• 대한수학교육학회지:수학교육학연구
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• 제25권1호
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• pp.59-74
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• 2015

본 연구의 주된 목적은 수학 학습에 대한 담론적 접근의 한 배경이 되는 후기 비트겐슈타인 철학이 무엇인지를 상세히 밝히는 것이다. 이는 수학 학습에 대한 담론적 접근을 철학적 측면에서 이해할 수 있도록 그리고 일관성 있게 사용할 수 있도록 돕는다는 점에서 그 의의가 있다. 본 연구에서는 먼저 비트겐슈타인의 전-후기 철학을 구분하고, 언어와 세계에 대한 그의 관점이 어떻게 바뀌었는지에 초점을 두어 설명함으로써 후기 비트겐슈타인 철학을 논의하였다. 다음으로 수학 학습에 대한 담론적 접근이 무엇인지와 이 접근에서 그의 후기 철학이 사용되고 있는 논리를 분명히 하였다. 이 논의들을 토대로 후기 비트겐슈타인 철학이 수학교육에 줄 수 있는 시사점에 대해 논의하고 후속연구 주제를 제안하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.35.3-35
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• 2002

Reinforcement Learning (RL) is one of machine learning methods and an RL agent autonomously learns the action selection policy by interactions with its environment. At the beginning of RL research, it was limited to problems in environments assumed to be Markovian Decision Process (MDP). However in practical problems, the agent suffers from the incomplete perception, i.e., the agent observes the state of the environments, but these observations include incomplete information of the state. This problem is formally modeled by Partially Observable MDP (POMDP). One of the possible approaches to POMDPS is to use historical nformation to estimate states. The problem of these approaches is how t..

• 기술혁신학회지
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• 제2권1호
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• pp.1-15
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• 1999

This study aims at integrating the sociological study of technology and the economic study of technological learning. It is argued that the sociological approaches of innovation have some strong points in criticizing technological determinism, but have some weak points in explaining how the knowledge base for innovation is accumulated. On the contrary, the economic approaches of innovation have strong points in explaining technology accumulation, but ignore socio-political process of innovation. This study suggests the model which integrates the socio-political process and technological loaming process.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• Educational Technology International
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• 제11권2호
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• pp.25-41
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• 2010

Challenges brought by applications of advanced technologies in education call for new approaches that can best ensure the provision of quality e-learning experiences. This paper presents an ecological approach as one of such approaches to quality assurance in e-learning that can monitor, assess and improve the effectiveness and the links between the various elements of e-learning. The ecological model for QA in e-learning emphasizes interrelation transactions between elements (e.g. providers, learners, cultures and policies) and systemic integration of those elements, and stresses that all these elements within a QA system play an equal role in maintaining balance of the whole. The model focuses attention both on individual and societal/cultural environmental factors as cornerstones for QA efforts in e-learning. It addresses the importance of QA efforts directed at changing QA transactions from provider-centered to 'all stakeholder-oriented', from one-size-fits-all model to 'globally oriented, locally adaptive model' and from control framework to 'culture creation framework'.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

• 한국콘텐츠학회논문지
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• 제16권12호
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• pp.35-42
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• 2016

본 연구는 액션러닝을 적용한 간호과정 학습프로그램이 간호학과 학생들의 문제해결능력과 자기주도적 학습능력에 미치는 효과를 규명하기 위한 비동등성 대조군 전후설계 실험연구이다. 연구대상은 G시와 J도에 소재한 4년제 간호학과 2학년 중 실험군 53명, 대조군 52명으로 총 105명이었다. 주 2회, 4주간 프로그램을 실시하였으며 실시 전과 후에 문제해결능력과 자기주도적 학습능력를 측정하였다. 측정된 결과는 ${\chi}^2$-test와 Chi-Square test, t-test, paired t-test로 분석하였다. 연구결과 액션러닝 학습프로그램은 문제명료화, 원인분석, 대안개발, 계획/실행, 수행평가로 구성된 학습자의 문제해결능력을 증진시켰고 학습계획, 학습실행, 학습평가로 구성된 자기주도적 학습능력을 증진시켰다. 그러므로 본 연구는 학습방법으로의 액션러닝이 간호학과 학생들의 간호과정 수업을 하는데 효과적임을 파악한 점에서 연구의 의의를 지니며, 향후 간호관련 전공학문 분야의 효과적인 교수법으로 확대적용 할 수 있을 것으로 기대한다.

• 전자통신동향분석
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• 제33권5호
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• pp.13-23
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• 2018

Deep learning is a promising solution to a number of complex problems based on its inherent capability to approximate almost all types of functions without the demand for handcrafted feature extraction. New wireless transmission and access schemes based on deep learning are being increasingly proposed as substitutes for existing approaches, providing a lower complexity and better performance gain. Among such schemes, a communications system is viewed as an end-to-end autoencoder. The learning process applied in autoencoders can automatically deal with some nonlinear or unknown properties in communications systems. Deep learning can also be used to optimize each processing block for required tasks such as channel decoding, signal detection, and multiple access. On top of recent related research trends, we suggest appropriate research approaches for communications systems to adopt deep learning.

• 디지털산업정보학회논문지
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• 제9권4호
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• pp.111-118
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• 2013

Today, universities in Korea give a great effort to complement basic college scholastic ability of freshman. Maybe it could be unnecessary effort from university. However, we cannot just leave students who have poor academic performance about 10 to 30% in a university. Universities have to prepare to solve this problem. Thus government and universities investigate a lot of efforts but there is no guarantee. As a result, e-learning could be the best solution to complement poor academic performance of freshman and it also could be the best way to reduce financial burden of university. So, many universities introduce e-learning system and they also support professors to make e-learning content. We need to promote content usage and to improve overall operations. In this paper, we discuss poor academic performance problems under the current middle and high school education systems and review approaches to solve these problems. From this analysis, we propose the design of an effective e-learning system and a method of operation to complement students who achieve poor academic performance.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.26-32
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• 2023

Hajj is a fundamental pillar of Islam that all Muslims must perform at least once in their lives. However, Umrah can be performed several times yearly, depending on people's abilities. Every year, Muslims from all over the world travel to Saudi Arabia to perform Hajj. Hajj and Umrah pilgrims face multiple issues due to the large volume of people at the same time and place during the event. Therefore, a system is needed to facilitate the people's smooth execution of Hajj and Umrah procedures. Multiple devices are already installed in Makkah, but it would be better to suggest the data architectures with the help of machine learning approaches. The proposed system analyzes the services provided to the pilgrims regarding gender, location, and foreign pilgrims. The proposed system addressed the research problem of analyzing the Hajj pilgrim dataset most effectively. In addition, Visualizations of the proposed method showed the system's performance using data architectures. Machine learning algorithms classify whether male pilgrims are more significant than female pilgrims. Several algorithms were proposed to classify the data, including logistic regression, Naive Bayes, K-nearest neighbors, decision trees, random forests, and XGBoost. The decision tree accuracy value was 62.83%, whereas K-nearest Neighbors had 62.86%; other classifiers have lower accuracy than these. The open-source dataset was analyzed using different data architectures to store the data, and then machine learning approaches were used to classify the dataset.