The objective of this study is to develop a production technology for polymer gel-type moisture-retaining complex fertilizers by neutralizing the FER-PANS MCD polymer reagent, formaldehyde, and inorganic acids along with mineral components using nitric acid or sulfuric acid. The parameters for synthesizing gel-like substances based on the FER-PANS MCD polymer reagent, cross-linking agents, and inorganic and organic acids were investigated. The consumption norms of formalin, citric acid, and inorganic acids required for gel formation were determined by analyzing their correlation with gelation time and swelling parameters. Based on experimental studies, moisture-retaining complex fertilizers were synthesized by incorporating the mineral component MAP in varying ratios into the optimally selected formulations. The key physicochemical characteristics of the synthesized complex fertilizers were examined using IR spectroscopy, SEM analysis, thermal analysis, and swelling capacity assessment. SEM analysis of the obtained moisture-retaining complex fertilizers revealed that the MAP-containing variant comprised essential plant nutrients, including O (52.59%), P (18.11%), Ca (0.04%), K (0.11%), Fe (0.03%), and S (0.03%). Additionally, the swelling capacity of the fertilizer, which indicates its moisture retention ability, was found to range from 26% to 36.5%.

In this study, an educational program utilizing decision trees was developed and applied to improve chemistry teachers' metamodeling perceptions regarding oxidation-reduction reaction models. The participants included 23 chemistry teachers enrolled in a master's program at a comprehensive teacher training university. The program was conducted online over three sessions, each lasting four hours, for a total of 12 hours. The educational program consisted of six stages: concept verification and motivation regarding the electron transfer model, oxidation state change model, and bond type change model (Goodstein model); exploration of AI tool functions using the Orange3 program; generation of concept-based data for each model; concept refinement through AI modeling; AI-based error diagnosis and evaluation; and reflection and sharing. Teachers generated information about chemical reaction equations to create a decision tree classification model, identified the causes of classification errors through the decision tree when machine learning indicated errors, and corrected misconceptions independently. To analyze the effectiveness of the program, changes in teachers' values regarding oxidation-reduction reaction models, model-based judgment capabilities, and perceptions of scientific metamodeling were examined through pre- and post-surveys. The results indicated that teachers' values regarding oxidation-reduction reaction models shifted from a hierarchical perspective to a pluralistic perspective, and they developed a higher-level judgment capability to clearly recognize the scope and limitations of the models. In particular, the perception of scientific metamodeling progressed from the level of objective explanatory tools (Level 2) to the level of exploratory and pluralistic tools (Levels 3 and 4). AI tools were utilized as effective teaching and learning instruments that facilitated teachers' metacognitive reflection. Teachers had positive learning experiences through immediate error identification and visualization, promotion of collaborative discussions, and enhancement of metacognitive reflection. These results suggest that inquiry experiences utilizing AI are effective in deepening teachers' understanding of the nature of science and leading to changes in their practical teaching strategies. Therefore, it is essential to continuously develop and expand professional development programs centered on inquiry experiences where teachers construct and evaluate scientific models themselves using AI tools. This will enable chemistry teachers to deeply understand the nature of models and design lessons that foster the scientific thinking and inquiry skills required by future society.

This study investigated the effects of the “elementary science inquiry education” course on pre-service elementary teachers’ orientation toward scientific inquiry teaching and the factors of the teaching-learning experience of the course that affected the orientation. To do so, 45 second-year students (11 males and 34 females) taking the course at a university of education were surveyed about their orientation toward scientific inquiry teaching before and after taking the course. In addition, the experience factors that influenced their orientation toward scientific inquiry teaching were examined after the course, and individual in-depth interviews were conducted with some students. The results showed that before the course, the most common orientation toward scientific inquiry teaching was “scientific practice”, followed by “concept understanding” and “complex”. In particular, “complex” was mainly a mixture of “concept understanding” and “scientific practice”. “Process skills”, “activity driven”, and “engineering practice” were very rare. After taking the course, “complex” was the most common, followed by “scientific practice”. While “complex” was still dominated by a mix of “concept understanding” and “scientific practice”, there were some new mixes that did not appear before the course. “Concept understanding”, “activity driven”, “process skills”, and “engineering practice” were very rare. By type of change in orientation toward scientific inquiry teaching, “simple → complex” was the most common, and “simple → simple” was the second most common. Many pre-service elementary teachers selected “preparing for science class demonstration”, “mentoring from a professor in preparing for science class demonstration”, “conducting science class demonstration”, “mentoring from a professor after conducting science class demonstration”, “learning theories about science learning models”, “participating in science classes as a learner”, “peer discussion and feedback on science class demonstration”, “learning theories about process skills”, “learning theories about the nature of science”, and “analyzing science textbooks” as experience factors that influenced their science inquiry teaching orientation.

This study examines the types of inquiry activities and the levels of digital tool utilization in the Material units of elementary science textbooks developed under the 2022 revised science curriculum. The results indicate that Experiment and Observation (EO) accounted for the highest proportion (62.4%), followed by Investigation, Discussion, and Presentation (IN) (15.6%) and Expression (EX) (12.1%). In contrast, Discussion and Debate (DE) was absent, and Data Interpretation (ID) accounted for only 3.5%. Digital tools were used in 59.9% of inquiry activities, with Auxiliary Digital Inquiry (AD) being the most common (45.3%), followed by Central Digital Inquiry (CD) (13.4%). Fully Digital Inquiry (FD) had the lowest proportion (1.2%). These findings suggest the need for curriculum development that incorporates diverse inquiry activities, professional training to enhance discussion-based science inquiry instruction, and strategies for effective digital tool integration in inquiry-based learning.

This study developed an educational program that integrates AI and data science into chemistry teaching using the no-code machine learning platform Orange3 and examined its effects with 20 in-service chemistry teachers. The program was designed to provide hands-on experience across the entire process of data collection, preprocessing, visualization, and modeling. Quantitative analysis revealed significant improvements in all subdomains of data literacy and in teachers’ individual AI teaching efficacy. Qualitative analysis identified three key themes: (1) a shift in perception of AI from an expert-exclusive domain to a universally accessible tool, (2) the enhancement of chemistry concept understanding through Orange3’s visualization features, and (3) the strengthening of teachers’ practical willingness to apply data-driven lessons to students. These findings suggest that no-code AI tools can expand teachers’ instructional competencies and contribute to innovation in data-driven science education.

This study aims to explore changes in the teaching orientations of four high school chemistry teachers in a professional learning community (PLC) focused on scientific inquiry using digital tools. Data was collected through pre- and post-PLC questionnaires, interviews, and PLC discussions. Analysis across three subcomponents, i.e., beliefs about the nature, goals, and teaching-learning of scientific inquiry using digital tools revealed that teacher orientation remained stable, newly emerged, elaborated, expanded, adjusted or narrowed after teacher activities in PLC. These changes seemed to occur through reconstructing the meaning of scientific inquiry using digital tools based on teacher beliefs about science and scientific inquiry, developing goals for teaching scientific inquiry using digital tools, and reorganizing the structure and process of teaching and learning. The findings suggest that PLC activity, classroom practice, teacher and school learning context, and teacher-student interaction collectively shape and transform teaching orientations toward scientific inquiry using digital tools.