• Journal of Science Education
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• v.39 no.2
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• pp.267-277
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• 2015

This study explored elementary school teachers' perspectives on science inquiry teaching. First, an open-ended questionnaire was administered to elicit teachers' experiences of their approach to inquiry teaching. These self-reported approaches revealed three conceptions of teaching for inquiry learning in science: 'science process skills-centered' category focused on observing, classifying, measuring, and fair testing; 'generating scientific questions' category focused on students' question-generating; and 'illustrate concept and/or content' category focused on science content demonstration by making use of experimental procedures to obtain expected results. Second, teachers were asked to place 18 activity cards either close to or further from an 'inquiry-based science classroom' card. The relative distances from the activity card to the central classroom card were measured. The teachers perceived that students' activity of 'designing and implementing appropriate procedures' was the most important in supporting an inquiry-based science classroom. Understanding teachers' views has implications for both the enactment of inquiry teaching in the classroom as well as the uptake of new teaching behaviors during professional development.

• Korean Journal of Health Education and Promotion
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• v.27 no.4
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• pp.165-175
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• 2010

Objectives: The purpose of this study is to evaluate the program theory of a lifestyle intervention program for the prevention and treatment of metabolic syndrome. Methods: The program evaluated is a tailored intervention for multiple health behavior associated with metabolic syndrome which is informed by theoretical constructs from the Intervention Mapping and Transtheoretical model. The program components include one-to-one health counseling, a self-management handbook, and a health diary. To evaluate program impact theory we examined the logic of program goals and objectives, intervention methods and strategies, and the theoretical constructs of program materials through document review and matrix building. Results: This evaluation has found that the intervention program applied social cognitive theory constructs to design intervention methods and strategies in addition to the Transtheoretical model: self-monitoring for goal setting and monitoring skill, outcome expectation for the benefits of health behavior change, and interaction with environment for observational learning through modeling. While the intervention addresses multiple determinants and behaviors, it is limited to an individual level and lacks social and environmental approaches. Following the Transtheoretical framework, the contents of the intervention materials were developed utilizing consciousness raising as a main strategy for earlier stages of change, and counterconditioning and stimulus control for later stages of change. Conclusion: Program theory evaluation can be a process of enhancing program validity. It would also be necessary for providing basis for efficient program implementation. When comparisons of program theory between similar programs are possible, program theory and validity will be strengthened when comparisons of program theories between similar programs are possible.

• Korean Medical Education Review
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• v.19 no.3
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• pp.115-120
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• 2017

Despite the importance of how the premedical education curriculum is organized, the basic direction of the curriculum has not been evaluated at a fundamental level. In order to explore the basic directions of the premedical education curriculum, this study examined medical education as a university education, the historical basis of premedical education, and the direction of the premedical education curriculum. Historically, as medical education was incorporated into the university education system, premedical education developed based on basic science and liberal arts education. Accordingly, the direction of the premedical education curriculum began to split into two approaches: one believing in a basic science-based education intended to serve as the foundation of medical training, and the other believing in a liberal arts-based education intended to cultivate the qualities of a doctor. In recent years, however, the binary division in the direction of premedical education has ceased to exist, and the paradigm has now shifted to an agreement that premedical education must cultivate the basic scientific competence required for learning medical knowledge as well as the social qualities that a doctor should have, which are cultivated through the liberal arts. Furthermore, it has been asserted that the direction of premedical education should move toward the qualities that will be required in the future. With the fourth industrial revolution underway, the role of doctors is now being re-examined. This means that today's medical education must change in a future-oriented way, and the direction of the premedical education curriculum must be on the same page.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.39-39
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• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.21-30
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• 2021

Economic transformations have led to an increase in the role of creative assets and their central role in public life. Changes in creative activity have led to a change in the organization of the work of institutes engaged in the training of specialists, in particular teachers of labor education. Methods and approaches to training determine the development of creative industries, being the basis for models of professional training of future teachers of labor training. The purpose of an article was to develop a modern model of professional training of future teachers of labor training based on the concept of creative economy. The methodology is based on the concepts of holistic craft and creative economy. Based on the integration of pedagogical learning models "Craft as design and problem-solving", "Craft as skill and knowledge building", "Craft as product-making" and "Craft as self-expression" developed and experimentally confirmed the conceptual model of professional training of future teachers of labor training. The proposed model forms a practitioner with professional, technical, digital and creative skills who is able to transfer the experience to students. The training course "Creativity and creative thinking" has been developed. The model provided for the development of a course based on the strategy of developing professional creativity, flexibility, improvisation, openness, student activity, joint practice, student-oriented approach. The practical value implies the adaptation of the developed model of professional training of future teachers of labor education during the training of teachers in higher education, which is confirmed in the experiment.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.751-776
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• 2022

Purpose: The purpose of this paper is to present implications by analyzing research trends on CSR, CSV and ESG by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on CSR, CSV and ESG. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "CSR", "CSV" and "ESG" as search terms, and the Korean abstracts and keyword were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, CSR 2,847 papers, CSV 395 papers, ESG 555 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; CSR, CSV, and ESG studies showed that research slowed down somewhat before 2010, but research increased rapidly until recently in 2019. Research have been found to be heavily researched in the fields of social science, art and physical education, and engineering. As a result of the study, there were many keyword of 'corporate', 'social', and 'responsibility', which were similar in the word cloud analysis. Looking at the frequent keyword and word cloud analysis by field and year, overall keyword were derived similar to all keyword by year. However, some differences appeared in each field. Conclusion: Government support and expert support for CSR, CSV and ESG should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to them. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of the Korean Chemical Society
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• v.67 no.4
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• pp.241-252
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• 2023

This study aims to increase students' understanding of equilibrium, one of the many concepts in chemistry that students find difficult. Dynamic equilibrium must be dealt with at the sub-microscopic level where the real and the representation overlap in order to microscopically understand the constant motion and interaction of particles and to understand the macroscopic characteristics expressed through this. However, as a result of analyzing 9 Chemistry I textbooks, the expression approach for equilibrium had some limitations. As a strategy to understand equilibrium at a sub-microscopic approach, the classes using scratch were consisted of a total of 4 hours, and it was implemented with 56 students. The classes were composed of 6 steps, and it was designed to understand equilibrium step by step. As a result of comparing the pretest and post- test, the number of students who got both the microscopic and macroscopic explanations of chemical equilibrium correct increased largely. Through this, it was possible to get a glimpse of the applicability of classes using scratch as the approach strategy of the sub-microscopic representation.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.