• Management & Information Systems Review
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• v.35 no.4
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• pp.185-211
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• 2016

This study examined the impact of learning organization activities on burnout and the moderating effect of supervisor trust in a learning organization. The results of the study shows that among the activities of a learning organization, independent variables in this study, promoting inquiry and dialogue as well as encouraging collaboration and team learning affect burnout. In other words, the dedication of an organization to creating a culture in which various learning approaches are experimented through questioning and giving feedback as well as collaborative learning that can reinforce the effective use of team resources have an impact on reducing emotional exhaustion, which is considered to be at the core of burnout. Plus, these factors reduce impersonalization, which is activated to prevent further emotional exhaustion by dealing with customers, colleagues and jobs in a cold, negative and perfunctory way. In this study, the dimensions of promoting inquiry and dialogue as well as encouraging collaboration and team learning were found to reduce the decline in personal sense of achievement of an employee with a negative assessment of himself or herself derived from a lack of achievement in his or her job. Supervisor trust (integrity, benevolence and ability) had a moderating effect on the relationship between strategic learning leadership and impersonalization/emotional exhaustion. This suggests that the trust of supervisor helps mediate and moderate the emotional exhaustion and impersonalization of organizational members by encouraging leaders to drive change and take the organization to a new direction. The study has provided implications that communication plays an important role in reducing burnout in the learning context such as positive, appreciative inquiry and feedback analysis to identify strength, and that supervisor trust is critical in order to ensure strategic learning leadership exerts greater influence on the organization.

• Korean Medical Education Review
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• v.10 no.1
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• pp.35-42
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• 2008

Purpose : Since PBL was first developed by Howard Barrows at McMaster, it has been adopted as one of the best teaching and learning methods in medical schools throughout the world. However, the educational superiority of PBL relative to traditional approaches is less clear. Given the somewhat extensive resources required for the operation of PBL curriculum, this gives reason for concern. The aim of this study is to review experiences of PBL in other medical schools and learn how to implement PBL in our school. Methods : This study was undertaken in two stages. In the first stage, PBL curricular examples in 7 medical schools (University of Pennsylvania, University of Melbourne, University of Maastricht, McMaster University, Flinders University, Harvard medical school. University of California at L.A.) were collected and summarized. In the second stage, a careful search for articles of journals published since 2000 regarding PBL group assessment, effectiveness of PBL and group facilitation skills was conducted. Results : PBL is generally introduced in a core curriculum in undergraduate medical education. Relating to small group assessment, the perception of students has been well developed. but the current PBL assessment tool needs to be revised, to develop thinking skills of students. The PBL graduates considered themselves as having much better interpersonal skills, better competencies in problem solving and self-directed learning than the non-PBL graduates. Tutors used various techniques to raise awareness, facilitate the group process and direct learning. Conclusions : The following three aspects can be regarded as important in this study. First, to implement PBL in our school more effectively, it might be considered, which curriculum content can be best learned with PBL. Second, to enhance students' thinking skills during PBL, a new assessment tool needs to be developed. Third, tutors' competencies are important to facilitate, group process, so it would be worthwhile including in staff development.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1375-1385
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• 2022

In this paper, we propose a semi-supervised domain adaptation solution to deal with practical face recognition (FR) scenarios where a single face image for each target identity (to be recognized) is only available in the training phase. Main goal of the proposed method is to reduce the discrepancy between the target and the source domain face images, which ultimately improves FR performances. The proposed method is based on the Domain Adatation network (DAN) using an MMD loss function to reduce the discrepancy between domains. In order to train more effectively, we develop a novel loss function learning strategy in which MMD loss and cross-entropy loss functions are adopted by using different weights according to the progress of each epoch during the learning. The proposed weight adoptation focuses on the training of the source domain in the initial learning phase to learn facial feature information such as eyes, nose, and mouth. After the initial learning is completed, the resulting feature information is used to training a deep network using the target domain images. To evaluate the effectiveness of the proposed method, FR performances were evaluated with pretrained model trained only with CASIA-webface (source images) and fine-tuned model trained only with FERET's gallery (target images) under the same FR scenarios. The experimental results showed that the proposed semi-supervised domain adaptation can be improved by 24.78% compared to the pre-trained model and 28.42% compared to the fine-tuned model. In addition, the proposed method outperformed other state-of-the-arts domain adaptation approaches by 9.41%.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.541-548
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• 2014

The goal of this study is to suggest evaluation tools to assess computational thinking(CT) skills in primary robot-based programs. In addition, the researcher has expanded its evaluation approaches to include interpersonal competencies from the socio-cultural perspectives, not just focusing on intrapersonal competencies. In order to pursue the research goal, one-semester robots programs for the fifth graders were designed, and evaluation tools including a learners' CT competencies survey and a learning process monitoring rubric were developed. The results of this study are meaningful because it has expanded the evaluation approaches to connect to the concepts of CT and to include interpersonal aspects from the socio-cultural perspectives.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.398-411
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• 2020

This study explored the case of non-face-to-face classes at colleges initiated in the Corona 19 environment. The scope of this study was to analyze the learning development process of the non-face-to-face class case facing the Corona 19 environment in university classrooms in terms of class and learning content, interaction, assignment activity, and teaching-learning activities. It was conducted for first-year students at H University's Early Childhood Education Department of Startup Basic Course. Study results found that, first, interest in entrepreneurship increased as learning content. Second, as a result of exploring the teaching and learning process, there is no significant difference in understanding the content or achieving learning goals. Third, the most regrettable thing about non-face-to-face teaching-learning is the lack of interaction activities. Fourth, the students finished by adapting to the new non-face-to-face teaching-learning environment. In the future, a non-face-to-face environment platform should be established, and studies that can deal with new perspectives and approaches, such as an educational interaction system including online and offline, should be continued.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.1-6
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• 2020

There are various approaches to evaluate a watermelon sweetness. However, there are some limitations to evaluating cost, watermelon damage, and subjective issue. In this study, we developed a novel approach to predict a watermelon sweetness using reflected sound and the machine learning algorithm. It was observed that higher brix watermelon produced higher spectral power is reflected sound. Based on the spectral-temporal features of reflected sound, the machine learning algorithms could accurately predict the sweetness group at a rate of 83.2 and 59.6 % in 2-groups and 3-groups classification, respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.79-83
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• 2015

The Response to Intervention(RTI) approaches is the method to help students who are at risk for learning difficulties in advance and provide an appropriate level of intervention. In this article, the characteristics of model RTI were reviewed for students with achievement of below basic proficiency level. We considered RTI as supporting system to document students' progress and its applicability for the general educational setting in Korean school. The tier of RTI make it possible the evidence based individual instruction and counseling, differentiated step-by step approach for students with achievement of below basic proficiency level. In conclusion, RTI can be used as educational tools for dealing with improvement of academic subjects learning, behavioral and emotional problem for students with achievement of below basic proficiency level. For building high quality implementing for RTI it is needed the collaboration of teachers, counselors and learning consultants and related educators.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.