Surigao State College of Technology

The study aimed to develop and evaluate digitized instructional materials aligned with the K–12 Agriculture and Fishery Arts curriculum. Specifically, the study assessed the learning and instructional needs of Grade 8 students and teachers in terms of digital engagement, ICT accessibility, learning and teaching interruptions, and teachers’ digital readiness. Using a developmental-descriptive research design anchored on the Cognitive Theory of Multimedia Learning of Mayer, data were gathered from students, teachers, and validators through survey questionnaires and evaluation instruments. Findings revealed that students preferred multimedia-rich and interactive learning environments, while teachers demonstrated willingness to implement technology-based instruction despite experiencing ICT limitations and instructional interruptions. The developed digitized learning materials were highly observed in terms of learning objectives and competencies, multimedia integration, and alignment with the K–12 curriculum. Likewise, validators rated the materials highly effective in terms of content quality, instructional quality, technical quality, aesthetic and design quality, and accessibility and user-friendliness. The study concluded that the digitized learning materials were conceptually sound, instructionally effective, curriculum-aligned, and appropriate for Grade 8 Agriculture and Fishery Arts instruction.

This study presents a comparative machine learning approach for flood prediction as an extension of a previously developed IoT-based flood monitoring system. The earlier system enabled real-time acquisition, transmission, and visualization of environmental parameters, including water level, temperature, and humidity. Building upon this foundation, the current study utilizes the collected dataset from 2024 to develop predictive models for future flood forecasting. Four models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid LSTM–GRU, and Extreme Gradient Boosting (XGBoost)—were implemented and evaluated using standard performance metrics such as accuracy, F1 score, and Receiver Operating Characteristic (ROC) curve. Experimental results show that XGBoost outperformed the deep learning models, achieving the highest accuracy (73.76%) and F1 score (0.73), while also demonstrating significantly faster training time. Furthermore, the selected model was used to generate flood forecasts for the period of May 2026 to March 2027, highlighting its capability to predict future flood events based on historical IoT sensor data. The findings demonstrate that integrating machine learning with IoT-based monitoring systems enhances predictive capabilities and supports proactive disaster risk management and early warning systems.

This study developed English 10 lesson exemplars using printed tourism promotional materials, specifically enhanced brochures and booklets featuring tourist attractions in San Francisco and Malimono, Surigao del Norte. It was guided by the concept of authentic materials in English instruction and followed the ADDIE Model as its instructional design framework. The study employed a developmental-descriptive research design and used researcher-made survey and evaluation instruments validated by language experts. A total of 84 respondents participated in the study, composed of locals, language experts or teachers, graphics experts, tourism experts, and tourists. Findings revealed that the existing promotional materials were generally adequate but required improvement in linguistic clarity, accuracy of information, and promotional appeal. The developed brochures and booklets were found to be well-organized, coherent, visually functional, informative, and useful for both tourism promotion and English instruction. Their acceptability was rated Highly Acceptable in terms of linguistic clarity, accuracy of information, promotional appeal, and persuasive effectiveness. Suggested enhancements included adding tour guide contact details, accommodation packages, nearby restaurants, and the updated Malimono logo. The study contributes to contextualized English instruction, local tourism promotion, cultural appreciation, community participation, and institutional collaboration. It also supports sustainable development by strengthening quality education, local economic opportunities, sustainable communities, and partnerships among schools, local government units, tourism stakeholders, and community members.

This study developed an Alugbati (Basella alba)-based seasoning and compared the sensory acceptability of three formulations. A developmental-descriptive design was implemented at the Food Technology Innovation Center of Surigao del Norte State University. A purposively selected panel of 120 respondents (20 food technology faculty, staff, or experts and 100 students) evaluated color, aroma, flavor, saltiness, texture, and composite appeal using a nine-point hedonic scale. Mean scores and standard deviations were calculated, and one-way analysis of variance with pairwise comparisons was used to evaluate formulation differences at the 0.05 level. All three formulations received generally favorable acceptability scores. Formulation C had the highest composite-appeal mean (7.17), followed by B (7.08) and A (6.78). Significant formulation differences were reported for aroma (F = 5.38, p = 0.01) and composite appeal (F = 4.76, p = 0.01), but not for the other four attributes. Acceptability of Formulation C did not differ significantly by age or sex. Per 5-g serving, Formulation C contained 20 kcal, 3 g carbohydrate, 1 g protein, and 0 g fat. The findings support further optimization of Alugbati seasoning, particularly its aroma, and additional shelf-life and food-safety testing prior to commercialization.

This study addressed the critical need for sustainable innovation in Philippine aquaculture by developing and validating a Remote-Controlled Solar-Powered Fishpond Aerator. Conducted in Surigao del Norte, the research utilized a Developmental and Descriptive-Evaluative design to create a prototype capable of operating in off-grid rural environments. The device integrates a nine-component modular system, including a solar harvesting unit, a 12V battery, and a paddle-wheel mechanism protected by an IP67-rated enclosure. Technical evaluations confirmed high operational efficiency: the prototype successfully raised dissolved oxygen (DO) levels from a hazardous 3.2 mg/L to an optimal 6.8 mg/L within two hours, consuming only 42 watts of power. Reliability tests established a functional remote-control range of 50 meters and a nighttime battery endurance of 6.5 hours under continuous use. Perceptual assessments from experts and local fishpond operators yielded high scores for Functionality (4.6) and Practicality (4.63), specifically praising its ability to eliminate electricity costs. The study concluded that the aerator is a technically viable, cost-effective, and user-friendly alternative to traditional systems. Recommendations include long-term field testing and government subsidies to support wide-scale adoption among small-scale farmers. The study concludes that the remote-controlled solar-powered fishpond aerator is effective, efficient, reliable, user-friendly, and cost-efficient. Recommendations include extending the remote-control range, adding a battery indicator, improving wind stability, and conducting longer-term field testing. The device is a viable and practical innovation for sustainable aquaculture in off-grid areas.