Mindanao University of Science and Technology

Teachers play an important role in adopting teaching strategies that foster active, constructive learning and make challenging subjects engaging and enjoyable. This study aimed to design, develop, and evaluate Chem-Okey, an educational card game, as a supplementary tool to master the acid-base concepts using game-based learning (GBL) theory. The evaluation process utilized a Likert scale based on the five criteria of GBL results indicated high acceptance and effectiveness in all criteria, demonstrating Chem-Okey’s potential to significantly enhance student engagement and learning outcomes. The learning resources management and development system (LRMDS) of the Philippines’ education standards were applied to validate the educational merit of the game, confirming its suitability for interactive content delivery. Furthermore, the results revealed that students had positive in-game, post-game, and social presence experiences, underscoring the game’s ability to foster a collaborative and immersive learning environment. Chem-Okey’s gameplay, requiring team strategy and peer dialogue, effectively promoted social interaction and cognitive engagement, making learning more enjoyable and effective. Chem-Okey is an effective supplementary learning tool, helping students enhance their understanding of acid-base concepts while enjoying the game. The findings highlight the value of well-designed educational tools in transforming learning experiences and promoting both academic and social competencies among students.

Many Senior High School students struggle to develop strong English language skills, which are essential for academic achievement and critical thinking. This study investigates the factors influencing English language learning and critical thinking among Senior High School students in Cagayan de Oro City, focusing on how demographic characteristics (gender, age, grade level, and socioeconomic status) and learning-related variables (motivation, parental involvement, learning environment, and teaching strategies impact student outcomes. This quantitative study employed surveys and multiple regression analysis to determine key predictors of students' critical thinking skills. The results revealed a statistically significant model, F(8, 428) = 0.323, p < .05, with an R² value of 0.336, indicating that 33.6% of the variance in students’ critical thinking skills was explained by the examined factors. Significant predictors included gender (t = -2.30, p < .05), socioeconomic status (t = 2.13, p < .05), motivation (t = 7.29, p < .05), teaching strategies (t = 3.38, p < .05), and learning environment (t = 4.07, p < .05), while age, grade level, and parental involvement were not statistically significant. Descriptive statistics supported these findings, with motivation rated highest (x̄= 4.19, SD = 0.787), followed by teaching strategies (x̄= 3.90, SD = 0.835) and learning environment (x̄ = 3.56, SD = 0.90); parental involvement scored lowest (x̄ = 3.03, SD = 1.04). The overall mean score (x̄ = 3.67, SD = 0.891) suggests generally strong support for English language development. Despite socioeconomic challenges, students demonstrated commendable English proficiency and critical thinking skills. The null hypothesis was rejected. The study concludes that learner motivation, effective instructional strategies, and a supportive learning environment are critical to enhancing English language learning. It recommends promoting interactive teaching, integrating educational technology, and strengthening home-school collaboration to support inclusive and effective language education.

Technology integration transformed education, redefining the teaching and learning experience. Today's classrooms have come a long way from the days of textbooks and blackboards. This study focuses on the design and development of an instructional module aimed at enhancing faculty digital competence in technology integration within higher education. Employing a Design and Development Research (DDR) methodology and guided by the ADDIE Model, the research involved comprehensive needs assessments through stakeholder interviews and expert evaluations. The instructional module was structured to address identified gaps in faculty technology integration skills and is grounded in Kolb’s Triple E Model of Engaging, Enhancing, and Extending learning experiences. Evaluation results from a panel of experts revealed that the module effectively met several key pedagogical standards, demonstrating high levels of content clarity, engagement, and appropriateness for professional development needs. Despite these strengths, the findings indicated opportunities for improvement, particularly in the areas of content inclusivity for diverse learners and the incorporation of structured self-assessment and reflection opportunities. Recommendations emerging from the study include enhancing content tailored to varied learning styles, fostering deeper reflective practices, establishing continuous feedback mechanisms, and conducting professional development sessions to support effective module implementation. This study underscores the importance of continually refining instructional resources to empower faculty in effectively integrating technology within their teaching practices, ultimately enhancing student learning experiences in the digital era.

Introduction: The role of dental health in enhancing athletic performance and overall well-being remains underexplored, despite its significant impact on systemic health.This study aims to examine how innovative dental care and prosthetics can be integrated into sports and physical health education programs to optimize physical performance and promote lifelong wellness.Methods: We conducted a systematic review using PRISMA methodology on 58 articles; 20 peer-reviewed articles published between 2014 and 2024 were utilized. The selected literature explored interdisciplinary approaches to oral health care in sports, emphasizing preventive care, injury management, and interprofessional collaboration.Results: Findings highlight a growing recognition of the correlation between oral health and athletic outcomes. Poor oral conditions, such as caries and periodontal disease, were linked to reduced stamina, slower recovery, and heightened injury risks. Innovative programs incorporating regular dental check-ups, customized mouthguards, and oral health education showed measurable benefits, including a 65% improvement in athletic performance and a 30% reduction in sports-related injuries. Educational initiatives enhanced participants’ knowledge retention by 85% and improved oral hygiene practices by 70%. The study also looked at different ways to integrate dental care, like mobile dental clinics and partnerships between dentists and coaches.Conclusions: This research underscores the need for interdisciplinary collaboration and innovative strategies to embed oral health into sports and education curricula. By fostering a culture of proactive dental care, institutions can enhance athletic performance and contribute to public health advancements. Future research should address gaps in empirical evidence and provide solutions for diverse populations.

Aims: This study evaluates the predictive accuracy of the Random Forest model and a Hybrid ARIMA-Random Forest model for forecasting student enrollment trends in higher education. Accurate forecasting is crucial for institutional planning, resource allocation, and decision-making. This study examines whether combining statistical time series forecasting (ARIMA) with machine learning (Random Forest) improves prediction accuracy. Study Design: A comparative forecasting study using historical enrollment data from 1949 to 2024. Place and Duration of Study: Conducted at a Higher Education Institution in Misamis Occidental, Philippines utilizing 75 years of enrollment records. Methodology: The dataset was split into 80% training and 20% testing. The Random Forest model captured nonlinear relationships in enrollment trends, while the ARIMA (2,1,1) model identified long-term patterns and seasonality. A Hybrid ARIMA-Random Forest model combined predictions from both models to improve accuracy. Forecasting performance was assessed using Mean Absolute Percentage Error (MAPE). The analysis was conducted using Python and relevant statistical/machine learning libraries. Results: The Random Forest model had a MAPE of 15.62%, while ARIMA recorded 16.95%. The Hybrid model achieved the lowest MAPE of 14.52%, indicating superior accuracy. Projected enrollments for 2025-2029 suggest a stable trend between 3,065 and 3,408 students, with seasonal variations. Conclusion: The Hybrid ARIMA-Random Forest model outperformed both standalone models, demonstrating that integrating statistical and machine learning approaches enhances forecasting accuracy. These findings support data-driven decision-making for university administrators in faculty recruitment, admissions planning, and financial management. Future research should explore additional machine learning approaches, particularly deep learning and neural networks, to further enhance predictive accuracy.