Eastern Samar State University

Every learner's learning is enriched by their cultural practices, lived experiences, and practical skills. This concept, called Funds of Knowledge (FoK), explores how students' FoK is integrated into the teaching and learning of science concepts by life science teachers. This was examined in this Interpretative Phenomenological Analysis study. The data were obtained from video transcripts of three different focus group discussions (FGDs), which were analyzed using thematic analysis. Students' Funds of Knowledge (FoK), as perceived by Life Science teachers, can facilitate learning engagement and deepen conceptual understanding, since these teachers believe that students' FoK are derived from their own experiences and cultural practices. The findings also revealed that certain strategies, such as background checking, home visits, and idea sharing, are key to identifying or capturing students' FoK. Moreover, integrating FoK into Life Science concepts benefits the teaching and learning process by improving conceptual understanding, enhancing student engagement, and sustaining cultural identity. However, several challenges were also identified, including the time-consuming nature of developing FoK-based teaching methods, the lack of localized instructional materials, and the limited applicability of FoK to certain Life Science topics. Thus, this study recommends a grounded science education that is culturally inclusive and responsive to students' needs.

Objectives: This study aims to predict student success in an Introduction to Computing course using Senior High School (SHS) specialized and applied coursework data, with particular focus on graduates from the Technical-Vocational-Livelihood Information and Communications Technology (TVL-ICT) track. Methods: A quantitative predictive modeling approach was employed using educational data mining techniques. A decision tree classifier was developed based on the academic records of 258 first-year Bachelor of Science in Information Technology students. The model utilized grades from three applied courses and two specialized courses as input features, with student performance (pass/fail) as the target variable. Model performance was evaluated using 10-fold cross-validation. Findings: The results revealed that the model achieved an accuracy of 84.5%, indicating strong predictive capability. Among the predictors, specialized coursework emerged as the most significant factor influencing student success, while applied coursework demonstrated a secondary yet meaningful contribution. Novelty: This study contributes to the limited body of research linking SHS TVL-ICT academic preparation directly to college-level computing success through predictive modeling. This research specifically isolates the predictive value of specialized and applied SHS coursework using a decision tree approach. The study introduces a data-driven framework for early identification of at-risk students Keywords: Predictive modelling, Senior high school, Computing, Coursework, Decision tree

Objectives: This study examined the relationship between Artificial Intelligence (AI) literacy and AI ethical awareness among educators, specifically determining their levels and testing whether a statistically significant relationship exists between the two variables. Method: A quantitative–correlational research design was employed. From a population of 243 educators, 151 faculty members were selected using random sampling. Data were collected using an adapted Meta AI Literacy Scale (MAILS), which demonstrated acceptable content validity and high internal consistency (Cronbach’s α = 0.89). Descriptive statistics (mean and standard deviation) and Pearson’s correlation coefficient were used for data analysis. Findings: Results revealed that educators exhibited moderately low levels of AI literacy AI literacy (M = 3.65, SD = 1.42) and AI ethical awareness (M = 3.70, SD = 1.39). Specifically, deficiencies were observed in higher-order competencies such as critical evaluation and practical application of AI tools. Ethical awareness was also limited, particularly in areas of bias mitigation, data privacy, and academic integrity. Correlation analysis showed a strong and statistically significant positive relationship (r = 0.63, p ≤ 0.05) between AI literacy and ethical awareness, indicating that higher levels of technical competence are associated with stronger ethical understanding. These findings suggest that improving AI literacy may directly enhance educators’ ethical decision-making in AI integration. Novelty: This study provides empirical evidence from rural Philippine State Universities and Colleges (SUCs), a context that remains underexplored in AI-in-education research. It highlights the interconnected development of AI literacy and ethical awareness and offers data-driven insights for designing capacity-building programs in resource-constrained educational settings. Keywords: Artificial Intelligence, AI Literacy, AI Ethical Awareness, Education Technology, Faculty Competence

The study determines the intervention of inclusive education and students’ drop-out rate in Southern Samar National Comprehensive High School, Balangiga, Eastern Samar. The valuable insights into the effectiveness of inclusivity interventions and drop-out rate that is currently implemented can inform future policy decisions, such as the development of more tailored training programs for teachers, allocation of resources, and improvements to inclusive education strategies. The study employed a descriptive-correlational research design with total of 105 respondents from the different grade levels in Southern Samar National Comprehensive High School, Balangiga, Eastern Samar. Findings revealed that the level of implementation of inclusive intervention in Southern Samar National Comprehensive High School, Balangiga, Eastern Samar in terms of Assistive Technology with a mean score of 2.87 interpreted as moderately commendable this points need for more extensive training, seminar, and professional development in inclusive education. The limited training on assistive technology led to lack of technology assistive tools and effective instruction. In terms of differentiated instruction, a mean score of 4.48 interpreted as Highly Commendable and highly committed to differentiate instruction in inclusive education, they utilized different resources to support diverse learners. However, collaborative learning got mean score 4.55 interpreted as Highly Commendable this employs that in inclusive education must be a collaborative in nature from instruction, strategies, and shared responsibility. The level of inclusive education intervention was a commendable with a mean score of 4.12 on the part of teachers in Southern Samar National Comprehensive High School, Balangiga, Eastern Samar. While, the drop-out rate was low but it remains a challenge to everyone it must be zero drop-out rate in inclusive education. The correlational analysis revealed that there is a strong significant relationship between the level of implementation of inclusive education intervention and drop-out rate in Southern Samar National Comprehensive High School, Balangiga, Eastern Samar wherein inclusive teaching strategies are more extensively and consistently applied tend to have lower dropout rates.

Background: Traditional salted egg production relies on uncontrolled environmental conditions, resulting in long fermentation periods (20–40 days) and inconsistent product quality due to seasonal temperature variations and uneven salt penetration. Although recent studies have introduced PLC- and microcontroller-based automation in food processing, limited research has focused on integrating temperature regulation and salinity control within a low-cost, community-scale system specifically for salted egg production. Objectives: This study aimed to (1) design and develop an Arduino-based automated salted egg production device; (2) implement controlled temperature stabilization and standardized salinity regulation during fermentation; and (3) evaluate the usability and acceptability of the developed system using the System Usability Scale (SUS). Method: The study developed an Arduino-based automated salted egg production system designed to regulate fermentation temperature and brine salinity in a controlled environment. The system integrates an Arduino AtMega microcontroller, thermocouple sensor, PTC heater, real-time clock (RTC) module, stepper motor, water pump, relay modules, and LCD display to automate brining, temperature stabilization, and fermentation monitoring. Experimental evaluation was conducted over a 25-day fermentation period using duck eggs immersed in 10%, 15%, 20%, and 25% brine solutions at a 1:1 egg-to-brine ratio while maintaining a constant temperature of 25 °C. System usability was assessed using the System Usability Scale (SUS) with 20 members of a local farmers’ association. Findings: Results demonstrated that the developed system successfully maintained stable fermentation conditions and reduced processing time from 40 days to 25 days, representing a 37.5% reduction. The system achieved a SUS score of 85.0, classified as “Very Good,” indicating high usability and user acceptance. Novelty: The study introduces a low-cost, temperature- and salinity-controlled fermentation system tailored for community-scale salted egg production. By integrating environmental control, automation, and usability evaluation, the proposed system offers a practical and scalable solution for improving productivity, consistency, and modernization in traditional food processing. Keywords: Arduino-based system, salted egg production, temperature control, food processing automation