• 한국노년학
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• v.29 no.2
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• pp.669-682
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• 2009

The purpose of this study was to explore the role of professional identity as mediating variable in the relationship between job- and organization- related factors and job satisfaction. This study surveyed social workers who worked at 24 senior welfare facilities in Daegu·Gyeoungbuk province from Aug. 1. to Aug. 30. 2006. A total of 137 questionnaires were collected using on-site survey (response rate 76.7%). Statistical analyses were performed using SPSS 12.0 for Windows. Descriptive analysis and frequency analysis were performed on overall measurement items and hierarchical regression analysis was conducted to test the mediating effect of professional identity. The reliability of statements was acceptable since the coefficient alphas were > .70. Results of hierarchical regression showed that professional identity was verified as a partial mediator in the relationship between factors related with job and organization and job satisfaction. As the population ages, there will be an increasing need for professional social workers effectively to work with and help care for the elderly. This study highlighted that job- and organization- related factors, namely self-regulations and social supports, are significantly related with job satisfaction of social workers. Especially, such effect was more significantly apparent in high professional identity which is playing a partial mediator. This result implies that there is potential to change work environments of social workers ensuring a delegation of power and responsibility. Therefore, efforts should be made to improve the promotion system and connect social worker as servant with community through diverse service learning programs.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.319-348
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• 2008

Self-efficacy, achievement goals, task value, and attribution are some of the representative motivation constructs that explain adolescents' cognition, affect, and behavioral patterns in achievement settings. These constructs have won researchers' recognition by demonstrating explanatory and predictive utility that transcends various social and cultural milieus learners are exposed to. Korean adolescents' motivation is generally in line with this universal trend and can be described adequately with these constructs. Nonetheless, there also exist a host of indigenous factors that shape these motivation constructs to be uniquely Korean. The purpose of the present article was to explore some of the socio-cultural factors that appear to wield particularly determining effects on Korean adolescents' academic motivation. Review of the relevant literature identified interdependent self-construal, traditional morals of filial piety, familism, educational fervor, academic elitism, and the college entrance system as important cultural, social, and policy-related such factors. Also discussed in this article were the roles of these factors in creating more immediate psychological learning environments for Korean adolescents, such as parent-child relationships, teacher-student relationships, and classroom goal structures.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.273-299
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• 2024

This is a qualitative research study in which three families focused on scientific culture and conducted the process of forming a family fandom using ethnography. The ultimate goal of science education is the "cultivation of scientifically literate persons.", The researcher examines families who regularly participate in informal science educational programs, such as those offered by the National Marine Biodiversity Institute of Korea, to understand the cultural ans sociological significance of these activities as part of their daily routines. This study analyzes and summarizes the experiences of three families in different home environments as to the completion of the family fandom through the process of self-sustaining cultural practice formation through family education activities, and science activities. This study found that the process tword completion is more meaningful than the completion itself, in the context of science, culture, family and fandom. The findings of this study are as follows: 1) The process of forming a family fandom began with the individual purpose of each family member. 2) The process of fandom formation was created in an organic relationship through the interaction between parents and children, and the self-sustaining cultural practice strengthened the bond and expanded the consensus on scientific culture. 3) Parents and children together share scientific culture, and unique culture in the form of sharing in their own cultural life as becoming scientifically literate people. The self-sustaining cultural practice of selecting and enjoying these scientific activities is not simple consumption of popular culture, but the role of parents as cultural designers. This has conducted experiential consumption as "refined (or sophisticated) cultural consumers," and family leisure activities as meaning production of family members so it has social and cultural implications that can be developed into a scientific culture.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

• Journal of Science Education
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• v.48 no.2
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• pp.75-90
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• 2024

Contemporary society faces increasingly diverse risks with expanding impacts. In response, the importance of science education has become more prominent. This study aims to analyze the characteristics of existing research on science-related risk education and derives implications for such education. Using detailed bibliometric analysis, we collected citation data from 83 international scholarly journals (SSCI) in the field of education indexed in the Web of Science with the keywords 'Scientific Risk.' Subsequently, using the bibliometrix package in R-Studio, we conducted a bibliometric analysis. The findings are as follows. Firstly, research on risk education covers topics such as risk literacy, the structure of risks addressed in science education, and the application and effectiveness of incorporating risk cases into educational practices. Secondly, a significant portion of research on risks related to science education has been conducted within the framework of socioscientific issues (SSI) education. Thirdly, it was observed that research on risks related to science education primarily focuses on the transmission of scientific knowledge, with many studies examining formal education settings such as curricula and school learning environments. These findings imply several key points. Firstly, to effectively address risks in contemporary society, the scope of risk education should extend beyond topics such as nuclear energy and climate change to encompass broader issues like environmental pollution, AI, and various aspects of daily life. Secondly, there is a need to reexamine and further research topics explored in the context of SSI education within the framework of risk education. Thirdly, it is necessary to analyze not only risk perception but also risk assessment and risk management. Lastly, there is a need for research on implementing risk education practices in informal educational settings, such as science museums and media.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.107-115
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• 2024

Curriculum revision is a very important process for improving students' learning achievement and abilities, responding to social needs, strengthening equality and inclusiveness, strengthening teachers' professionalism, strengthening national competitiveness, and responding to the era of globalization, and for continuous development and innovation. Through this, we can provide better educational opportunities and environments for future generations. The 2022 revised curriculum is a curriculum that reflects the knowledge and skills students need in modern society and enables them to respond to changes in industry and society. The purpose of this study is to present the direction of career education by analyzing the career education shown in the 2022 revised curriculum. If we analyze only the contents related to career education in the 2022 revised curriculum that directly mention career and occupation, the following contents are found. First, in the curriculum for future response, contents related to career education appear in the strengthening of basic digital knowledge. Second, in the field of autonomous innovation support tasks at school sites, the organization of the free semester system and improvement plans are presented among the details of the improvement of flexibility in the operation of the elementary and secondary school curriculum. Third, in the area of strengthening learner-customized education, the core of career education is strengthening career-linked education between elementary, middle and high schools. Career education is mentioned in the area of the detail itself. As such, it is no exaggeration to say that the core content of the 2022 revised curriculum is career education. The direction and contents of career education are faithfully reflected in the 2022 revised curriculum.