Earthquake activities in and around the Korean Peninsula are relatively low in number and intensity compared with neighboring countries such as Japan and China. However, recent seismic activity caused great alarm and concern among citizens and government authorities, and uncovered the level of preparedness toward earthquake disasters. A survey has been conducted on 1256 participants to investigate the seismic literacy of Korean citizens, including seismic knowledge, awareness and management using a questionnaire of citizen earthquake literacy (CEL). The results declared that the citizens had low awareness and literacy, which means that they are not properly prepared for earthquake hazards. To develop an earthquake risk reduction plan and program efficiently and effectively, not only must it appropriately characterize the target audience, but also indicate high potential earthquake zones and potential earthquake damage. Therefore, this study mapped and analyzed the seismic vulnerability in southeast Korea using LogitBoost, logistic model tree (LMT), and logistic regression (LR) machine learning algorithms based on a building damage inventory map. The damaged buildings’ locations were generated after the 2017 Pohang earthquake using the damage proxy map (DPM) method from the Sentinel-1 synthetic aperture radar (SAR) data. DPMs detected coherence loss, which indicates damaged buildings in urban areas in the Pohang earthquake and shows a good correlation with the Korea Meteorological Administration (KMA) report with modified Mercalli intensity (MMI) scale values of more than VII (seven). The damage locations were randomly divided into two datasets: 50% for training the vulnerability models and 50% for validating the models in terms of accuracy and reliability. Fifteen seismic-related factors were used to construct a model of each algorithm. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of seismic vulnerability maps using the LogitBoost, LMT, and LR algorithms were 0.769, 0.851, and 0.749, respectively. We suggest that earthquake preparedness efforts should focus on reconstruction, retrofitting, renovation, and seismic education in areas with high seismic vulnerability in South Korea. The results of this study are expected to be beneficial for engineers and policymakers aiming at developing disaster risk reduction plans, policies, and programs due to future seismic activity in South Korea.

Accurate, rational, and scientific decision making is now considered to be the most important skill in science education. Many studies have found that overconfidence bias is one of the cognitive biases hindering people from achieving such decision making. Gender and country play crucial roles in overconfidence bias. For instance, some particular cultures and genders tend to be more overconfident than others. However, whether or not the two variables interact to influence overconfidence bias also indirectly influences decision making, especially in the context of science education. The purpose of this study is to identify the effects of country and gender on performance, confidence, and overconfidence bias in the samples of Indonesian and Korean high-school students while doing on a biology exam. The twenty-one American Association for the Advancement of Science (AAAS) questions on the topics of genetics and evolution were administered to 297 Indonesian and 235 Korean high-school students, in their first and second years. Every question was featured with a question asking students how confident they are in answering the question correctly. The two-way Analysis of Variances (2-way ANOVA) test was used to answer the research questions. Based on the analyses, we found no significance interactional effects of gender and country in test scores. In contrast, we found a significant interactional effects in both confidence in genetics and evolution. Regarding overconfidence bias, for which that we merged both concepts, we found that country had a higher influence on students’ overconfidence bias than did gender. Additionally, we found the hard-easy effect phenomenon followed overconfidence bias phenomenon. The relationships between country, gender, science education, cognitive bias, and overconfidence bias are discussed. Suggestions for reducing overconfidence bias are also provided.

This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across scientific and non-scientific contexts. The findings revealed marked discrepancies in the performance of AI for extracting argument components, with a significant variance between issues of a scientific nature and those that are not. Higher accuracy was noted in identifying claims, data, and qualifiers, as opposed to rebuttals, backing, and warrants. The study illuminated AI's promise for educational applications but also its shortcomings, such as the increased frequency of erroneous element identification when accuracy was low. This highlights the essential need for more in-depth comparative research on models and the further development of AI to enhance its role in supporting argumentation training. Keywords: argumentative writing, artificial intelligence, automated assessment, natural language processing, web architecture

The goal of our study was to examine a large (>400), cross-sectional sample of Chinese pre-service biology teachers (PBTs) in order to document their evolution acceptance levels, evolution knowledge, and evolutionary reasoning patterns. This approach was taken in order to better understand the degree to which particular evolutionary reasoning difficulties exist independent of religious worldviews. The sample included (1) 160 PBTs tasked with completing four items from the ACORNS instrument, (2) 320 PBTs who completed the CINS and MATE instruments, and (3) 32 teachers who completed semi-structured clinical interviews using four ACORNS items. Findings from these samples revealed that Chinese PBTs’ knowledge (CINS) and acceptance (MATE) were equivalent with teachers’ scores from other countries, whereas performance on explanation tasks was lower. Scores from the CINS, MATE, and ACORNS did not reveal any significant improvements through the four-year teacher education programme. Although a large body of work has shown the important roles that religious affiliation and religiosity can play in evolutionary understanding and acceptance, our findings demonstrate that many evolutionary reasoning difficulties extend beyond religious factors, and add to a growing body of work showing that religiosity does not adequately account for PBTs’ moderate evolution acceptance.

Digital literacy is essential for scientific literacy in a digital world. Although the NGSS Practices include many activities that require digital literacy, most studies have examined digital literacy from a generic perspective rather than a curricular context. This study aimed to develop a self-report tool to measure elements of digital literacy among middle and high school students in the context of science practice. Using Messick's validity framework, Rasch analysis was conducted to ensure the tool's validity. Initial items were developed from the NGSS, KSES, and other countries' curricula and related research literature. The final 38 items were expertly reviewed by scientists and applied to 1194 students for statistical analysis. The results indicated that the tool could be divided into five dimensions of digital literacy in the context of science practice: collecting and recording data, analyzing and interpreting (statistics), analyzing and interpreting (tools), generating conclusions, and sharing and presenting. Item fit and reliability were analyzed. The study found that most items did not show significant gender or school level differences, but scores increased with grade level. Boys tended to perform better than girls, and this difference did not change with grade level. Analysis and Interpretation (Tools) showed the largest differences across school levels. The developed measurement tool suggests that digital literacy in the context of science practice is distinct from generic digital literacy, requiring a multi-contextual approach to teaching. Furthermore, the gender gap was evident in all areas and did not decrease with higher school levels, particularly in STEM-related items like math and computational languages, indicating a need for focused education for girls. The tool developed in this study can serve as a baseline for teachers to identify students' levels and for students to set learning goals. It provides information on how digital literacy can be taught within a curricular context.