Optically Adaptive System for Imaging Spectroscopy (OASIS) 3D data of the Canada-France-Hawaii Telescope in the central 10''.48''.3 (2.92.3 kpc2) region of the Seyfert 2 galaxy NGC 1358 were analyzed. Emission line maps for H at 6563 Å and H at 4861 Å were obtained from the OASIS spectra in the 4800-5500 and 6220-6990 Å wavelength regions. Density distribution, as indicated by the H/H flux ratio, is 105.5 cm3 at the center and 104.4-106.0 cm3 surrounding the center. An elliptical region with a density of 105.5-105.8 cm-3 (perpendicular to the bar's position angle, PA) was discovered at symmetrical locations approximately 1.2-2.0 arcsec and 1.1-1.9 arcsec south east (SE) and north west (NW), respectively, of the center along the bar (PA=130o) axis. A lower-density region (appearing as a void) also existed between the center and this symmetrical structure. The high F(H)/F(H) flux ratio values and the distribution of line widths suggest a region with high-density neutral hydrogen gas. The H flux image and linewidth, and F(H)/F(H) flux ratio image maps suggest presence of a substructure associated with a supermassive black hole at the galactic center, as well as independent structures with relatively strong fluxes in the SE and NW regions. The SE structure is delineated as one substantial substructure, whereas the NW substructure appears broken, or is potentially two separate structures, due to dust shielding. The regions have an independent boundary layer with a density exceeding 106.0cm3 toward the center, likely resulting from collision of the structure flowing along the bar with the Inner Lindblad resonance zone.
Among various contributors to urban heat islands, anthropogenic heat flux (AHF) plays a particularly important role during cold waves due to increased energy demand. In this study, we examined the urban weather characteristics of the Seoul Metropolitan Area, as simulated by an urban canopy model, through an AHF sensitivity experiment for the region during a cold wave. We used the Weather Research and Forecasting-Urban Canopy Model (WRF-UCM) with prescribed AHF values for January 2017 to conduct the experiment. Sensitivity experiments were conducted with AHF scaled to 0, 1, 2, and 4 times the baseline value. The model underestimated the air temperature and relative humidity by about 1 o C and 20%, respectively, and overestimated the wind speed by 1.5ms1 without AHF. Doubling the anthropogenic heat flux led to a notable decrease in the root mean square error and mean bias error, particularly for temperature. These results suggest that, to more accurately reproduce urban weather conditions, larger amounts of anthropogenic heat flux should be prescribed during extreme cold events.
This study investigates how long-term changes in mesoscale wind systems have influenced near-surface PM10 concentrations in central Korea, focusing on Chungcheongbuk-do (Chungbuk Province) during spring from 2000 to 2024. Observational data reveal a nationwide decline in near-surface wind speeds, particularly during spring in the 2010s. Empirical Orthogonal Function (EOF) analysis of 850 hPa wind speed anomalies indicates that this weakening trend is linked to synoptic-scale atmospheric variability over the East Asia-North Pacific region. As transboundary contributions of PM10, particularly from eastern China, have declined in recent years, the role of mesoscale wind patterns in shaping local PM10 concentrations in central Korea has become increasingly significant. To assess the influence of weakened mesoscale winds, two contrasting years were analyzed: 2011, marked by anomalously strong winds, and 2023, characterized by anomalously weak winds. Eulerian PM10 flux convergence (PMFC) analysis revealed a shift from divergence-driven dispersion in 2011 to weak PM10 convergence and accumulation in 2023. Despite these stagnant conditions in 2023, PM10 concentrations continued to decline in both Chungbuk and across Korea, underscoring the dominant effect of anthropogenic emission reductions. These findings suggest that although weakened wind conditions can limit pollutant dispersion, sustained emission control measures remain effective in improving air quality.
Water resources are a core component of Earth Science Education, and scientific modeling can be used to enhance students’ understanding of water resources. With recent shifts internationally toward standards-based science teaching and learning, researchers have noted the need for a deeper understanding of how teachers use curricula. The purpose of this study is to examine how science teachers interact with the curriculum and help better determine the role teachers’ conceptions of the curriculum play in how they implement the curriculum in teaching water resources. The concept of ‘curriculum use’ relates to the ways in which a teacher interacts with and is influenced by material resources constructed to support instruction. Further, this study focused on teachers’ perceptions of their role within the teachercurriculum relationship, where these might range from that of an enactor of a planned curriculum to that of a collaborator with curriculum materials, to better understand how their notions influence their curriculum use. Three teachers were purposely selected for a post-curriculum implementation interview following a professional development workshop with 21 science teachers. The interviews were analyzed using thematic analysis. Finally, in the context of the implementation of the curriculum, a better understanding of teachers’ experiences and interactions with the curriculum emerged which highlighted how curriculum resource designs can be improved to take advantage of how teachers work with curriculum materials.
This study explores the pedagogical opportunities and instructional practices that emerge when elementary preservice teachers design science lessons using generative artificial intelligence (GenAI). Drawing on Chiu’s (2024) fourdomain model—Learning, Teaching, Assessment, and Administration—ten third-year pre-service teachers in South Korea participated in a four-week workshop using ChatGPT to design and refine Earth Science lessons aligned with the national curriculum. The participants documented their lesson planning, AI interactions, and reflections, producing qualitative data that were analyzed thematically. Findings show that participants identified various educational possibilities: GenAI supported idea generation and inquiry scaffolding (Learning), helped structure student-centered strategies (Teaching), improved formative assessments and clarified misconceptions (Assessment), and assisted with lesson preparation and time management (Administration). These possibilities translate into specific pedagogical practices, including revising teachercentered approaches to inquiry-based learning, developing scaffolded materials suited to students’ cognitive levels, and reflecting on their evolving roles as science educators. This study suggests that GenAI can act not merely as a tool but also as a catalyst for pedagogical reflection and professional growth. This highlights the need for teacher education programs to foster critical pedagogical reasoning and ethical AI literacy to ensure thoughtful and responsible use of GenAI in science classrooms.
This study investigated the current research trends related to the integration of artificial intelligence (AI) into science education by analyzing 106 domestic and international research papers published between 2020 and 2025. The analysis categorized the studies according to research stage, topic, methodology, educational subject, and keyword frequency. The results indicate that most research is conceptual and theoretical, focusing on understanding the role of AI and developing educational materials, with limited large-scale empirical or curriculum integration studies. Research is methodologically early stage, predominantly design-based, and exploratory, with a notable lack of studies addressing expanded applications and long-term impacts. Curriculum development is active but incomplete; while AI technology advances rapidly, it often outpaces pedagogical adaptation. Teachers and students’ readiness for AI integration has been identified as a critical gap in emerging training models. Additionally, research on Earth Science Education in the context of AI remains sparse. These findings highlight the need for more comprehensive, empirical, and application-focused research to effectively incorporate AI into science education across all disciplines.
In response to the growing demand for deeper and transferable learning in an increasingly complex knowledge society, the 2022 Revised Science Curriculum of Korea introduced “core ideas” as a structural foundation to promote conceptual understanding. Aligned with Understanding by Design (UbD) and concept-based curriculum models, these revisions aim to enhance competency-based learning and support interdisciplinary application. However, conceptual ambiguity surrounding the meaning and implementation of core ideas has posed significant challenges for teachers. We investigated how science teachers understand and interpret core ideas, design and implement inquiry-based instruction, and assess students' process skills, values, and attitudes. In-depth focus group interviews were conducted with twelve in-service secondary school science teachers to examine their pedagogical reasoning and curriculum enactment practices within the 2022 revised curriculum. Findings indicate that teachers recognize the potential of core ideas as powerful, generalizable concepts that foster integration and real-world relevance. Nonetheless, they reported difficulties in redesigning units, facilitating open-ended inquiry, and assessing affective learning outcomes. Teachers emphasized the need for clearer guidance on implementing core ideas, professional development focused on inquiry facilitation, and diverse tools for assessing students’ scientific values and ethical reasoning. The study concludes with practical strategies to strengthen curricular coherence and instructional alignment, including structured examples of core ideas, building teachers’ competency for inquiry-oriented pedagogy, and reinforcing process-based assessment practices.
This study aimed to validate the Revised Systems Thinking Measuring Instrument (Re-STMI) for high school and university students and examine the differences between the two groups. Data were collected from 475 high school students and 340 university students. Analyses were conducted using the Rating Scale Model of Item Response Theory and traditional Classical Test Theory methods including internal consistency reliability and independent-sample t-tests. The findings are as follows: First, the Rating Scale Model analysis indicated that students with higher levels of systems thinking were more likely to choose the highest score (5) for each item, and the item distribution exhibited a normal pattern around the mean item difficulty. Average systems thinking ability was higher among university students (1.21) than among high school students (0.94). Second, Differential Item Functioning (DIF) analysis showed that the items functioned equivalently across the two groups. Third, the internal consistency reliability of the instrument, based on Classical Test Theory, was high (Cronbach’s = .866). Additionally, the independent samples t-test revealed a statistically significant difference in the mean scores between the groups (p< .001). Based on these results, the instrument was verified to be valid through both Item Response Theory and Classical Test Theory frameworks. Therefore, the Re-STMI can be utilized in future research on systems thinking in various educational contexts.
This study examines how key components of science communication are embedded within science museum exemplary exhibits and identifies exhibit design strategies that enhance their educational and communicative impact. Drawing upon theoretical frameworks in experiential and socially mediated learning, the research explores how exhibit design can facilitate visitor engagement with science communication. To identify strategies that promote effective science communication, the researcher conducted field observations at five science museums in the United States and Germany. Exhibits were selected based on science and technology content and their alignment with at least three of six established components of science communication: concept, interest, enjoyment, nature of science (NOS), opinion, and awareness. Data were collected through photographic documentation and qualitative analysis of exhibit features and visitor interactions. Findings identified thirty exhibit design strategies that enrich science communication in museum, with the most effective being scientists’ work, models, nature of science, science-technology-society, and varied tools. Other strategies such as comparisons, inquiry, hands-on activities, history, questioning, metaphors, dioramas, and aesthetic elements also enhance concepts, interest, engagement, NOS, and opinion. This study contributes to the literature on science communication and museum education by offering a practical framework for exhibit design that promotes inclusive and impactful public engagement with science. Implications are offered for museum professionals, exhibit developers, and science educators seeking to align exhibit content with visitors’ diverse motivations, identities, and learning needs.