Enverga University

This study explores the secondary school teachers’ perceptions of integrating local cultural practices into non-cultural subjects, specifically Mathematics and Science, in selected secondary schools in Lucena City. It examines how teachers perceived cultural integration in terms of students’ cultural roots, cultural beliefs and practices, and cultural heritage, as well as the culturally responsive strategies employed, challenges encountered, and opportunities identified in the integration process. The study employed a qualitative descriptive-analytical narrative inquiry design involving eight (8) secondary school teachers. Moreover, data collected through semi-structured interviews were analyzed using thematic analysis following Braun and Clarke’s framework. Findings revealed that teachers held generally positive perceptions toward the integration of local cultural practices, perceiving it as an effective approach to making non-cultural subjects more relevant, engaging, and inclusive. Teachers viewed local cultural practices as valuable pedagogical resources that helped contextualize abstract concepts, increase student participation, and strengthen students’ cultural identity and sense of belonging. The study further showed that teachers employed culturally responsive strategies such as contextualized examples, collaborative learning, and differentiated instruction across subject areas. However, teachers encountered challenges related to limited localized instructional materials, time and curriculum constraints, diverse students’ backgrounds, and difficulties in aligning cultural examples with prescribed learning competencies. Despite these challenges, teachers identified opportunities for cultural integration through intentional lesson planning, observation of the lived experiences of the students, reliance on community knowledge, and contributions. The study concluded that integrating local cultural practices into non-cultural subjects was both feasible and beneficial when supported by reflective teaching and curriculum alignment. As a major output, the study developed Culturally Responsive Teaching: A Resource Guide for Integrating Local Cultural Practices in Non-Cultural Subjects.

This study examines CAD learning readiness and how it relates to students’ access and connectivity conditions in CAD coursework. Using a cross-sectional survey dataset (N = 204), responses that met embedded attention-check criteria were retained for analysis (n = 197). The instrument measured perceived ease of use (PEOU), perceived usefulness (PU), intrinsic motivation (IM), CAD self-efficacy (CSE), and CAD learning readiness (CLR) using 5-point Likert items. Reliability results indicated strong internal consistency across constructs (α = 0.80–0.92). Descriptive findings show generally high perceived usefulness (PU mean = 4.45) and intrinsic motivation (IM mean = 4.32), while CAD self-efficacy was comparatively lower and more variable (CSE mean = 3.85). Correlation analysis indicated that readiness is most strongly associated with self-efficacy (r = 0.78) and ease of use (r = 0.73). Group comparisons further revealed significant readiness differences by internet reliability (p < 0.001; moderate effect), while differences by hardware capability were smaller (p < 0.05; small effect). The results highlight that CAD readiness is shaped by both learner beliefs and infrastructural learning conditions, suggesting the need for confidence-building scaffolds and connectivity-resilient course design.

This study investigates the moderating role of technology in the relationship between government regulation, knowledge management, and ERP system utilization among semiconductor companies. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the research model evaluates both direct and moderating effects to better understand the dynamics that influence successful ERP implementation. Results reveal that government regulation significantly influences both knowledge management and ERP system utilization, with a large effect size on knowledge processes. Knowledge management emerged as the strongest predictor of ERP system utilization, underscoring its central role in ERP performance. Technology was found to significantly moderate the relationship between knowledge management and ERP utilization, enhancing the effectiveness of knowledge practices when digital tools are in place. The findings emphasize the strategic value of aligning internal capabilities with enabling technologies, offering practical insights for improving ERP outcomes in the semiconductor industry.

This study explores how service quality—categorized into Technical and Customer Service dimensions—affects customer satisfaction and loyalty within an Application Service Provider (ASP) environment. Technical Service refers to operational aspects such as onboarding, installation, and implementation, while Customer Service encompasses relational factors like empathy, responsiveness, reliability, and assurance. Using structural equation modeling (PLS-SEM) based on user perceptions, the study tests a conceptual model that distinguishes the roles of each dimension. Results show that Customer Service quality has a strong and significant effect on customer satisfaction and loyalty, both directly and through mediation. In contrast, Technical Service quality does not significantly impact either outcome, suggesting its value lies in foundational support rather than relationship-building. These findings highlight the importance of prioritizing user-centered support in digital services. The model contributes to service quality theory in technology-mediated environments and offers practical guidance for ASPs seeking to enhance customer experience and retention through improved service interactions.

Microplastics in aquatic life are hazardous to sea life, food safety, and aquaculture. In this paper, we describe MicroPolluScan, an AI-based deep learning system which uses automated microscopic image analysis to detect, classify, and quantify microplastic contamination in fishponds. To facilitate the development, we employed an Agile Scrum-CRISP-DM paradigm, that is an architectural process based on an iterative software design methodology, along with structured data science methodology. Microscopic pictures of beads, fragments, and fibers were taken from fishponds in Lucena City, augmented with Roboflow, and trained in YOLOv5 and YOLOv8 networks on Google Colab. Comparison results demonstrated that YOLOv8 provided the best performance (precision $=0.82$, recall $=0.85$, F1 $=0.83, \text{mAP}=0.89$) because of the anchor-free detection head and C2f supporting mechanism. A fine-tuned model was implemented in a Flask-based web system, for real-time detection and visualization dashboards for practitioners of aquaculture. Software quality of the software evaluated following ISO/IEC 25010 has a mean of 3.80 (Strongly Agree) followed by a Cronbach's $\alpha$ of 0.89 indicating instrument stability and system usability. Our results show that by combining computer vision and web technologies microplastic monitoring can be automated leading to rapid decision making. We envisage future research projects to provide more diversity on the dataset, incorporating IoT-based sensors, and applying explainable-AI models for improved transparency and ground application.