• Journal of Korean Academy of Nursing Administration
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• v.12 no.1
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• pp.140-150
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• 2006

Purpose: The purpose of this study was to determine learning styles and preferred learning methods of clinical nurses. Method: Data were collected from 735 nurses at one university hospital in Seoul. Learning style inventory, a self-report questionnaire was completed by the subjects. Result: Learning styles of nurses were accommodator 35.9%, diverger 30.4%, converger 18.2%, assimilator 15.5%. Learning styles varied significantly with clinical practice area and academic background. Furthermore, RO(reflective observation) learning mode varied significantly according to the clinical practice area. AC(abstractive conceptualization) learning mode varied significantly with job position. AC and AE(active experimentation) learning modes varied significantly according to the academic background and preferred learning method. Preferred learning methods were lecture 24.8%, clinical practice 23.1%, self-directed learning 21.5%, audiovisual education 16.7%, and group discussion 13.9%. Preferred learning methods varied significantly with learning styles and career. Lecture was preferred in diverger and self-directed learning was preferred in assimilator. Clinical practice was preferred in accommodator and converger. Conclusions: This study suggested that clinical education should be applied to nurses after examining learning styles and preferred learning methods. In conclusion, to identify the nurses' learning styles could be helpful for developing the effective educational skill.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.1
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• pp.29-34
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• 2012

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop a load modeling method based on Extreme Learning Machine(ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate active and reactive powers than conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.169-189
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• 2005

In order to derive a better classifier with a limited number of training examples, active teaming alternately repeats the querying stage fur category labeling and the subsequent learning stage fur rebuilding the calssifier with the newly expanded training set. To relieve the user from the burden of labeling, especially in an on-line environment, it is important to minimize the number of querying steps as well as the total number of query examples. We can derive a good classifier in a small number of querying steps by using only a small number of examples if we can select multiple of diverse, representative, and ambiguous examples to present to the user at each querying step. In this paper, we propose a cluster-based batch query selection method which can select diverse, representative, and highly ambiguous examples for efficient active learning. Experiments with various text data sets have shown that our method can derive a better classifier than other methods which only take into account the ambiguity as the criterion to select multiple query examples.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• Korean journal of medical education
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• v.30 no.4
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• pp.359-364
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• 2018

Purpose: Learning physiological concepts and their practical applications in the appropriate contexts remains a great challenge for undergraduate medical students. Hence the present study aimed to analyze the learning experience of undergraduate medical students during an active learning process of 'preparation of models' depicting physiological concepts. Methods: A total of 13 groups, involving 55 undergraduate medical students with three to five individuals in each group, were involved in model preparation. A total of 13 models were exhibited by the students. The students shared their learning experiences as responses to an open-ended questionnaire. The students' responses were analyzed and generalized comments were generated. Results: Analysis of the results showed that the act of 'model preparation' improved concept understanding, retention of knowledge, analytical skills, and referral habits. Further, the process of 'model preparation' could satisfy all types of sensory modality learners. Conclusion: This novel active method of learning could be highly significant in students' understanding and learning physiology concepts. This approach could be incorporated in the traditional instructor-centered undergraduate medical curriculum as a way to innovate it.

• Journal of Engineering Education Research
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• v.15 no.5
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• pp.54-59
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• 2012

It is reported that the constructivistic learning method (CLM) enhances the understanding of the students in the learning process, especially in engineering classes. In CLM-based classes, the students can take the initiative in the learning process, which is called the student-centered model of the learning process. This is different from the traditional learning method based on the teacher-centered model, where a teacher plays the central role in the learning process of students. The authors have applied the method of CLM to one of the Engineering classes, namely production planning and inventory control (PPIC) class for undergraduate students. The PPIC class provides multimedia-based study materials and factory visits as well as regular lecture sections to cover the whole subject of inventory control theory and practice. In the review sessions, students are divided into several groups, and question-and-answer discussions were actively carried out among these groups under the support of the teacher as a facilitator. It was observed that the student engagement in the class was very active compared to the conventional lecture-based classes. As for further support of students understanding on the subject, simulation-based materials are also under study for the class. This paper presents the review of case study of CLM-based PPIC class and discusses the feasibility of simulation-based study materials for further improvement of the class.

• Journal of the Korean Home Economics Association
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• v.48 no.8
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• pp.51-65
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• 2010

This research explored the importance of conflict resolution in communication between adolescents and their parents. A teaching and learning plan on communication using the Active listening, I-message method and no-lose conflict resolution method was developed and applied to teaching the 'Communication among Family Members' unit in the middle school home economics and technology textbook. The experimental class of 203 students using the active listening, I message and no-lose conflict resolution method in communication was compared with a comparative class of 163 students using only the active listening and I message method, investigating the amounts of communication, conflicts, functional communication and degree of satisfaction the students perceived regarding relationships with their parents via pre and post tests. Both groups showed significant increases in their amounts of communication and functional communication. However, only the experimental class showed an increase in their degree of relationship satisfaction with their parents. Both groups evaluated the lesson positively.

• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.15-21
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• 2018

The purpose of this study is to design a Content-based Korean Class model using Flipped learning for foreign students. The class model that presents on this paper will lead the language learning through content learning, also it will be enable the student more active and to have an initiative in the class. Prior to designing a Content-based Korean Class model using Flipped learning, the concepts and educational significance and characteristics of flip learning were reviewed through previous studies. Then, It emphasizes the necessity of teaching method adapting Flipped learning to Content-based teaching method in Korean language education. It also suggests standards and principles of composition in Contents-based teaching method using Flipped learning. After designing the instructional model based on the suggested standards and principles, it presents a course of instruction about how learning methods, contents and activities should be done step by step. The Content-based Korean class model using the Flipped learning will be an alternative approach to overcome the limitations of teacher-centered teaching methods and lecture-teaching methods which are the dominant of present classroom environment.

• Korean Journal of Clinical Pharmacy
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• v.23 no.4
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• pp.291-299
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• 2013

Purpose: Problem-based learning (PBL) has been adopted to foster active and self-directed learning and enhance critical thinking and problem-solving skills in many health-care academic disciplines in Korea. Interest in PBL has rapidly grown with a 6 year pharmacy degree program in Korea. The objective of this study was to evaluate feasibility of PBL, student satisfaction and academic performance with a self-assessment survey questionnaire. Method: Sixty students participated in the PBL for pharmacotherapy course. Average scores from student self-assessment on participation, satisfaction, and academic performance were $3.85{\pm}0.55$, $2.94{\pm}1.04$, $3.09{\pm}0.91$ out of 5 point lickert scale (1-do not agree at all, 5-agree completely), respectively. Results & Conclusion: The level of participation was positively correlated with improvement of communication skill in academic performance (correlation coefficient 0.27, p=0.037). In the quality analysis of the cases provided for PBL, students who participated more in the PBL greatly agreed the cases given were appropriate to learn fundamental knowledge for each disease state. The students disagreed that PBL was fun. The students stated that PBL was good to experience self-directed learning and clinical context beforehand but too time-consuming to devote and too demanding to commit. Lack of facilitator and insight on active learning should be rectified for successful launch of PBL in Korean pharmacy education.