Pangasinan State University

This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.

How to efficiently perform network tomography is a fundamental problem in network management and monitoring. A network tomography task usually consists of applying multiple probing experiments, e.g., across different paths or via different casts (e.g., unicast and multicast). We study how to optimize the network tomography process through online sequential decisionmaking. From the methodology perspective, we introduce an online probe allocation algorithm that sequentially performs network tomography based on the principles of optimal experimental design and the maximum likelihood estimation. We rigorously analyze the regret of the algorithm under the conditions that $i)$ the optimal allocation is Lipschitz continuous in the parameters being estimated and ii) the parameter estimators satisfy a concentration property. From the application perspective, we demonstrate that the quantum bit-flip network fulfills the two theoretical conditions and provide their corresponding regrets when deploying our proposed online probe allocation algorithm. Besides case studies with theoretical guarantees, we also conduct simulations to compare our proposed algorithm with existing methods and demonstrate our algorithm's effectiveness.

This study examined teachers’ proactive approaches to classroom behavioral management and their implications for a guidance program. Guided by Coombs’ Systems Approach using the input-process-output model, the study focused on teacher profiles, students’ behavioral problems, factors causing such problems, classroom management approaches, and guidance program implications. A descriptive research design was employed among 50 public elementary school teachers from Cabanatuan City during the school year 2024–2025. The respondents were selected through simple random sampling, and data were gathered using a validated questionnaire adapted from a previous study. Frequency, percentage, weighted mean, and related statistical procedures were used to analyze the responses. Findings showed that most respondents were female, aged 31–40, married, with master’s units, holding Teacher I positions, with 6–10 years of service, handling mostly Grade VI learners, and having attended only one to two training sessions. Teachers strongly agreed that students’ behavioral problems, including vandalism, absenteeism, bullying, technology misuse, inattention, disrespect, cheating, and lack of learning skills, were serious classroom concerns. They also strongly agreed that these problems were influenced by inadequate values education, social media and technology, weak parental guidance, poverty, peer influence, and inconsistent discipline. Teachers strongly supported proactive, student-centered, and non-punitive strategies, particularly supportive school culture, parent collaboration, respectful guidance, emotional regulation, and clear expectations. The study aligns with SDG 4, SDG 3, SDG 16, and SDG 17 by promoting quality education, student well-being, safe school environments, and stakeholder collaboration. Its sustainability impact lies in strengthening educational, institutional, social, and community-based support systems for positive student behavior and effective classroom management.

This qualitative study examined the workload and teaching outcomes of Filipino English teachers working in Thailand and Lithuania, with the aim of generating policy-relevant insights for educational practice in international teaching contexts. Using a qualitative comparative design, data were gathered from ten purposively selected Filipino English teachers—five from each host country—through semi-structured interviews and narrative accounts. Thematic analysis was employed to explore teachers’ descriptions of workload components, the meanings they attributed to their experiences, and the perceived effects of workload on instructional quality, student engagement, assessment practices, and classroom effectiveness. Findings revealed that teachers in both contexts experienced workloads extending beyond instructional duties, including administrative tasks, extracurricular responsibilities, and non-teaching assignments. While workload was generally perceived as manageable, participants described periods of heightened demand that affected lesson preparation, assessment depth, and individualized student support. Teachers attributed their workload experiences to institutional policies, leadership practices, and cultural expectations, with notable differences between the more structured and policy-driven workload environment in Lithuania and the more flexible but task-intensive context in Thailand. Despite these challenges, teachers demonstrated adaptive coping strategies, professional resilience, and sustained motivation, supported by collegial relationships and a strong sense of purpose. However, excessive non-teaching demands were linked to stress, work–life imbalance, and concerns about long-term professional well-being. The study concludes that workload management, institutional support, and culturally responsive leadership play critical roles in sustaining teaching effectiveness and teacher well-being among migrant educators. The findings offer actionable insights for policymakers, school leaders, and teacher education institutions in both host and sending countries to strengthen support systems, align workload with teaching goals, and promote quality education in international settings.

The Philippine salt industry, particularly in culturally significant regions like Pangasinan, has faced a steep decline due to a reliance on antiquated, labor-intensive production methods. This study addresses this challenge through a three-part objective-driven approach: (1) identifying the technological gaps in artisanal salt production through a comprehensive literature review and needs analysis; (2) developing a prototype salt vibrating screen classifier with an integrated grinder to address these gaps; and (3) evaluating the prototype's acceptability among experts and end-users. The methodology involved a systematic review of academic and industry literature, which pinpointed a lack of appropriate-scale post-harvest machinery. A prototype, fabricated from stainless steel and powered by a 5-horsepower motor, was subsequently developed. The machine's acceptability was determined through a mixed-method evaluation with technical experts and salt farmers. The results from the evaluation demonstrated high acceptability, with participants confirming the machine's robust performance, efficiency in grinding and classifying salt, and significant practical advantages over traditional methods. The study concludes that the developed prototype is a highly acceptable and viable technological solution that directly addresses the needs identified in the literature, offering a means to enhance productivity and quality for Filipino salt farmers.