• 대한수학교육학회지:학교수학
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• 제6권1호
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• pp.37-57
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• 2004

본 연구에서는 7차 교육과정에서 요구하는 교실 수업 개선의 한 방법으로 웹 자료의 활용이 초등학교 수학과의 자기 주도적 학습에 어떻게 영향을 주는가를 알아보는데 그 목적이 있다. 본 연구를 통하여 웹 자료가 아동들에게 적극적인 학습태도를 갖게 해 주며, 수학 개념 형성을 용이하게 해 주며 협동학습에 도움을 주며 수준별 학습을 강화시켜 주고 문제해결력을 신장시키고 스스로 객관적 평가를 할 수 있도록 하여 자기 주도적 학습에 긍정적 영향을 주었음을 알 수 있었다.

• 문화기술의 융합
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• 제7권1호
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• pp.615-619
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• 2021

이 연구는 사물인터넷, 클라우드 컴퓨팅 그리고 에지 컴퓨팅 등 많은 임베디드 시스템에서 성능 및 에너지 효율을 높이고자 최적화하는 메모리 시스템에 초점을 맞추어 그 성능 개선 기법을 제안한다. 제안하는 기법은 최근 많이 이용되고 있는 머신 러닝 알고리즘을 기반으로 메모리 시스템 성능을 도모한다. 머신 러닝 기법은 학습을 통하여 다양한 응용에 사용될 수 있는데, 메모리 시스템 성능 개선에서 사용되는 데이터의 분류 태스크에 적용될 수 있다. 정확도 높은 머신 러닝 기법 기반 데이터 분류는 데이터의 사용 패턴에 따라 데이터를 적절하게 배치할 수 있게 하여 전체 시스템 성능 개선을 도모할 수 있게 한다.

• 한국콘텐츠학회논문지
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• 제18권10호
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• pp.306-319
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• 2018

본 연구는 기존 영어교재 및 학습법의 한계를 뛰어넘어, 교수의 비중을 줄이고 대학생들의 협동학습을 통해 언어감각 발전을 증진시키는 것을 연구목적으로 한다. 본 연구에서는 대학교양 영어수업의 협동학습이 영어말하기 능력향상과 정의적 성과에 미치는 영향을 분석하기 위해 서울소재 K대학 대학생 50명을 연구대상으로 하여, 2018년 3월 2일부터 6월 30일까지 약 4개월에 걸쳐, 직접 영어말하기를 위한 협동학습을 진행하고, 정의적 성과를 검증하였다 연구결과는 다음과 같다. 첫째, 대학교양 영어수업의 협동학습은 대학생의 영어말하기 능력 향상(정확성, 유창성, 복잡성)에 차이가 발생하였다. 세부요인별로는 정확성, 유창성, 복잡성순으로 차이가 발생하였다. 둘째, 대학교양 영어수업의 협동학습은 대학생의 정의적 성과 중 학습자신감과 학습흥미에 차이가 있었지만, 학습태도와 학습동기에는 차이가 발생하지 않았다. 셋째, 대학교양 영어수업의 협동학습은 영어말하기 능력향상(정확성, 유창성, 복잡성)이 정의적 성과의 학습자신감과 학습흥미에 미치는 영향은 차이가 발생하였지만, 학습태도와 학습동기에는 차이가 발생하지 않았다.

• 디지털산업정보학회논문지
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• 제14권3호
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• pp.103-113
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• 2018

The purpose of this study supports the establishment of national e-learning policy by analyzing e-learning status and current status of higher education. Enhance the competitiveness of higher education through sharing information between universities. And to improve e-learning quality management. We surveyed the current status of e-learning in 341 universities and questionnaires about e-learning content, e-learning application form, e-learning platform status was surveyed through each school's learning management system. As a result, the infrastructure of e-learning, the rate of platforms secured, and the contents are increasing gradually each year; however, still, not all students can receive the services equally. Dedicated servers and learning management systems were secured by more than 70% of general universities. In the current development status of e-learning content, multimedia, animation, and text forms are gradually decreasing, but video contents are increasing every year. Most of the online contents were used in the e-learning contents by application type, and blended learning, flipped learning, and mooc is not yet actively used since they are still in the beginning stage. Learning analysis techniques should be supported in order to easily use online learning contents such as flipped learning and mooc. We suggest that the effectiveness of e-learning should be measured and the current state of learning analysis for customized learning should be done. This study aims to contribute to the improvement of competitiveness of higher education by sharing information about e-learning among universities as a basis for improvement of e-learning policy. Future tasks are to improve the customized learning environment by adding whether the system environment for learning analysis is provided at the time of the survey.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.203-210
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• 2023

As the environment changes become more complex, learners should establish the learning strategy for the 4th industrial era and the post-COVID-19, also change. This paper focuses on the importance of self-regulated learning. Through this learning strategy, learners will form more positive attitudes and reduce negative attitudes toward the 4th industrial era. This attitude change will lead to an improvement in learners' career decision-making efficacy as a sense of future efficacy. As a result of the study, it was demonstrated that self-regulated learning improves career decision-making efficacy through the mediating effect of positive attitude formation toward the fourth industry. This article emphasizes the necessity of self-regulated learning as a valid learning strategy for the new era. The effect of self-regulated learning is explained as an improvement in attitude toward the future and a sense of efficacy. Through this learning strategy, learners' future performance could be improved.

• 정보학연구
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• 제12권4호
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• pp.33-37
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• 2009

This paper analyzed effects Web-Based cooperation Learning have on improvement in Social Adaptability and problematic behavior, using Web-Based cooperation Learning system that is designed for Mental retardation children. Is Made Teaching Design according to students level, based on elementary school Bareunsaenghwal subject. Teaching and Learning program that is going with flash and PPT Embodied is. Designed to bulletin the evaluation data for cooperation studying after studying a part of the lesson. Verification of learning effect went with experimental group and comparison group consisted of groups of 8. Students studied the Internet web data and Teaching material paper and they took pencil test. As a result, point of post-inspection was higher than that of pre-inspection. Web-Based cooperation Learning is confirmed to be effective on Social Adaptability and problematic behavior improvement.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.198-207
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• 2023

Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.457-460
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• 1989

In this paper, an improvement of the EBP(error back propagation) learning speed using an acceleration algorithm is described. Using an acceleration algorithm known as the Partan method in the gradient search algorithm, learning speed is 25% faster than the original EBP algorithm in the simulaion results.