합이 가능하다. 이러한 점직적 학습 효율성은 빅데이터 기반 데이터 처리에 주요한 특성인 데이터 생산 속도를 극복하기 위한 좋은 대안이 될 수 있음을 확인하였다.

• Digital Contents
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• no.5 s.144
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• pp.68-69
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• 2005

습자가 원하는 학습자원을 컴퓨터가 스스로 찾아내서 학습자에게 전달해주고, 더 나아가 새로운 지식까지 추론해서 제공해 줄 수는 없을까? 의미의 웹이라 불리고 있는 시멘틱 웹(Semantic Web)은 의미적으로 연결돼 있는 학습 정보를 컴퓨터가 의미를 이해해서 학습자가 원하는, 학습자 수준에 맞는 정보를 제공해주고 더 나아가 지식까지도 추론해서 학습자에게 가장 적합한 형태로 전달해 줄 수 있는 강력한 메커니즘으로 부각되고 있다. 이에 필자는 살이 있는 e러닝이 되기 위해서는 시멘틱 웹과의 통합이 필요하다고 생각해 2회에 걸쳐 시멘틱 웹과, 시멘틱 웹을 e러닝에 어떻게 적용할 것인지에 대해 진단해 보고자 한다.