Pangasinan State University (PangSU or PSU Filipino: Pamantasang Pampamahalaan ng Pangasinan) is a public university in Pangasinan province, Philippines. The university was founded in its current form in 1979, although its origins trace back to the 1920s. PSU is notable for its many locations throughout the province of Pangasinan. It is mandated to provide advanced instruction in the arts, agricultural and natural sciences as well as in technological and professional fields. Its main campus is located in Lingayen, Pangasinan. Other campuses are located in Alaminos, Asingan, Bayambang, Binmaley, Infanta, San Carlos City, Santa Maria, and Urdaneta City. The PSU School of Advanced Studies (SAS) is located in Urdaneta City and the Open University Systems (OUS) is located in Lingayen Campus.
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.
The study surveyed 341 public elementary teachers in Region I regarding their awareness, utilization, integration, challenges, and opportunities related to AI use in teaching. It also examined the correlations between these factors and teacher demographics and the perception gaps between them and the school heads. Most teachers were female, middle-aged, married, and graduate-educated. Teachers showed moderate AI awareness (higher for general concepts) and use (ChatGPT was the highest), with many reporting limited use. Both groups rated integration as moderate (strongest in resources and lesson design) but identified barriers, including ethical/societal issues, technical issues, and skills gaps. Yet they saw great potential for student engagement, assessment, and innovation. Integration was significantly correlated with age, sex, education, experience, and training. Teachers and heads shared their perceptions, highlighting the need for inclusive training, policy support, and ethical standards in basic education.
This qualitative study examined the experiences and challenges in implementing technology-assisted inclusive education among educators in selected Pratum Schools in Phasi Charoen District, Bangkok, Thailand. Using a qualitative case study approach, data were collected through interviews with 13 educators who had varying levels of teaching experience, subject specializations, and technological preparedness. Findings revealed that the majority of participants were novice teachers (53.8%, n=7), followed by mid-career educators (38.5%, n=5) and veteran teachers (7.7%, n=1), indicating a predominantly early-career workforce. In terms of subject specialization, 53.8% (n=7) taught English language courses, while 23.1% (n=3) specialized in Mathematics and Science and another 23.1% (n=3) served as multi-subject educators. Analysis of technological preparedness revealed that 46.1% (n=6) had formal technology training, 38.5% (n=5) had no formal training, and 15.4% (n=2) relied on informal or peer-mediated learning, highlighting disparities in digital readiness. Major challenges included unstable power supply, internet disruptions, outdated devices, language barriers in digital interfaces, classroom space limitations, and inadequate technical support. Socio-economic inequities also created “Digital Spectatorship,” wherein students without access to personal devices became passive observers rather than active participants in learning activities. Educators’ perceptions reflected a dominant theme of “Pragmatic Frustration,” demonstrating a strong belief in technology as an inclusive educational bridge while simultaneously experiencing difficulties due to insufficient institutional support. Despite these constraints, teachers demonstrated instructional resilience through practices such as personal resource subsidy, mediated facilitation, peer-assisted learning, and grouped-access strategies. Furthermore, technologies such as YouTube, Canva, Kahoot, Baamboozle, LINE, and Smart TVs emerged as preferred instructional tools due to their accessibility and adaptability. The study concludes that teachers function as the primary “Human Bridge” sustaining technology-assisted inclusion in under-resourced educational settings.
