Quezon City University

The operational readiness of military reserve forces underpins national security and disaster response, yet persistent skill mismatches and manual task assignment hinder effective mobilization. This study developed a Web-Based Skill Management and Task Recommender System for Military Reserve Force Mobilization that integrates reservist profiling, skill tagging, task creation, automated recommendation, and notification delivery through a hybrid pipeline composed of (i) a rule-based filter that enforces minimum qualification constraints and (ii) a weighted multi-criteria scoring layer that ranks qualified personnel by skillset match, availability, proximity, and rank suitability. The system was built using the Agile methodology, and synthetic data modeled on the structure of real reservist profiles were used throughout development and evaluation to address confidentiality and operational security concerns. The system was evaluated by 26 respondents (11 IT experts and 15 military officers) selected through purposive sampling, using a structured questionnaire derived from the ISO/IEC 25010 Software Quality Model. The system was rated Very Acceptable across all eight quality characteristics, with weighted means ranging from 3.73 to 3.91. The hybrid approach was designed as a decision-support tool that surfaces transparent, auditable recommendations to reduce potential skill-to-task mismatches, while final deployment authority remains with qualified military personnel.

This study focused on enhancing kindergarten students’ verbal expression through the use of interactive speaking strategies during classroom discussions. Specifically, it aimed to determine the effectiveness of interactive speaking strategies in improving students’ ability to express ideas, respond to questions, and participate actively in discussions. A quasi-experimental research design was employed involving kindergarten pupils from a selected public school. The study utilized interactive speaking strategies such as guided questioning, storytelling, role-playing, and peer interaction activities. Data were gathered through teacher-made observation checklists and oral language assessment tools. Results revealed a notable improvement in students’ verbal expression skills after the implementation of the strategies. The study concludes that interactive speaking strategies are effective in promoting oral language development among kindergarten learners and recommends the integration of these strategies into daily classroom discussions to support early language development.

This study examined visitor satisfaction and engagement with local tour guides in Intramuros, Manila, and tested whether these outcomes differed by tourist profile. A cross-sectional quantitative survey was administered to 200 tourists who had experienced a guided tour in Intramuros, comprising equal numbers of Filipino and foreign visitors. The researcher-developed questionnaire assessed factors influencing the use of tour-guide services, post-tour evaluations of information and communication, tourist engagement, and proposed service-improvement strategies. The analyses used descriptive statistics, Kruskal-Wallis tests, Mann-Whitney U tests, and Pearson correlation. Respondents reported highly positive reasons for using tour guides (M = 3.58, SD = 0.411), high satisfaction with guide information and communication (M = 3.62, SD = 0.434), and high engagement (M = 3.62, SD = 0.415). Satisfaction and engagement did not differ significantly by age, gender, or nationality (all p > .05). Satisfaction was strongly and positively associated with engagement (r = .820, p < .001). Respondents strongly endorsed continued guide development, particularly communication and storytelling, feedback mechanisms, local cultural integration, multilingual provision, and digital support. The findings position tour guides as central interpretive mediators in heritage tourism and suggest that professional, communicative, and participatory guiding can support meaningful visitor experiences in Intramuros.

In an era marked by environmental uncertainty, technological disruption, and increasing global interconnectedness, the role of higher education institutions in shaping responsible and transformative knowledge has become more critical than ever. Universities are no longer merely centers of knowledge production; they have evolved into key actors in addressing complex global challenges through research, innovation, and community engagement (Altbach & Knight, 2007; UNESCO, 2021). Within this context, the integration of sustainability, ethical governance, and international collaboration has emerged as an essential foundation for responsible global scholarship.

Accurate loan approval prediction is essential for balancing risk management with operational efficiency in automated lending. As borrower datasets grow in complexity, machine learning classifiers offer an effective alternative to traditional scoring methods. This study presents a comparative performance evaluation of three widely applied algorithms: Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR). Using a localized dataset of 300 borrower profiles, the models were assessed across accuracy, precision, recall, and F1-score, with a 5-fold cross-validation scheme repeated three times to ensure stability and mitigate overfitting. The empirical findings show that the tree-based classifiers significantly outperformed the linear baseline. Both Decision Tree and Random Forest models achieved an overall accuracy of 97% and an F1-score of 93%, whereas Logistic Regression achieved 93% accuracy and a 90% F1-score. These results indicate that hierarchical, non-linear decision structures are better suited for mapping complex variable interactions in credit data. This study offers practical guidance for financial institutions seeking to deploy transparent, high-performing automated classification models that minimize false rejections while maintaining rigorous credit risk control.