• 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 Internet Computing and Services
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• v.25 no.5
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• pp.131-140
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• 2024

Aids to navigation, such as buoys used in maritime environments, play a crucial role in providing accurate information to navigating vessels, enabling them to precisely determine their position and maintain safe routes by marking surrounding hazardous areas. However, collisions between ships and these aids result in substantial costs for buoy damage and repair. While high-end equipment is currently used to prevent such accidents, its widespread adoption is hindered by cost concerns. This paper presents research on a maritime object detection algorithm utilizing embedded systems to address this issue. Previous studies employed the Hough transform for horizon detection, but its high computational demands posed challenges for real-time processing. To overcome this limitation, our approach first performs image segmentation, followed by an optimized Otsu algorithm for horizon detection. Subsequently, we establish a Region of Interest (ROI) based on the detected horizon, focusing on areas with a high risk of ship collision. Within this ROI, particularly below the horizon line, maritime objects are detected. A Convolutional Neural Network (CNN) model is then applied to determine whether the detected objects are ships. Objects classified as ships within the ROI are considered potential collision risks.

• 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.

• Korean Journal of Social Welfare
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• v.59 no.2
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• pp.5-38
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• 2007

This study attempts to develop a theory on the evolving process of wealthy major donors' sharing lives in Korea through a grounded theory approach. To conduct this study, the researchers have in-depth interviews with 11 exemplary wealthy major donors who have more than one million US dollars in his or her own asset and donate more than ten thousand US dollars annually. In data analysis, this study identifies 161 concepts on the evolving process of wealthy major donors' sharing lives; and the concepts are categorized with 33 sub-categories and 14 categories. In the paradigm model on the evolving process of wealthy major donors' sharing lives, it is identified that the central phenomenon, 'practicing sharing lives as noblesse oblige', is related with the causal conditions such as 'learning through memories and observation', 'realizing my duties', and 'emphasizing'; and the central phenomenon is related with the contingent conditions such as 'being sensitive to external evaluation', 'having limited information on giving', 'distrusting donation related environments'. The action/interactional sequences such as 'utilizing relationships' and 'strengthening active participation' are accomplished by moderating conditions such as 'having internal and external supports' and 'guiding by firm conviction'. It reveals that as a result, wealthy major donors enjoy the feeling of becoming a ideal and true wealthy person, establish sharing lives as firm and major parts of overall lives, and experience strong desires for better future and society. In this study, 'generous sharing that shares personal heritages and social benefits' is analyzed as a core category; it shows that sharing of wealthy major donors is related to the characteristics of generosity practice based on moral self-benefiting rather than complete altruistic characteristics or self-sacrificial characteristics. The process analysis reveals that it has the following stages: first, initial giving by exposure to causes or requests; second, routine practice of giving; third, evolution of practice of giving with gradual expansion in quantities and qualities; and fourth, living with giving. In the process, the following four types are identified: devoted wealthy donors for sharing, wealthy donors practicing sharing in daily life, wealthy donors practicing sharing with learning on external stimulus, and wealthy donors practicing sharing on empathy. Finally, this study discusses both meanings of identifying and developing a theory on the evolving process of wealthy major donors' sharing lives and implications of the research results in cultivating and developing potential wealthy major donors in Korea.

• Journal of Korean Home Economics Education Association
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• v.20 no.1
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• pp.45-62
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• 2008

The purpose of this study was to investigate middle school home economics(HE) teachers' perception and needs on self supervision related to HE subject matter, Using the methods of survey and interview, 177 samples were collected. For collected surveys, mean value, standard deviation, frequency, percentage analysis were performed by using an SPSS/Win (ver10.1) program. The results of this study were as follows. First, the middle school HE teachers recognized that self supervision related to HE subject matter was absolutely needed to expand the improvement of techniques for teaching instructions and the width of knowledge on the studies on textbook. Second, the middle school HE teachers recognized the necessary parts of self supervision related to HE subject matter as HE teaching-learning methods, the studies on textbook contents, and HE education philosophy in order. Third, the middle school HE teachers recognized that it would be helpful in improving their HE class and expertise in order of field survey, participation in various training programs, utilization of mass media, participation in societies for researches and meetings and information sharing with co-teachers among the types of self supervision. Fourth, the middle school HE teachers needed the reduction in miscellaneous duties, less pressure for time, restoration of teachers' desire, support of physical resources (improvement of various environments such as classrooms and special rooms), economic support and various support programs (expanding the opportunities to participate in training and society and establishment of a database for relevant materials, etc.) to facilitate self supervision. As such, the middle school HE teachers' overall recognition on HE-related self supervision became significantly higher. To enhance the HE-related expertise, however, it would be necessary to conduct concrete and active support for HE education, philosophical area and the studies on textbook contents as well as the teaching-learning methods for HE in which teachers' demand was high. In addition, the HE teachers wanted to have an easy and quick access to various HE-related data; therefore, it would be urgent to summarize scattered relevant data and support the HE teachers more systematically.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Education of Primary School Mathematics
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• v.27 no.2
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• pp.111-137
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• 2024

The purpose of this study is to identify the expected difficulties and necessary support when applying the 2022 revised mathematics curriculum to elementary schools, and to support the establishment of the field. To this end, we explored the major changes in the 2022 revised mathematics curriculum, and based on this, we conducted a survey of elementary school teachers to identify the expected difficulties and necessary support when applying it in the field. In particular, when analyzing the results, we also examined whether there were any differences in the expected difficulties and necessary support depending on the size of the school where it is located and the teaching experience of the teacher. The research results are as follows. First, the proportion of teachers who expect difficulties in applying the 2022 revised mathematics curriculum was mostly below 50%, but the proportion of teachers who demand support was much higher, at around 80%. Second, the difficulty of elementary school teachers in applying the 2022 revised mathematics curriculum was found to be the greatest in evaluation. Third, in relation to the use of edutech, teachers in elementary schools are also expected to have difficulties in teaching and learning methods to foster students' digital literacy, assessment using teaching materials or engineering tools, and assessment in online environments. Fourth, the difficulty of elementary school teachers in applying the 2022 revised mathematics curriculum was also significant in relation to mathematics subject competencies. Fifth, it was found that there is also difficulty in understanding the major changes of the achievement standards, including the addition, deletion, and adjustment of the achievement standards, and the impact on the learning of other achievement standards. Finally, the responses of elementary school teachers to the expected difficulties and necessary support in applying the 2022 revised mathematics curriculum did not differ depending on the size of the school where it is located, but statistically significant differences were found in a number of items depending on the teaching experience of the teacher. Based on these research results, we hope that various support will be provided for the 2022 revised mathematics curriculum, which will be applied annually from 2024.