• Educational Technology International
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• 제9권2호
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• pp.79-95
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• 2008

This paper aims to investigate modeling scripts based on the mode of group interaction in a computer-supported collaborative learning environment. Based on a literature review, this paper assumes that group interaction and its mode would have strong influence on the online collaborative learning process, and furthermore lead learners to create and share significant knowledge within a group. This paper deals with two different modes of group interaction- distributed and shared interaction. Distributed interaction depends on the external representation of individual knowledge, while shared interaction is concerned with sharing knowledge in group action. In order to facilitate these group interactions, this paper emphasizes the utilization of appropriate CSCL scripts, and then proposes the conceptual framework of CSCL scripts which integrate the existing scripts such as implicit, explicit, internal and external scripts. By means of the model regarding CSCL scripts based on the mode of group interaction, the implications for research on the design of CSCL scripts are explored.

• 동아시아식생활학회지
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• 제21권1호
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• pp.108-117
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• 2011

The purpose of this study was to analyze the association between educational environment (physical environment of cooking institutes and curriculum) and satisfaction with learning of students at local cooking institutes. Self-administered questionnaires were distributed to 300 student enrolled at cooking institutes located in Pohang and Gyeongju, and a total of 265 were usable. Collected data were statistically analyzed using SPSS 12.0 by frequency, factor, reliability, t-test and Duncan's multiple range test. The results can be summarized as follows. Most of the subjects were enrolled at cooking institutes to learn Korean and Western cuisine. There were significant differences in learning according to institution, facility, method and instructor. There were also significant differences in learning according to gender, age, education, and attended classes. Based on the results, the physical environment of cooking institutes contributed to learning in the students, but the operation system also needs to be improved. However, study was limited in sample size and area, the results can-not be generalized.

• 한국환경복원기술학회지
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• 제24권6호
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• pp.89-96
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• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.9-17
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• 2021

This is a descriptive investigative study which attempts to confirm the mediating effect of learning motivation in the relationship between self-directed learning, learning commitment, and learning persistence intention of university students in an online learning environment. The questionnaires were randomly distributed online and the agreed questionnaires were retrieved, with a total of 338 copies used for analysis. The following is the summary of the findings. First, there were significant differences in learning persistence intention according to general characteristics depending on age, major, part-time job, and academic level. Second, the results showed a positive correlation between self-directed learning, learning commitment, learning motivation, and learning persistence intentions of the subjects were statistically significant. Third, after checking the mediating effect of learning motivation in relation to self-directed learning, learning commitment and learning motivation, the learning motivation has a partial mediating effect on learning and 23% explanatory power, and the learning commitment was found to have a complete mediating effect on the impact of learning motivation on learning intentions with 21% explanatory power. Based on these results, it is necessary to provide a more diverse educational environment, such as operating a motivation semester program that can improve learning motivations along with learning commitment, and the use of a variety of contents that can focus the learner's interest or attention.

• 정보교육학회논문지
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• 제9권1호
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• pp.79-86
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• 2005

컴퓨터와 통신 기술이 발전함에 따라 네트워크를 통한 일반 사용자들의 컴퓨터 활용 빈도와 요구하는 데이터의 양이 급격히 증가되었다. 이에 따라 최근의 교육 시스템들은 정보의 활용성을 향상시키기 위하여 이질적인 시스템들을 의미상으로 연결하고 있다. 따라서 최근의 웹 기반 교수-학습은 학습자 스스로 학습 내용, 학습 시간 및 학습 순서를 선택하고 조직하는 유비쿼터스 학습방향으로 나아가고 있다. 즉, 학습자 개개인의 특성(선수 지식, 학습 양식, 흥미, 관심)에 맞는 적응적인 교수-학습 환경을 제공하는 방향으로 변화되고 있다. 본 논문은 유비쿼터스 학습 환경에서 다양한 분산정보의 통합을 위하여 사용자들이 요구하는 학습내용을 각 지역서버의 자치성을 유지하면서 효과적으로 학습하기 위한 미디에이터내의 처리방법에 대해 제안한다. 또한 과거와 최근의 학습내용의 활용형태가 다양하게 변할 수 있으므로 시간에 따른 감쇄율을 활용빈도에 적용하여 최근의 활용빈도의 변화에 민감하게 반응하고 활용형태의 변화에 따라 적응적으로 학습내용을 사용할 수 있는 방법을 제안한다.

• 전자공학회논문지S
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• 제35S권3호
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• pp.1-8
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• 1998

In this paper, we propose a Neural Call Admission Control (CAC) method using a Sparse Distributed Memory(SDM). CAC is a key technology of TM network traffic control. It should be adaptable to the rapid and various changes of the ATM network environment. conventional approach to the ATM CAC requires network analysis in all cases. So, the optimal implementation is said to be very difficult. Therefore, neural approach have recently been employed. However, it does not mett the adaptability requirements. because it requires additional learning data tables and learning phase during CAC operation. We have proposed a neural network CAC method based on SDM that is more actural than conventioal approach to apply it to CAC. We compared it with previous neural network CAC method. It provides CAC with good adaptability to manage changes. Experimenatal results show that it has rapid adaptability and stability without additional learning table or learning phase.

• 정보교육학회논문지
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• 제3권2호
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• pp.113-119
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• 2000

I studied a distance education model for English learning on the Internet. Basic WWW files, that contain courseware, are constructed with HTML, and functions, which are required in learning, are implemented with Java. Students and educators can access the preferred unit composed of the appropriate text, voice and image data by using a WWW browser at any time. The education system supports the automatic generation facility of English problems to practice reading and writing by making good use of the courseware data or various English text resources located on the Internet. Our system has functions to manage and control the flow of distance learning and to offer interaction between students and the system in a distributed environment. Educators can manage students' learning and can immediately be aware of who is attending and who is quitting the lesson in virtual space. Also, students and educators in different places can communicate and discuss a topic through the server. I implemented these functions, which are required in a client/server environment of distance education, with the use of Java. The URL for this system is "http://park.taegu-e.ac.kr" in the name of GAIA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3120-3137
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• 2021

Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

• 한국전자통신학회논문지
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• 제9권7호
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• pp.791-797
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• 2014

빅데이터의 병렬분산처리 시스템을 위한 아파치 하둡 환경을 구축하기 위해서는 다수의 컴퓨터를 연결하여 노드를 구성하거나, 하나의 컴퓨터에 다수의 가상 노드 구성을 통해 클라우딩 환경을 구축하여야 한다. 그러나 이러한 시스템을 교육 환경에서 실습용으로 구축하는 것은 복잡한 시스템 구성과 비용적인 측면에서 많은 제약이 따른다. 따라서 빅데이터 처리 분야의 입문자들과 교육기관의 실습용으로 사용할 수 있는 실용적이고 저렴한 학습 시스템의 개발이 시급하다. 본 연구에서는 라즈베리파이 보드를 기반으로 하둡과 NoSQL과 같은 빅데이터 처리 및 분석 실습이 가능한 빅데이터 병렬분산처리 학습시스템을 설계 및 구현하였다. 구현된 빅데이터 병렬분산처리시스템은 교육현장과 빅데이터를 시작하는 입문자들에게 유용한 시스템이 될 것으로 기대된다.

• 제어로봇시스템학회논문지
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• 제18권5호
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.