• 한국초등과학교육학회지:초등과학교육
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• 제28권1호
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• pp.24-34
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• 2009

The purpose of this study is to examine the effects of background knowledge on problem solving. To achieve this aim, I proposed the model which shows problem solving process centering around background knowledge, conducted the lessons concerning the concept 'weightlessness' on pre-service elementary teachers, and then classified the pre-service elementary teachers into several groups by the difference of the results presented in the process of solving the problems on weightlessness. And I examined qualitatively the effects of background knowledge on problem solving through the interview with 11 volunteers. On the cause of the failing the problem solving, the failure of acquiring or activating the background knowledge related to the learning concept was most frequently, secondly the use of the background knowledge unrelated to the learning concept, and thirdly the failure of understanding the teaming concept. To acquire or activate the background knowledge related to the teaming concept was more difficult than to understand the new teaming concept, and the cases that use the background knowledge unrelated to the learning concept failed to solve problem. The result of interview, all interviewee understood the learning concept correctly, but all of them who fail to acquire or activate the background knowledge related to the learning concept, or use the background knowledge unrelated to the learning concept, could not solve the problem.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권2호
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• pp.52-61
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• 2006

For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.

• 컴퓨터교육학회논문지
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• 제5권2호
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• pp.29-37
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• 2002

본 연구는 웹 기반 PBL 환경에서 배경지식 수준과 메타인지 지원 도구 제공여부가 학습자의 PBL 활동에 어떠한 영향을 미치는가를 알아보기 위한 것이다. 본 연구를 위하여 웹기반 PBL 프로그램을 활용하였고 실험집단에 별도의 연구 제작된 메타인지 지원 도구를 포함시켰다. PBL수행과정에 있어서는 메타인지 지원 도구를 제공한 집단의 경우 평균이 3.82점 높아 유의도 수준 .05에서 의미있는 차이가 있는 것으로 나타났다. 또한 PBL 수행결과에 있어서 배경지식 수준과 메타인지 지원 도구의 제공여부간의 상호작용효과가 통계적으로 유의한 결과를 보였다. 결국 배경지식이 하위인 학습자 집단에 메타인지 지원 도구를 제공하여줌으로써 문제를 해결하고 결과 보고서를 작성하는데 효과적임을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제18권2호
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• pp.138-148
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• 2015

Object detection methods based on background learning are widely used in video surveillance. However, when a car runs with headlights on, these methods are likely to detect the car region and the area illuminated by the headlights as one connected change region. This paper describes a method of separating the car region from the area illuminated by the headlights. First, we detect change regions with a background learning method, and extract blobs, connected components in the detected change region. If a blob is larger than the maximum object size, we extract candidate object regions from the blob by clustering the intensity histogram of the frame difference between the mean of background images and an input image. Finally, we compute the similarity between the mean of background images and the input image within each candidate region and select a candidate region with weak similarity as an object region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권6호
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• pp.1946-1963
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• 2014

Most background subtraction methods focus on dynamic and complex scenes without considering robustness against noise. This paper proposes a background subtraction algorithm based on dictionary learning and sparse coding for handling low light conditions. The proposed method formulates background modeling as the linear and sparse combination of atoms in the dictionary. The background subtraction is considered as the difference between sparse representations of the current frame and the background model. Assuming that the projection of the noise over the dictionary is irregular and random guarantees the adaptability of the approach in large noisy scenes. Experimental results divided in simulated large noise and realistic low light conditions show the promising robustness of the proposed approach compared with other competing methods.

• 간호행정학회지
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• 제12권1호
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• pp.140-150
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• 2006

Purpose: The purpose of this study was to determine learning styles and preferred learning methods of clinical nurses. Method: Data were collected from 735 nurses at one university hospital in Seoul. Learning style inventory, a self-report questionnaire was completed by the subjects. Result: Learning styles of nurses were accommodator 35.9%, diverger 30.4%, converger 18.2%, assimilator 15.5%. Learning styles varied significantly with clinical practice area and academic background. Furthermore, RO(reflective observation) learning mode varied significantly according to the clinical practice area. AC(abstractive conceptualization) learning mode varied significantly with job position. AC and AE(active experimentation) learning modes varied significantly according to the academic background and preferred learning method. Preferred learning methods were lecture 24.8%, clinical practice 23.1%, self-directed learning 21.5%, audiovisual education 16.7%, and group discussion 13.9%. Preferred learning methods varied significantly with learning styles and career. Lecture was preferred in diverger and self-directed learning was preferred in assimilator. Clinical practice was preferred in accommodator and converger. Conclusions: This study suggested that clinical education should be applied to nurses after examining learning styles and preferred learning methods. In conclusion, to identify the nurses' learning styles could be helpful for developing the effective educational skill.

• 한국수학교육학회지시리즈A:수학교육
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• 제42권4호
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• pp.453-463
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• 2003

The main purpose of this paper is to develope elementary mathematics teaching-learning programs for pre-service elementary teachers. The elementary mathematics education program developed in this work is divided into two parts: One is the theory, the other is the practice. The theory deals with the foundations of mathematics, the objectives of mathematics education, the history of mathematics education in Korea, the psychology of mathematics learning, the theories of mathematics teaching and learning, and the methods of assessment. With respect to the practice, this study examines the background knowledge and activities of numbers and their operation, geometry, measurement, statistics and probability, pattern and function.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.98-112
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• 2024

As digital education progresses, MOOC (Massive Open Online Courses) are increasingly utilized by learners, making research on MOOC learning outcomes a necessary endeavor. In this study, we systematically investigated the impact of audiovisual elements on learning outcomes in MOOC, highlighting the nuanced role these components play in enhancing educational effectiveness. Through a comprehensive survey and rigorous analysis involving descriptive statistics, reliability metrics, and regression techniques, we quantified the influence of text, graphics, color, teacher images, sound effects, background music, and teacher's voice on learner attention, cognitive load, and satisfaction. We discovered that background music and text layout significantly improve engagement and reduce cognitive burden, underscoring their pivotal role in the instructional design of MOOC. We findings contribute new insights to the field of digital education, emphasizing the critical importance of integrating audiovisual elements thoughtfully to foster better learning environments and outcomes. Not only advances academic understanding of multimedia learning impacts but also offers practical guidance for educators and course designers seeking to enhance the efficacy of MOOC.

• Journal of East Asia Management
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• 제4권2호
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.