• ETRI Journal
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• 제38권6호
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• pp.1207-1217
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• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

• 한국지구과학회지
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• 제41권3호
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• pp.291-306
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• 2020

이 연구에서는 다층 서답형 문항을 이용하여 태양계 구조에 대한 학습 발달과정을 개발하고 그 타당성을 검증하고자 하였다. 이를 위해 Wilson(2005)이 제안한 구인 모델링 방식을 적용하여 '태양계 구성원', '태양계 행성의 크기와 거리의 경향성', '태양계 모델링'을 발달 변인(progress variables)으로 설정하고 각각에 대한 다층 서답형 문항을 개발하여 검사지로 구성하였다. 개발된 문항을 초등학교 5학년 150명을 대상으로 '태양계와 별' 단원 수업의 사전 및 사후에 적용하였다. 평가 결과를 기술하기 위해 각각의 평가 문항에 대한 학생 응답을 범주화 하는 과정을 거쳤으며, 이범주들을 구인별로 5개 수준으로 분류하였다. Rasch 모델의 부분점수 모형을 적용하여 작성된 Wright map을 분석함으로써 학생들의 응답 결과를 기반으로 작성된 학습 발달과정의 수준이 적절한지 검토하였다. 또한, 수업 전후 학생들의 수준 변화를 추적함으로써 학습 발달과정에서 설정한 가설적인 경로의 타당성을 검증하였다. 연구 결과는 다음과 같다: 다층 서답형 문항을 이용한 상향식 연구방법으로 초등학교에 적용할 수 있는 태양계 구조에 대한 경험적 학습 발달과정을 정교하게 설정할 수 있었다. 그리고 학습 발달과정의 구인 타당도가 높게 나타나며 학생들의 발달이 학습 발달과정을 따라 변화하는 것으로 나타났다.

• 한국학교수학회논문집
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• 제9권3호
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• pp.363-384
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• 2006

일선학교에서는 방과 후 또는 방학 중 교육으로써 학습부진학생들에 대하여 특별보충과정반이라는 이름으로 따로 학급을 편성하여 운영하고 있다. 그러나 이들 학급 편성에 대한 명확하면서 납득할 만한 기준이 없고, 학교의 여건, 교사 수급 문제, 학생의 정서 문제 등 여러 가지 이유에 의하여 활성화 되지 못하고 있는 것이 사실이다. 따라서 본 연구에서는 학습부진 학생들을 위한 효율적인 지도방안으로서, 특히 특별보충과정반의 편성 운영에 대하여 탐색한다.

• International Journal of Contents
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• 제13권3호
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• pp.25-31
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• 2017

With tremendous advancement of information and communication technologies, mobile learning systems have been widely adopted in language learning contexts, and several frameworks have been developed for identifying and categorizing different factors of mobile-assisted language learning (MALL). However, pre-existing frameworks have limitations when evaluating the importance level of criteria. The purpose of this study is to develop a comprehensive hierarchical framework for identifying and categorizing success factors of MALL and prioritizing them according to the importance level. To do that, AHP method is used to quantitatively estimate weight values of MALL criteria. Results reveal that the priority of MALL criteria is ordered as follows: content, system, learner, language learning. Local weights of each criterion are also analyzed; for example, usefulness, accuracy, and authenticity are critical factors for improving MALL contents. Ease of use and mobility of MALL systems are also considered more critical than other systematic factors. In addition, availability of immediate feedback and self-directness has the highest weight values of importance. The findings of the study are discussed regarding hierarchical orders of MALL criteria and conclude that successful MALL implementation may be achieved if related elements are diversely measured and evaluated. Pedagogical implications and suggestions for further research are also presented.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.115-120
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• 2022

The main purpose of the study is to identify the key aspects of the use of innovative distance learning technologies in the training of biology students. Currently, there is a modernization, the evolution of the education system from a classical university to a virtual one, from lecture material teaching to computer educational programs, from a book library to a computer one, from multi-volume paper encyclopedias to modern search databases. During studies in higher education, distance learning ensures the delivery of information in an interactive mode through the use of information and communication technologies. The main disadvantage of distance learning is the emotional interaction of the teacher with students. It is necessary to increase the level of methodological developments for independent studies of students. The methodology includes a number of theoretical methods. Based on the results of the study, the main elements of the use of innovative distance learning technologies in the training of biology students were identified.

• 대한전기학회논문지
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• 제45권3호
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• pp.444-450
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• 1996

A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). refs., figs., tabs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• 한국강구조학회 논문집
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• 제16권4호통권71호
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• pp.443-452
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• 2004

인간의 뇌와 유사한 병렬 연산 모델을 활용하여 다양하고 복잡한 비선형적인 문제에 효과적으로 연관관계를 조직화 할 수 있는 인공신경망에 관한 연구가 근래에 공학의 넓은 분야에서 도입되고 그에 따른 많은 성과가 나타나고 있다. 본 연구에서는 설계자의 판단력과 경험에 의존 하던 기존의 예비구조설계 단계에 효과적인 인공신경망을 적용하여 예비 구조설계 단계에 컴퓨터를 이용한 정형화된 방법을 제시하고자 한다. 이를 위해 각 구조물의 일반적인 설계과정에 따른 다단계 신경망을 제시하고 인공신경망의 학습은 역전파알고리즘과 유전알고리즘을 적용하여 예비구조설계의 원형을 구현한다. 이와 같이 구성된 다단계 신경망을 사장교의 예비구조설계 단계에 활용하여 본 연구의 적용성과 두가지 학습기법에 따른 결과를 비교 분석 한다.

• 경영과정보연구
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• 제2권
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• pp.311-334
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• 1998

By decreasing in CPU price and development of computer assembling technology, personal computer fake a good chance to accelerate its supply. Recently, as being introduced new computing technology so called multi media, teaming assist system which is based on single media such as studying book, cassette tape, video tape, or something else is rapidly being replaced by new assist education system based on multi media in which it is operated by the personal computer. In the computer assist education system, there is an evaluation module which appraise learner's study level into the next study strategy. At the view of this point, this part is very important. In this part, there are some factors like Importance, complexity, or difficulty which commonly include fuzzy factors in our surrounding. But until now, we are still out of the level to handle the evaluation module adequately among the some studies. In this study, we would like to suggest a new module that evaluate learning achievement of ICAI which have a variety of fuzzy environment. We combine Independent fuzzy environment like importance, complexity, difficulty into making total evaluation of learner's achievement. By the result, with expressing by linguistic form, this study can provide the theoretical basis in which we will be able to carry out sentence toward evaluation among elementary school.

• 인터넷정보학회논문지
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• 제24권4호
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.