Sibuyan Island is experiencing a significant increase in electricity demand due to population growth, urbanization, and industrial development. The island plans to use solar energy, recognizing its abundance and renewable nature; thus, this study was conducted to visualize the spatial distribution of solar exploration suitability using a geographic information system (GIS). Various criteria, including climatology, location, geography, meteorology, and disaster susceptibility, were considered in the assessment. Parameters affected by government policies, such as protected areas, proximity to rivers, roads and faults, ancestral domains, and proclaimed watersheds, were also considered. Parameters were weighted, and suitability levels were highlighted using AHP. The study revealed that about 5.88% (2674.06 km²) of the island was categorized as highly suitable for a solar farm, 34.99% (15,908.21 km²) as suitable, 2.49% (1129.95 km²) as moderately suitable, and the majority, 56.64% (25,754.47 km²), was considered not suitable for solar projects. A solar power suitability map was developed as a reference for local governments and residents in establishing solar PV systems in their respective sites, thus maximizing the full potential of their land. The study also directs future solar power exploration studies in Sibuyan Island, supporting ongoing efforts to maximize solar energy utilization.

The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow’s pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as “clean”. Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.

Involving students in making an educational decision is claimed to produce better outcomes. The effect of consensus-based education (CBE) on achievement in bioenergetics as moderated by gender and learning styles was determined. Two undergraduate biology classes were compared employing the quasi-experimental design, one using CBE and the other taught the conventional way. The Biology Achievement Test (BAT) was the main data collection tool used, supplemented with questionnaires, learning style inventory, videos, journal, and informal interviews. ANCOVA tested the effect of educational approach and the moderating effects of gender and learning styles while t-test compared the BAT scores between the groups. Results show that CBE can be a feasible alternative approach to teaching biology as it fairly addresses issues on gender and learning styles. It has also helped students develop their reasoning skills and improve their appreciation of democratic practices in the classroom. Key words: educational decision-making, consensus-based education, learning style effect, gender effect, achievement in bioenergetics.

These days’ educational landscape forces teachers to adapt to changing demands and embrace innovations. In this study, Artificial Intelligence (AI) literacy was analyzed as how it mediates the association between Computational Thinking Skills (CTS) and Organizational Agility (OA) among secondary teachers. A quantitative causal mediation analysis design was utilized in this study. Standardized AI literacy test, CTS Test, and OA test instruments were utilized to gather pertinent data among 305 respondents. The test instruments were first subjected to confirmatory factor analysis for model fitness. Path and mediation analysis through structural equation modeling revealed that CTS significantly predicts AI literacy, AI Literacy significantly predicts OA, and CTS significantly predicts OA. It was found out that AI literacy partially mediates the relationship between CTS and OA among teachers. This recommends that schools should conduct a comprehensive training program to enhance teachers' CTS and AI proficiency for schools' sustained agility. Keywords: AI literacy, computational thinking skills, organizational agility, structural equation modeling, mediation analysis

Amid rising energy demands in rural areas, thorough resource assessments for initiatives such as wind power are crucial. This study involves a land resource assessment for wind power generation on the rustic Sibuyan Island in the Philippines, which is currently experiencing an electricity shortage. A comprehensive overview of the island’s suitability for wind energy projects is performed via evaluation and analysis using geospatial data and multi-criteria decision making (MCDM). The research results indicate that 50.44% (220.68 km²) of the island’s land area is categorized as ‘poorly suitable’ since it considers protected areas where developments are not allowed. Only 0.08% (0.35 km²) of the island can be classified as ‘marginally suitable’, while 9.15% (40.73 km²), 36.64% (176.39 km²), and 0.69% (3.05 km²) are labeled as ‘moderately suitable’, ‘suitable’, and ‘highly suitable’, respectively. This confirms the potential for wind energy exploration on the island. Delineating the suitability levels provides a foundational framework for stakeholders that enables them to identify optimal sites for wind power, sustain the island’s resources, and contribute to the renewable energy landscape of this rural location. Overall, this study, underpinned by data analysis, offers invaluable insights for decision making in wind power development, with the presented framework adaptable to other areas of interest.

Science education is a key area of research, largely due to its inherent complexity and challenges. Various teaching methods have been developed to address these difficulties, which include the consensus classroom climate. However, no standardized tool has been created to evaluate the consensus within a classroom. This study aimed to develop and validate the Consensus Classroom Climate Inventory (CCCI), an instrument designed to measure the consensus climate of a classroom. The research was conducted in two phases: (1) initial item pool development based on an extensive literature review and (2) expert panel review for content validation. The resulting 29-item scale was pilot-tested with 120 freshmen students from a state university. Exploratory factor analysis using SMART-PLS yielded four distinct factors: Student Agency, Collaborative Learning Environment, Inclusive and Flexible Learning, and Student Autonomy. The final scale consisted of 23 items. Confirmatory factor analysis demonstrated strong factor loadings (>0.8) for all items, indicating good construct validity. Model fit indices suggested a generally good fit, though some indices indicated room for improvement. Reliability analysis revealed high internal consistency across all factors. The CCCI shows promise as a reliable and valid tool for assessing consensus classroom climate, though further refinement is recommended to enhance model fit. This instrument can assist educators in evaluating the consensus climate of Science classrooms and help educators enhance teaching to improve students’ Science learning. Keywords: consensus classroom climate, factor analysis, scale development, reliability test, validity test