• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of Korean Elementary Science Education
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• v.43 no.3
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• pp.353-364
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• 2024

The purpose of this study is to analyze the achievement standards for grades 3-4 of the 2022 revised science curriculum and identify the goals of science education for grades 3-4 of the 2022 revised curriculum, as well as provide implications for the development of the science textbooks for grades 3-4 and the direction of teaching for teachers in the field. For this purpose, 57 achievement standards of the Science Department 2022 revised curriculum for grades 3-4 were analyzed as to their knowledge dimensions and cognitive processes according to Bloom's Taxonomy of the New Educational Objectives. In cases where an achievement standard is a double sentence or combines two or more knowledge dimensions or cognitive process dimensions, we separated the sentences after having consulted with a group of experts and divided the achievement standards into 57 sentences. We then analyzed the frequency of the categorization of concepts and descriptors by comparing them with the previously studied elementary science standards from the 2015 revised curriculum. The main findings of the study are as follows. First, in the knowledge dimension, the "factual knowledge" accounted for 50 items (86%), compared to "conceptual knowledge" (10%), and "procedural knowledge" (4%), and "metacognitive knowledge" was not analyzed at all. Second, in terms of the cognitive processes, "Understanding" was the highest at 60% with 34 items. It was followed by "applying" with 11%, "creating" with 19%, "evaluating" with 15%, and "analyzing" and "remembering" with 6%. Third, when analyzing the descriptors, "I can explain" was the highest with 9%, followed by "comparison" with 6%, and "practice" and "classification" with 5%. Fourth, compared to the 2015 revised curriculum, "conceptual knowledge" was reduced and "factual knowledge" was overwhelmingly increased. Fifth, in the cognitive process dimension, "understanding,' has increased significantly, while the other cognitive process dimensions have decreased. Conclusions and implications based on these findings are as follows: the focus of the Science Department for grades 3-4 in the 2022 revised curriculum is heavily weighted toward the "factual knowledge," with "understanding" dominating the cognitive process dimensions. As a result, many concepts and applications have been reduced. Based on the results of the comparison of the descriptors with the results of the 2015 revised curriculum, the implications for the development of the science textbooks for grades 3-4 of the 2022 revised curriculum were discussed, and so were the implications of the curriculum for the field.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.959-968
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• 2016

In this study, the Delphi Method was conducted to extract variables as effects of integrated education. Forty-six experts engaged in both the integrated education and research fields participated in this study. The Delphi survey was conducted for three rounds. In the first round, an open questionnaire was given asking variables possibly considered as effects of integrated education. In the second round, variables induced from analysis of the first survey results were given and the degree of agreement for each variable was determined according to the Likert scale. In the third round of the survey, mean, standard deviation, and the first and third quartile calculated using the results of the second survey were given to experts to determine their degree of assent. In addition, categories for variables were suggested. The degree of agreement for appropriateness of categorization and relative importance were determined As a result, a total of 18 variables were chosen except for career awareness. They were categorized according to their definition and properties into five categories: 'creativity' (flexible thinking, associative thinking, intuitive thinking, creative thinking), 'problem solving' (meta-cognition, problem recognition and solving, critical thinking, decision making ability, ability of knowledge application, knowledge and information processing skills), 'integrative perception and sensitivity' (concern and interest in various disciplines, understanding and acceptance of difference, integrative thinking), 'interpersonal relations' (communication skills, cooperation), and 'disciplinary literacy' (humanistic imagination, basic knowledge and literacy of each discipline, academic motivation). The degree of agreement was high in variables included in 'creativity' and 'problem solving' categories and the frequency of choosing the importance was high in variables included in 'integrative perception and sensitivity'. The educational implication related to implementation and practice of integrated education were discussed on the basis of results.