• Journal of vocational education research
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• v.36 no.4
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• pp.41-56
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• 2017

The purpose of this study is to analyze the level of current difference of education needs for instructional competency of lifelong education instructor and the level of importance of lifelong education for drawing priority. Through the literature review, this is divided the lifelong education instructor's competencies such as planning, implementation, management and support and analyzed the current level and importance with 35 items through t-test analysis. The priority for education needs is applied to Borich and the Locus for Focus model simultaneously. According to result for study, the largest item of competency for lifelong education instructor is verified with the current level and importance for building of social networking and managing competency. The top priority item of education needs for instructional competency of lifelong education instructor is located in the first quadrant of model and the Locus for Focus model, according to priority in needs for Borich and was showed in program competency. The second items in priority were derived by learning resources, information gathering, competency for focus development, equitable evaluation for student, competency for building team work. Therefore, these competencies are considered as factors for priority of lifelong instructor and will be developed in personal and organizational development.

• Korean Journal of Heritage: History & Science
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• v.54 no.3
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• pp.106-125
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• 2021

This paper presents several reasons for the necessity of archaeological hands-on training and strategies for its implementation. First, it is necessary to produce a specialized manual for local cultural heritage education that can enhance the specialization and educational effectiveness of archaeological experience education. In addition, in order to secure professionalism in hands-on education and conduct it systematically, the ability of instructors to conduct education is important, so instructor competence reinforcement education needs to be conducted regularly. In addition, hands-on education needs a strategy of planning and content development of archaeological education programs, with consideration given to the subjects of learning, and the establishment of a cooperative network. It is time to cooperate with various experts to establish an education system necessary for cultural heritage education in the region and develop customized content for local archaeological heritage supplementary textbooks. Finally, due to Covid-19, we agonized over effective education plans for online archaeological heritage education, which requires active interaction class design and a strategy to promote interaction between professors and learners. In addition, such archaeological heritage education should be compatible with the goal of providing customized lifelong education.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.639-652
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• 2021

In accordance with the rapidly changing social environment, it is becoming more important to cultivate creative and convergent practical talents with flexible thinking skills and problem-solving skills. Therefore, it is necessary for universities to provide educational experiences that enable students to cooperate and converge multidisciplinaryly to carry out on-the-job projects based on what they have learned at school. Therefore, this study designed, developed, and operated with the aim of cultivating creative talents with integrated problem-solving ability through a multidisciplinary capstone design curriculum based on industry-academia cooperation. To this end, the curriculum was developed together by recruiting participating companies and forming a convergence professor team, and it was operated for 15 weeks for students majoring in cosmetics engineering at D University. After the education was over, learning satisfaction and perceived academic achievement were surveyed, and as a result of the analysis, it was found to be above average with 3.77 points and 3.86 points, respectively. And as a result of the in-depth interview on the participation experience, five themes related to the positive experience and three themes related to the negative experience were derived. This study will be able to provide basic data when operating a multidisciplinary convergence capstone design curriculum based on industry-academia cooperation in the future.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.351-368
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• 2023

The scientist-led Research and Education (R&E) program aims to strengthen gifted science students' research capabilities under the guidance of scientists. Students' actual research experiences in scientist-led R&E activities range from understanding how scientists conduct research to actively participating in research. To develop R&E that promotes student agency, i.e., student participation, this study aimed to identify the pedagogical characteristics that supported gifted science students' agentic participation in the scientist-led R&E program. We conducted interviews with learners and scientists in three teams undertaking R&E activities every three months. The interview covered their perceptions of R&E activities, student participation, and scientists' support for the activities. The recordings and transcripts of the interviews were used as primary data sources for the analysis. The trajectory of each team's activities, as well as the learners' and scientists' dynamic positioning were identified. Based on this analysis, we inductively identified the pedagogical characteristics that emerged from classes in which the scientists supported the students' learning and engagement in research. Regarding agency, three types of student participation were identified: 1) the sustained exercise of agency, 2) the initial exercise and subsequent discouragement of agency, and 3) the continuous non-exercise of agency. Two pedagogical characteristics that supported the learners' agentic participation were identified: 1) opportunities for students to take part in research management and 2) scientist-student interactions encouraging learners to present expert-level ideas. This study contributes to developing pedagogies that foster gifted science students' agentic participation in scientist-led R&E activities.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.177-188
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• 2023

The purpose of this study is to develop and validate a scale for measuring digital literacy by identifying the factors consisting of digital literacy and extracting items for each factor. Preliminary items for the Delphi study were developed through the analysis of previous literature and the deliberation of the research team. As a result of two rounds of the expert Delphi study, 65 items were selected for the main survey. The validation of the items was carried out in the process of exploratory and confirmatory factor analyses, reliability test, and criterion validity test using the data collected in the main survey. As a result, a 4-factor structure composed of 31 questions(factor 1: digital technology & data literacy- 9 questions, factor 2: digital content & media literacy- 8 questions, factor 3: digital communication & community literacy- 9 questions, factor 4: digital wellness literacy - 5 questions) was confirmed. Also, the goodness of fit indices of the model were found to be good and the result of reliability test revealed the scale had a very appropriate level of Cronbach's alpha(α=.956). In addition, a statistically significantly positive correlations(p<.001) were found between digital literacy and internet self-efficacy and between digital literacy and self-directed learning ability, which were predicted in the existing evidence, therefore the criterion validity of the developed scale was secured. Finally, practical and academic implications of the study are provided and future study and limitations of the study are discussed.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.107-118
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.

• Journal of the Korean Applied Science and Technology
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• v.39 no.2
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• pp.246-260
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• 2022

This study was conducted to analyze the creativity education contents of dance activities in physical education textbooks for the 3rd and 4th grades of elementary school. For this purpose, 16 types of textbooks and auxiliary data for physical education in the 3rd and 4th grades of elementary school were collected and analyzed using the creative education content analysis frame of the physical education textbook based on the 4P model. In order to secure the integrity of the research, expert consultation was operated. The results of this study are as follows. First, from the viewpoint of creative person, 'inquiry' was the most common in creative mind, and the rest of the elements appeared relatively evenly. As for the subject of activity, 'individual' and 'colleague (team)' showed similar frequencies. Second, from the viewpoint of the creative process, all activity areas appeared as 'learning', and most of the elements of the activity purpose appeared evenly, and the creative process was explored. Third, from the viewpoint of creative output, physical activity performance was the most common activity method, and two or three activity methods were used together. In the creativity factor, all factors appeared evenly, and sensitivity and sophistication were the most common with 4 factors. Fourth, from the viewpoint of the creative environment, most of the activity spaces were no restrictions, and the activity media consisted of many educational contents using the body. Through this study, it was requested that creativity education in dance activities should be expanded quantitatively and intensified in quality, and the necessity of spreading creativity education contents of dance activities to other areas was explored.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.