• Educational Technology International
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• 제18권2호
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• pp.215-247
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• 2017

This study aims to conduct a literature review on visual scaffolding. Visual scaffolding, as a support for learning, employs various forms of visual objects which can be either content-independent or content-dependent and the types of which would be abstract-verbal, concrete-verbal, concrete-visual, or abstract visual. The effectiveness of visual scaffolding can be argued in the following three aspects: 1) explicit representation of information and emphasis of critical features in effective and efficient manner, 2) supplement of additional information, 3) structural understanding with decrease in cognitive load. The limitations of the study and the suggestions for future study are discussed.

• 한국콘텐츠학회논문지
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• 제20권1호
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• pp.562-572
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• 2020

본 연구는 동료학습을 적용한 조리실무관련 실습과목의 학습부진 대학생의 주관적 인식유형을 분석하여 조리실무 관련 수업의 보다 나은 교육효율성 제고를 위한 기초 자료를 제공하고자 한다. 또한 소규모 학생들의 주관적 인식에 대한 연구를 위해 Q방법론을 이용하여 분석하였으며, 주관적 인식들 사이에서 발견되는 공통적인 특징의 분석을 통해 총 3가지의 유형을 도출하였다. 제 1유형(N=8) : 학습효과 증대 형 (Increase learning effectiveness type), 제 2유형(N=8): 소극적 학생에 대한 수업자료개발 필요 (Development of lesson materials for passive students), 제3유형(N=6): 실습형 자기주도학습 개발 필요 (Practical self-directed learning needs development)은 각각 고유의 특징을 가진 유형으로 분석되었다. 결과적으로 조리실무관련 실습과목의 학습부진 학생들에게 동료학습을 적용함으로서 학습부진학생들의 수업흥미유발, 실수감소로 인한 자신감 상승 등의 학습효과증대와 추가학습에 대한 인식 증가와 주도적 학습 참여 유도와 같은 긍정적인 학습효과를 가져왔음을 발견하였으나, 일부학생의 경우 동료학생에 피해를 주는 것 같은 느낌과 타 학생들의 시선에 대한 부담감을 표현하는 집단이 발견되기도 했다. 연구 결과를 통해 수준별 학습 분위기가 조성된 학습 환경에서의 실습조리과정의 진행과 주도적 학습을 위한 추가 교육과정의 개발은 보다 효과적인 동료학습을 위한 추가적인 조건임을 발견하였다.

• 지식경영연구
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• 제17권4호
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• pp.147-162
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• 2016

Knowledge sharing has many benefits; however, employees are generally reluctant to share their knowledge with co-workers. This reluctance can be attributed to the facts that sharing knowledge involves the threat of losing personal competitiveness and the codification of knowledge for sharing requires additional effort. This study explains why employees engage in knowledge sharing despite the threat and cost of sharing knowledge. Specifically, it examines the effects of the quality of co-worker exchange (CWX) on knowledge sharing and the moderating effect of learning goal orientation on the relationship between CWX and knowledge sharing. Data from 186 individuals indicate that there is a positive relationship between CWX and knowledge sharing, and that this relationship is strengthened when learning goal orientation is low rather than when it is high. The theoretical and practical implications of the findings are also discussed.

• ETRI Journal
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• 제31권4호
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• pp.419-428
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• 2009

A question answering (QA) system can be built using multiple QA modules that can individually serve as a QA system in and of themselves. This paper proposes a learnable, strategy-driven QA model that aims at enhancing both efficiency and effectiveness. A strategy is learned using a learning-based classification algorithm that determines the sequence of QA modules to be invoked and decides when to stop invoking additional modules. The learned strategy invokes the most suitable QA module for a given question and attempts to verify the answer by consulting other modules until the level of confidence reaches a threshold. In our experiments, our strategy learning approach obtained improvement over a simple routing approach by 10.5% in effectiveness and 27.2% in efficiency.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권2호
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• pp.76-80
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• 2005

In this paper, a Direct Learning Control (DLC) method is proposed for linear feedback systems to improve the tracking performance when the task of the control system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions given to the system have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to genera additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.385-398
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• 2021

The complexity of deep learning models affects the real-time performance of gesture recognition, thereby limiting the application of gesture recognition algorithms in actual scenarios. Hence, a residual learning neural network based on a deep convolutional neural network is proposed. First, small convolution kernels are used to extract the local details of gesture images. Subsequently, a shallow residual structure is built to share weights, thereby avoiding gradient disappearance or gradient explosion as the network layer deepens; consequently, the difficulty of model optimisation is simplified. Additional convolutional neural networks are used to accelerate the refinement of deep abstract features based on the spatial importance of the gesture feature distribution. Finally, a fully connected cascade softmax classifier is used to complete the gesture recognition. Compared with the dense connection multiplexing feature information network, the proposed algorithm is optimised in feature multiplexing to avoid performance fluctuations caused by feature redundancy. Experimental results from the ISOGD gesture dataset and Gesture dataset prove that the proposed algorithm affords a fast convergence speed and high accuracy.

• 공학교육연구
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• 제21권5호
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• pp.3-9
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• 2018

This article examined the effects of self-reflecting journal writing in speaking class on academic performance of science and engineering students. To assess the effect, 27 science and engineering students from the "Speech and Life" class were asked to keep a self-reflecting journal. Pre and post-intervention surveys were conducted, followed by the analysis of learning effect and satisfaction. In addition to the pre and post-intervention surveys, an additional survey on speaking ability was conducted at the same time and the change of the students' ability was assessed. Results showed that after writing self-reflection journals, participants' learning effect and satisfaction has increased, and their speaking performance was also improved.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제22권1호
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• pp.35-46
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• 2019

This study examines how open-ended tasks can be implemented with the support of redefined learning goals and teaching practices from a student-centered perspective. In order to apply open-ended tasks, learning goals should be adopted by individual student's cognitive levels in the classroom context rather than by designated goals from curriculum. Equitable opportunities to share children's mathematical ideas are also attainable through flexible management of lesson-time. Eventually, students can foster their meta-cognition in the process of abstraction of what they've learned through discussions facilitated by teachers. A pedagogical implication for professional development is that teachers need to improve additional teaching practices such as how to tailor tasks relevant to their classroom context and how to set norms for students to appreciate peer's mathematical ideas in the discussions.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.67-74
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• 2021

As IoT(Internet Of Things) devices like a smart sensor have constrained power sources, a power strategy is critical in WSN(Wireless Sensor Networks). Therefore, it is necessary to figure out the residual power of each sensor node for managing power strategies in WSN, which, however, requires additional data transmission, leading to more power consumption. In this paper, a residual power estimation method was proposed, which uses ignorantly small amount of power consumption in the resource-constrained wireless networks including WSN. A residual power prediction is possible with the least data transmission by using Machine Learning method with some training data in this proposal. The performance of the proposed scheme was evaluated by machine learning method, simulation, and analysis.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.13-20
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• 2021

A Wasserstein GAN(WGAN), optimum in terms of minimizing Wasserstein distance, still suffers from inconsistent convergence or unexpected output due to inherent learning instability. It is widely known some kinds of restriction on the discriminative function should be considered to solve such problems, which implies the importance of Lipschitz continuity. Unfortunately, there are few known methods to satisfactorily maintain the Lipschitz continuity of the discriminative function. In this paper we propose techniques to stably maintain the Lipschitz continuity of the discriminative function by adding effective regularization terms to the objective function, which limit the magnitude of the gradient vectors of the discriminator to one or less. Extensive experiments are conducted to evaluate the performance of the proposed techniques, which shows the single-sided penalty improves convergence compared with the gradient penalty at the early learning process, while the proposed additional penalty increases inception scores by 0.18 after 100,000 number of learning.