• Journal of The Korean Association For Science Education
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• v.32 no.6
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• pp.1072-1086
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• 2012

Science, Technology, Engineering, and Mathematics (STEM) education in the U.S. has been identified as a significant national reform in K-16 education and curriculum in order to prepare students for the global economy of the 21st century. Korea has been facing very similar challenges to improve science, technology and mathematics education, in particular, the affective aspect of learning science and mathematics. Science, Technology, Engineering, Arts, and Mathematics (STEAM) education has become a crucial issue in Korean education system. The major purpose of this exploratory study is to inform the exemplary framework of STEAM education in the U.S. for Korea and to provide descriptive and analytical accounts on STEAM teaching and learning as an innovative integrated convergence education. This study integrates the outcomes of research papers on STEM education and recent literature. It employs content analysis methodology qualitatively by analyzing and synthesizing the findings, conclusions, discussions, and recommendations of accumulated research works related to STEM/STEAM education. This study will help gain a stronger sense of the STEAM framework and will guide to develop the educational programs for Korea.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.535-549
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• 2024

Facial expressions (FEs) serve as fundamental components for human emotion assessment and human-computer interaction. Traditional convolutional neural networks tend to overlook valuable information during the FE feature extraction, resulting in suboptimal recognition rates. To address this problem, we propose a deep learning framework that incorporates hierarchical feature fusion, contextual data, and an attention mechanism for precise FE recognition. In our approach, we leveraged an enhanced VGGNet16 as the backbone network and introduced an improved group convolutional channel attention (GCCA) module in each block to emphasize the crucial expression features. A partial decoder was added at the end of the backbone network to facilitate the fusion of multilevel features for a comprehensive feature map. A reverse attention mechanism guides the model to refine details layer-by-layer while introducing contextual information and extracting richer expression features. To enhance feature distinguishability, we employed islanding loss in combination with softmax loss, creating a joint loss function. Using two open datasets, our experimental results demonstrated the effectiveness of our framework. Our framework achieved an average accuracy rate of 74.08% on the FER2013 dataset and 98.66% on the CK+ dataset, outperforming advanced methods in both recognition accuracy and stability.

• 대한공업교육학회지
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• v.35 no.2
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• pp.70-81
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• 2010

The purpose of this study is to introduce formation 'International framework curriculum for a Master Degree for TVET teachers and lectures' to present implications about fostering Chinese vocational teachers and to analyze the contents related to it. In 2004, UNFSCO composed formation International framework curriculum for a Master Degree for TVET teachers and lectures ("framework curriculum") to improve the ability of professionals in the vocational education and training fields including teachers and training leaders as well as to promote international academic exchange. Universities which introduce the framework curriculum should form specialized committee and carry out education considering the specific situation including other universities' situation, students' ability, educational certification system, etc. The framework curriculum should include the latest trends of the development of international vocational education science and carry out united educational learning between several internal or external high schools. UNFSCO tries to promote the development of educational learning and study of basic departments of vocational education such as vocational educational learning theory, vocational science, etc through the framework curriculum and to improve knowledge of vocational educational teachers and realize specialization of them. The number of universities that established the master's degree of vocational education in China is approx. 20 and the number of students that they collect every year. As for the plans of the master's degree of vocational teachers in each university, the courses about the practical problems like educational courses and educational learning are insufficient. But the framework curriculum thinks that educational learning of application theory is more important and emphasizes practice about the specific area and educational learning much more. Utilization of preceding experiences of advanced countries has the important meaning in search of models that foster Chinese vocational teachers and departmental system. The framework curriculum implies several useful points in installment of majors and educational process of the process that fosters Chinese vocational teachers.

• Journal of Technology Innovation
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• v.5 no.2
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• pp.22-47
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• 1997

This paper suggests a model of Living & Learning Nation as a new ploicy framework. It is a combination of Living Nation and Learning Nation. Living Nation model takes the nation as a living entity composed of spirit, resource, and communication : it grows but healthy and balanced growth is needed, its organs are closely connected, it has a circulation system, the 'spirit' factor plays the central role, etc.. Learning Nation model is a national level version of learning organization concept. The model defines new perspectives on the objectives, span of means, and the role of government in S&T policy. Therefore, the model can be used to give new insights to policymakers of developing countries facing the knowledge-based economy.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1468-1473
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• 2005

In this paper, I proposed the INTelligent Agent System by Kohonen's Self Organizing Neural Network (INTAS). INTAS creates each user's profile from the information. Based on it, learning community grouping suitable to each individual is automatically executed by using unsupervised learning algorithm. In INTAS, grouping and learning are automatically performed on real time by multiagents, regardless of the number of learners. A new framework has been proposed to generate multiagents, and it is a feature that efficient multiagents can be executed by proposing a new negotiation mode between multiagents..

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.429-440
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• 2001

To achieve a computer tutor framework with high learning effects as well as practicality, the goal of this research has been set to developing an intelligent tutor for problem-solving in mathematics domain. The maine feature of the CyberTutor, a computer tutor developed in this research, is the facilitation of a learning environment interacting in accordance with the learners differing inferential capabilities and needs. The pedagogical information, the driving force of such an interactive learning, comprises of tutoring strategies used commonly in various domains such as phvsics and mathematics, in which the main contents of learning is the comprehension and the application of principles. These tutoring strategies are those of testing learners hypotheses test, providing hints, and generating explanations. We illustrate the feasibility and the behavior of our propose framework with a sample problem-solving learning in geometry. The proposed tutorial framework is an advancement from previous works in several aspects. Firstly, it is more practical since it supports handing of a wide range of problem types, including not only proof types but also finding-unkown tpes. Secondly, it is aimed at facilitating a personal tutor environment by adapting to learners of varying capabilities. Finally, learning effects are maximized by its tutorial dialogues which are derived from real-time problem-solving inference instead of from built-in procedures.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.