• 대한공업교육학회지
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• v.34 no.2
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• pp.205-229
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• 2009

The purpose of this study is to verify the effect of forming groups according to the brain hemisphere preference on the cooperative problem solving learning achievement in the middle school technology. The subjects of this study were 95 second grade boy students of a middle school in Daejeon and the measurement instrument of the left and right hemisphere preference is the Brain preference Indicator(BPI) which had been developed by Torrance et al(1977) and was adjusted by Ko, Younghee(1991). The academic achievement was analyzed on cognitive, psychomotor and affective domains. Derived results from this research are stated below: First, making groups according that the brain preference is more similar was more effective than making groups according to the high familiarity and the similarity of performance in the academic achievement of psychomotor and affective domains. Second, making groups according that the brain preference is more similar was more effective than making groups according that the brain preference is more diffrent for the academic achievement of affective domains on the cooperative problem solving learning in technology. Third, the academic achievement score of the right hemisphere preference group is higher than the score of the population in three domains. Also, the academic achievement score of the right hemisphere preference group is higher than the score of the left hemisphere preference group.

• Hwankyungkyoyuk
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• v.10 no.2
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• pp.51-62
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• 1997

The objective of this study was to review the meaning and problems of Scientific Knowledge and Ethical Value in Environmental Education. The ultimate goal of environmental education is shaping proenvironmental human behavior. The factors of human behavioral decision making are ideology, value, attitude and behavioral intentions. Ideology is a kind of belief system used by social groups to interpret their social world. The main elements of belief system are knowledge and value. The traditional thinking in education has been that we can change behavior by making human beings more knowledgeable and more valuable. In environmental education, the aim of scientific inquiry is to analysis cause-effect relation of human beings behavior and environmental phenomenon, and ethical education is to change the mind of human beings from zero-sum to positive-sum about the relations between human beings and natural environments. But, there are many problems of knowledge education and value education in environmental education. For example scientific knowledge without ethical value is dangerous to environment protection, and ethical value without scientific knowledge is vague. Therefore, we must recognize that the relationship of ethical value and scientific knowledge is not substitutional but complementary. The teaching-learning methods which can integrate knowledge and value in environmental education are rational decision making model. For this model, we can construct teaching contents with inquiry materials. To earn the benefits of specialization among several subjects in environmental education, social studies can focus on social science knowledge and decision making, science education can focus on pure natural science knowledge and scientific investigation, moral education can focus on problems of ethical value system, home economics can focus on practical action and environmental education(Environments in middle school, Ecology and Environments in high school) can integrate social-national science knowledge and ethical value in broad perspective about human beings and ecosystem. That is the method to protect from law of diminishing marginal utility of learning in environmental education.

• STI Policy Review
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• v.9 no.1
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• pp.30-54
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• 2018

The government of Indonesia has initiated the Master Plan of National Research (RIRN) 2017-2045 as a policy umbrella of national research activity. The initiative has been in place since 2015, yet the process required a long period of coordination. And with the extensive movement of evidence-based policymaking (EBPM), there has been a call of expectation towards policymakers to accurately use scientific evidence in their policymaking process. However, the complexity of policymaking process renders the ideal notion of EBPM questionable. This research attempts to understand how the EBPM as an idea can shape the interactions of actors in the policymaking process by using the discursive institutionalism as the analytical framework. By conducting ten interviews with actors involved in the making of RIRN and close examination of the policy documents for content analysis, this research describes the institutional features of EBPM discourse in Indonesia, which are reflected in the interactions of policy actors in the policymaking process of RIRN. This research also offers descriptive and learning narratives on the role of discourse in the policymaking process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• Journal of Drive and Control
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• v.19 no.4
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.111-118
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• 2016

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.

• The Journal of Korean Association of Computer Education
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• v.8 no.2
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• pp.53-60
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• 2005

The teaching plan is an important material for teaching and learning, and it goes through many hardships to make out it. Although these materials have been shared through web and referenced for making their teaching plan by other teachers, it has been little utilized yet in learning. In this paper, we proposed a strategy for utilizing the teaching plan as a useful material in learning and had developed the web-based system for managing and utilizing the teaching plan based on the strategy.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.216-225
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• 2020

The purpose of the youth employment academy is to resolve the occurrence of job miss matches due to college curriculum, which are far from the demand of industrial field. Despite the government's efforts, college students' willingness to get a job has been on the decline recently, making it also important to improve their will to get a job or desire to achieve a job, in addition to delivering expertise to job seekers. Therefore, this study investigated to identify the learning environment characteristics of the youth employment academy and examine which of these factors could improve the performance of job seeking activities by encouraging learning motivation. Therefore, significant implications could be derived through combining the field factors with theory and hypothesis verification.

• Smart Media Journal
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• v.10 no.2
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• pp.48-54
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• 2021

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians' policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.