• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3658-3679
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• 2022

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers.

• Educational Technology International
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• v.23 no.1
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• pp.1-39
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• 2022

We study emotion regulation in a distant CABLe (Collaborative Argumentation Based-Learning) setting at university. We analyze how students achieve the group task of synthesizing the literature on a topic through scientific argumentation on the institutional Moodle's forum. Distinguishing anticipatory from reactive emotional regulation shows how essential it is to establish and maintain a constructive working climate in order to make the best out of disagreement both on social and cognitive planes. We operationalize the analysis of anticipatory emotional regulation through an analytical grid applied to the data of two groups of students facing similar disagreement. Thanks to sharp anticipatory regulation, group 1 solved the conflict both on the social and the cognitive plane, while group 2 had to call out for external regulation by the teacher, stuck in a cyclically resurfacing dispute. While the institutional digital environment did afford anticipatory emotional regulation, reactive emotional regulation rather occurred through complementary informal and synchronous communication tools. Based on these qualitative case studies, we draw recommendations for fostering distant CABLe at university.

• Journal of Engineering Education Research
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• v.14 no.4
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• pp.11-19
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• 2011

The purpose of this study was to identify learning strategies by learning style of first-year engineering students in order to find implications for teaching and learning strategies in engineering education. This study was conducted with 273 first-year students in two universities in Korea. Following were the results: First, there were Sensing learners(72.2%), Visual learners(84.6%), Reflective learners(64.8%), and Sequential learners(58.2%) and the level of learning strategies was 3.28(SD=0.38). Secondly, the finding revealed that there was only significant difference in learning strategies on Information processing dimension and Active students demonstrated higher level of learning strategies than Reflective students. To be more specific, there were significant differences in cognitive, meta-cognitive, and internal and external management. For engineering education, implications for teaching strategies in classroom and self-regulated learning strategies were discussed.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.113-124
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• 2020

Social movements to improve the performance of buildings through remodeling of aging apartment houses are being captured. To this end, the remodeling construction cost analysis, structural analysis, and political institutional review have been conducted to suggest ways to activate the remodeling. However, although the method of analyzing construction cost for remodeling apartment houses is currently being proposed for research purposes, there are limitations in practical application possibilities. Specifically, In order to be used practically, it is applicable to cases that have already been completed or in progress, but cases that will occur in the future are also used for construction cost analysis, so the sustainability of the analysis method is lacking. For the purpose of this, we would like to suggest an automated estimating method. For the sustainability of construction cost estimates, Deep-Learning was introduced in the estimating procedure. Specifically, a method for automatically finding the relationship between design elements, work types, and cost increase factors that can occur in apartment remodeling was presented. In addition, Monte Carlo Simulation was included in the estimation procedure to compensate for the lack of uncertainty, which is the inherent limitation of the Deep Learning-based estimation. In order to present higher accuracy as cases are accumulated, a method of calculating higher accuracy by comparing the estimate result with the existing accumulated data was also suggested. In order to validate the sustainability of the automated estimates proposed in this study, 13 cases of learning procedures and an additional 2 cases of cumulative procedures were performed. As a result, a new construction cost estimating procedure was automatically presented that reflects the characteristics of the two additional projects. In this study, the method of estimate estimate was used using 15 cases, If the cases are accumulated and reflected, the effect of this study is expected to increase.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.982-990
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• 2018

In recent years, many wearable devices and mobile apps related to life care have been developed, and a service for measuring the movement during walking and showing the amount of exercise has been provided. However, they do not measure walking in detail, so there may be errors in the total calorie consumption. If the user's behavior is measured by a multi-axis sensor and learned by a machine learning algorithm to recognize the kind of behavior, the detailed operation of walking can be autonomously distinguished and the total calorie consumption can be calculated more than the conventional method. In order to verify this, we measured activities and created a model using a machine learning algorithm. As a result of the comparison experiment, it was confirmed that the average accuracy was 12.5% or more higher than that of the conventional method. Also, in the measurement of the momentum, the calorie consumption accuracy is more than 49.53% than that of the conventional method. If the activity recognition is performed using the wearable device and the machine learning algorithm, the accuracy can be improved and the energy consumption calculation accuracy can be improved.

• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.603-612
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• 2011

This study suggested an educational curriculum(12 class periods) in order for higher level elementary school students to learn programming in discretionary activity hours using a line tracer and evaluated achievement level based on the outcome of learning by class period to assess the possibility of success. As a result, it could confirm that the programming learning using the line tracer was more excellent in creativity than the computer programming learning. In addition, it has been found that the programming learning method using the line tracer had a potential to be successful as a new creative tool that could replace the computer.

• Korean Journal of Child Studies
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• v.32 no.2
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• pp.71-85
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• 2011

This study was carried out in order to better understand how cooperative learning effects the geometric understanding of young children. The geometry tasks used in the study included the geometric relationship between two dimensional shapes and three dimensional shapes, coordination, symmetry and transformation visualization and spacial reasoning. The subjects were composed of children aged five years and were taken from two kindergartens in a relatively new city close to Seoul. The experimental group of children the comparative learning in geometry. The comparative group of children were enrolled in a kindergarten that uses an the intergrated curriculum. The results indicated that cooperative learning impacted positively on the children's understanding of geometry. The specific results are as follows : The scores that the experimental acquired were higher in terms of p < .001 level. than the scores of the comparative group studying the geometric relationships between two dimensional shapes and three dimensional shapes, coordination, symmetry and transformation visualization & spacial reasoning.

• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.116-124
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• 2006

Mentoring is defined as a sustained relationship between a mentor and a mentee. Through continued involvement, the mentor offers support, guidance, and assistance as the mentee faces new challenges, or works to correct earlier problems. A mentoring for cooperative learning has many merits including higher order thinking, collaborative competencies, socialization and development. In this paper, a U(Ubiquitous)-CoMM(Community of mentor & mentee) system was supposed to design an instructional learning strategy using cyber community of mentor & mentee in a ubiquitous environment. The proposed system provides participants with campus mentoring program in which they share their experience and expertise. By experimental result showed that the proposed system is effect in education about cooperative learning than existing system.

• Perspectives in Nursing Science
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• v.9 no.2
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• pp.136-145
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• 2012

Purpose: The purpose of this study was to verify the educational effectiveness of the Objective structured clinical examination (OSCE) module development activities on nursing students in the areas of performance skill, knowledge, self-directed learning readiness, and problem solving ability for medication skill. Methods: This study was a nonequivalent control group non-synchronized post-test design. The subjects (N=47), who agreed to participate in this study, were assigned to either the experimental (n=24) or control group (n=23). The experimental group was trained with OSCE module development activities for four days. The control group was trained with a traditional demonstration and practice class for the same amount of time as the experimental group. Medication performance skill and knowledge tests and surveys were done to measure self-directed learning readiness, and learning satisfaction after the experimental treatments. Results: The experimental group which participated in the OSCE module development activities showed significantly higher performance skill, self-directed learning readiness, and problem solving ability for skin test and insulin medication than that of the control group of traditional education. Conclusion: It is recommended to use the OSCE module development activities for nursing students in nursing education-learning in order to improve nursing skills.