• Journal of Digital Convergence
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• v.13 no.1
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• pp.77-83
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• 2015

This study, according as the limits on learning and unsatisfaction about e-learning are emerging and the structural contradictions of the e-learning industry are continuing, was carried out to present the policy alternatives for solving these. As a means for overcoming the limitations of e-learning and the healthy growth of e-learning industry, this study presents the application of Bigdata in e-learning and proposes several practical challenges of policy. Policy action plans are technology development support, professional manpower support, SME application support, legal improvement.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.221-233
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• 2021

This study analyzed the contents of changes in educational policy from the perspective of policy learning, based on the awareness of the lack of academic efforts to identify such phenomena despite repeated policy failures in educational policy. To this end, it has been more than 20 years since the policy was established, but it was analyzed using the policy analysis case of the teacher incentive policy, which still had severe conflicts between the government and teacher unions. As a result of the analysis, first, there were 11 changes in the policy content from the Kim Dae-jung administration to the Moon Jae-in administration. Whenever the government was newly launched, not only the contents of the policy for teacher incentives changed, but also the policy changes continued during the same government period. Second, when analyzing what kind of policy learning took place at the stage of change in each government's policy content, most of them were 'political policy learning' or 'instrumental policy learning'. Rather than a fundamental discussion about the goal of the policy, it has only repeated policy learning that adjusts only the ratio of differential payments to defend the teacher incentive policy. In order to recover from this current situation, this study suggests that it is necessary to present an alternative policy that can change the rigid society of teachers through 'social policy learning', which examines the basic values and strategies of teacher incentive policies.

• Journal of Digital Convergence
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• v.9 no.6
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• pp.197-206
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• 2011

This study proposes the "Smart Learning" as a next generation for e-Learning, and focuses on developing policy for smart learning industry. Especially, this study's purpose is proposing a political agenda for smart learning industry as a knowledge-based industry. This study process for the realization of this purpose is belows: first, analyzing status of industry and policy in decades, second, finding problems and solutions, and finally, proposing the core subjects focused on practical value. And this study also focuses on the development of new policy suitable characteristics of the smart learning.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.72-77
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• 2022

Among the techniques of reinforcement learning, Q-Learning means learning optimal policies by learning Q functions that perform actionsin a given state and predict future efficient expectations. Q-Learning is widely used as a basic algorithm for reinforcement learning. In this paper, we studied the effectiveness of selecting and learning efficient paths by designing policies and rewards based on Q-Learning. In addition, the results of the existing algorithm and punishment compensation policy and the proposed punishment reinforcement policy were compared by applying the same number of times of learning to the 8x8 grid environment of the Frozen Lake game. Through this comparison, it was analyzed that the Q-Learning punishment reinforcement policy proposed in this paper can significantly increase the learning speed compared to the application of conventional algorithms.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.49-56
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• 2015

Korea government has established clear cost estimation standard based on a survey of e-learning contents development cost and presented 'e-Learning Contents Development Cost Estimation Guidelines' that reflect the characteristics of the e-learning industry. However, if there is no institutional support, this guideline and system fails to achieve the purposes and objectives. And it is likely to be facing a dead document. Therefore, the policy foundation is required. This study suggested the following policy; stepwise activation of cost estimation standard, enact announcement and periodically adjustment of cost estimation standard, installation and operation of cost estimation standard operational committee, conjunction with the e-learning industry survey, cultural diffusion of co-owned copyright, systematic monitoring of the e-learning contents development process, research on activating policy of cost estimation standard, conjunction with the standard contract for enhancing policy effectiveness.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.93-99
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• 2011

In this paper, we propose a policy-gradient routing scheme under Reinforcement Learning that can be used adaptive QoS routing. A policy-gradient RL routing can provide fast learning of network environments as using optimal policy adapted average estimate rewards gradient values. This technique shows that fast of learning network environments results in high success rate of routing. For prove it, we simulate and compare with three different schemes.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Journal of dental hygiene science
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• v.23 no.2
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• pp.93-102
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• 2023

Background: To investigate political participation by dental hygiene students and analyze the differences therein based on the learning efficacy of dental hygiene policy. Methods: A total of 239 dental hygiene students who were expected to graduate responded to the survey. The data were collected online using a structured questionnaire consisting of 6 items on general characteristics, 10 on political participation, and 15 on the learning efficacy of dental hygiene policy. Statistical analysis was performed using SPSS 23.0. Political participation based on the learning efficacy of dental hygiene policy was analyzed using independent t-tests, ANOVA, and multiple regression analysis (p<0.05). Results: Among the dental hygiene students, 60.7% voted in all three recent presidential, general, and local elections, and 14.2% did not. For political parties supported, 65.7% responded that they had "no supporting party," and 34.3% indicated that they had a "supporting party." In terms of the level of political participation of dental hygiene students (0~50 points), the average score was 25.8 points, with the average passive political participation (0~25 points) score at 15.6 points and the average active political participation (0~25 points) score at 10.2 points. With an increase in dental hygiene policy learning efficacy, both passive and active political participation showed higher scores (p<0.05). Conclusion: Dental hygiene students showed low political participation. The presence of a supporting party, higher voting participation, and higher learning efficacy of dental hygiene policy were associated with higher passive and active political participation. Therefore, to increase this population's interest in political participation, various opportunities for related learning need to be promoted and provided in academia, leading to the enhancement of their political capabilities. In this manner, dental hygienists should expand their capabilities in various roles such as advocates, policy makers, and leaders.

• 한국디지털정책학회:학술대회논문집
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• 2004.05a
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• pp.639-650
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• 2004