• Korean Journal of Social Welfare
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• v.47
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• pp.148-177
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• 2001

Service learning usually has two aspects. One aspect is associated with applying class learning to related fields. The other aspect is associated with challenging and dynamic volunteering experiences. This study examined experiences of 70 social work students who took service learning courses at a university. After the courses, these students were asked regarding (1) evaluation and satisfaction of overall service learning experiences, (2) evaluation of service learning contents and the following activities, (3) the process of volunteering activities, and (4) the differences between service learning courses and other regular courses. Results were as follows: students generally regarded service learning experiences as positive; students reported understanding of social work practice and learning of professional skills; however, the service learning courses seemed to be very demanding in time and adjusting personal schedules; teamwork among students seemed to be good, especially in cooperation and emotional support; however, some students reported struggling experiences in allocating roles among team members; finally, the relationship between students and social workers at the agencies and the coordination of community resources seemed to be weak. In order for service learning courses to be more effective, this study presented some suggestions in the conclusion.

• Journal of Technologic Dentistry
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• v.44 no.4
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• pp.132-143
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• 2022

Purpose: The study aims to analyze differences in the satisfaction level of dental technology students regarding in-person and online classes. It also aims to provide fundamental resources for the improvement of major subject class methods that will improve students' self-directed learning abilities, thereby affecting their class satisfaction. Methods: In this study, a self-administered questionnaire was conducted from November 8 to November 30, 2021, for 256 dental technology students. The collected data were analyzed using the IBM SPSS Statistics ver. 21.0 statistical program. Frequency and percentage, mean, standard deviation, t-test, ANOVA, post-hoc test, correlation analysis, and linear regression analysis were performed to analyze the data. Results: In the self-directed learning abilities, the attitude of the learners was shown to have the highest positive (+) correlation in both in-person and online classes, with a statistically significant effect (p<0.001) on class satisfaction in major subject classes. Moreover, the explanatory power of the model was 52.2% and 39.7%, respectively. Conclusion: We concluded from the study that there is a need for professors to improve teaching methods to increase learners' self-directed learning competence, through problem-based learning, discussion learning, team-based collaborative learning, and mentor-mentee learning, thereby enabling learners to lead classes themselves.

• Journal of East Asia Management
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• v.4 no.2
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.851-865
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• 2008

Recently, the Web 2.0 and RSS techniques have gained enormous attention in the Internet and e-Learning fields. In this paper, we design a RSS system on the Web 2,0 base for e-Learning, and implement the educational RSS information sharing system for back-to-basic curriculum of secondary schools that is called EduRSS. Our EduRSS can create web feed file with RSS format from learning blogs for back-to-basic curriculum, and share it with other users conveniently. In addition, it also provide the recommendation function to improve the reliability of the RSS feed resources.

• Education of Primary School Mathematics
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• v.3 no.1
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• pp.37-45
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• 1999

This paper investigates the comparative effectiveness of two kinds of instructional methods in transfer of mathematics learning: one based on the situated cognition, ie. situated learning and the other based on traditional learning. Two classes of second graders studied the instruction about 3-digit addition and subtraction. After that, they completed two written tests and a real situation test. As a result. no significant differences were found between the two group's performance on the written test 1 and real situation test. But the situated learning group performed significantly better on the performance of story problem than traditional group. This result indicated that the situated learning made improvement in transfer of mathematic loaming. As a result of interviews with 12 children, the situated loaming group's children were able to use contextual resources in solving real situation as well as story problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.6
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• pp.69-78
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• 1982

Traditional CVP (Cost-Volume-Profit) analysis employs linear cost and revenue functions within some specified time period and range of operations. Therefore CVP analysis is assumption of constant labor productivity. The use of linear cost functions implicity assumes, among other things, that firm's labor force is either a homogenous group or a collection homogenous subgroups in a constant mix, and that total production changes in a linear fashion through appropriate increase or decrease of seemingly interchangeable labor unit. But productivity rates in many firms are known to change with additional manufacturing experience in employee skill. Learning curve is intended to subsume the effects of all these resources of productivity. This learning phenomenon is quantifiable in the form of a learning curve, or manufacturing progress function. The purpose d this study is to show how alternative assumptions regarding a firm's labor force may be utilize by integrating conventional CVP analysis with learning curve theory, Explicit consideration of the effect of learning should substantially enrich CVP analysis and improve its use as a tool for planning and control of industry.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.40-50
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• 2019

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.5
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• pp.638-644
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• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.