In recent years, the construction sector in the Philippines has faced significant challenges stemming from various events and occurrences, leading to cost overruns and delays in project timelines. A critical element for every construction undertaking's accomplishment is cost evaluation. Precisely approximating the cost of a project involves thorough consideration of various elements, making it a difficult undertaking to forecast. Several building constructions nowadays produce high cost overrun because of unforeseen change in the project budget that raises the overall project cost such as the complexity of the building system and the organization’s environment. The aim of this paper is to offer a potential prediction for cost estimation, with the goal of minimizing the substantial risk of cost overruns in low- to mid-rise buildings. In this study, the structural elements for low- to mid-rise buildings were utilized from building constructions, such as the number of exterior walls (QEW), type of construction material (TCM), building height (HB), total gross area (TGA), building footprint area (BFA), type of occupancy (TO), number of floors (NF), quantity of shear walls (QSW), and number of columns (NC); an artificial neural network (ANN) model was employed in this research to establish a model for forecasting the total construction cost (TCC). With a correlation value (R) of 0.999890 and a mean absolute percentage error (MAPE) of 0.601%, the modeling results shown that the best model structure was 9-25-1 (input-hidden-output), indicating its effectiveness and efficacy in forecasting the TCC. The impact of each variable employed as an input variable (IV) in the model establishment was seen employing the connection weights (CW) through Garson’s algorithm (GA). The calculation exhibited the order of influence observed as QSW > NC > HB > NF > QEW > TGA > BFA > TO > TCM, wherein the quantity of the shear walls is seen to have the most contribution to the construction cost. Moreover, to check its performance versus other prediction modeling tools, a multiple linear regression (MLR) model was also created and compared to the governing prediction model (GPM). The MAPE of the BP-NN is 7.108 times better than that of the created MLR model.

Delays in bridge construction are crucial problems that slow down the economic development in an area. In this study, an artificial neural network (ANN) model was utilized to create a model for predicting the duration delay in bridge construction projects which includes the project amount, length of bank protection, length of bridge approach slope protection, total area of bridge approach, number of item of works, number of foundations, type of foundation, number of girders, type of girder, number of lanes, number of spans, total width, total length, and type of construction as the independent variables (IV). The modeling results showed that the best performing model is the 14–14–1 network with R = 0.99406 and MAPE of 3.524%. By removing each of the parameters, the influence of the independent variables to the duration delay was determined. Using the sensitivity index method, the findings revealed that the ranking of influence of the factors (IF) to the duration delay was observed as LBASP > NS > TW > TC > NIW > LBP > PA > TABA > TL > NG > NF > NL > TG > TF with the length of bridge approach slope protection was seen to be the most influential parameter (MIP) to the duration delay.

This study focuses on the evaluation of a faculty Performance Modeling System, recognizing the critical role of faculty performance in educational quality and institutional success. Although previous research has often concentrated on the technical development and predictive capabilities of such systems, this paper shifts the focus to user acceptance and system efficacy from the perspective end-users, the faculty. To achieve this, the researchers propose and apply a Modified Technology Acceptance Model (TAM) as the theoretical framework for evaluation. This modified TAM incorporates specific constructs relevant to the academic environment and faculty roles, such as perceived impact on teaching effectiveness and perceived relevance to professional development, alongside traditional TAM constructs like perceived usefulness and perceived ease of use. The evaluation methodology involves assessing faculty perceptions and attitudes towards the system, utilizing both quantitative and qualitative data to understand factors influencing its adoption and continued use. The findings are expected to provide valuable insights into the practical applicability and user acceptance of faculty modeling systems, guiding future design and implementation efforts to ensure these tools effectively support faculty growth and institutional objectives.

This study addresses the challenge of identifying students at risk of academic underperformance in a BS Information Technology program. Using a predictive analytics framework aligned with the Philippine Computer Society’s Information and Computing Accreditation Board (PICAB) Criterion 3 on Student Outcomes, a decision tree model was developed in Python using Google Colab. The dataset included grades from key academic indicators such as OJT, Capstone, GPA, Programming, Math, Ethics, and Communication. The trained model achieved an accuracy of 83.33%, effectively distinguishing patterns of academic risk. Specifically, students with Capstone grades of 4.00 or higher, or multiple failing grades in core subjects, were frequently classified as "At-Risk." These findings provide actionable insights for academic intervention, curriculum refinement, and program enhancement. The research supports evidence-based decision-making and contributes to Sustainable Development Goal 4 which is Quality Education by promoting inclusive and data-driven approaches to student success.

This research explores how to optimize online learning environments in support of the United Nations' Sustainable Development Goal 4 (SDG 4), which advocates for inclusive and quality education. It specifically focuses on Network Academy platforms and aims to develop a predictive framework for course completion rates, contributing to SDG Target 4. enhancing technical and vocational skills among youth and adults. By adapting the Technology Acceptance Model (TAM) for educational sustainability, the study integrates traditional constructs like Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) with variables such as inclusive course design, quality instructor feedback, and student self-efficacy. This reframing positions technology acceptance not just as a matter of adoption, but as a strategic pathway to meaningful and equitable learning engagement. Using a mixed-methods approach, the research seeks to produce a robust model that informs educators, instructional designers, and platform developers on how to improve online training programs. Ultimately, the study offers practical, evidence-based recommendations for designing online systems that promote inclusive, high-quality education and directly support the 2030 Agenda for Sustainable Development.

Artificial Intelligence (AI) is reshaping teacher education by providing intelligent tools that enhance pedagogy, student learning, and accessibility. This study explores how Microsoft Copilot, an AI-powered learning assistant, supports both teachers and students in integrating accessibility-focused coding into IT education. By assisting with debugging, code recommendations, and inclusive design principles, Copilot strengthens teachers' digital competence and helps students gain confidence in developing equitable digital solutions. A quantitative survey of Filipino IT instructors and students assessed awareness, adoption, and confidence in using Copilot for accessibility-oriented coding. Statistical analyses, including correlation and confidence interval testing, validated the results. Findings suggest that Copilot not only improves coding efficiency but also supports teachers in embedding accessibility concepts into their instruction—contributing to inclusive and innovative teaching models.

This paper investigates the effects of facilitation approaches of teachers on the engagement of the students in the asynchronous learning environment mediated by the AI chatbots. Though the chatbots provide the benefits of immediate feedback, personality-based feedback, and constant interaction, the outcome of the educational technology is largely dependent on the facilitation approach of the teachers. With the mixed-methods correlational approach, the study collected the data related to the usage logs of the chatbots, sentiment, scores of the quiz, and facilitation inputs with 100 undergraduate IT majors and 20 teachers. The results show that the engagement of the chatbot (r=.74,β=.45), emotional sentiment (r=.66,β=.33), and facilitation inputs of the teachers (r=.61,β=.29) are all reliable predictors of academic performance, together explaining the collective variance of approximately 65% for the scores of the quiz. The results of the study provide evidence on the complementary effects of human facilitation to optimize the effects of AI-facilitated learning. Furthermore, this study also promotes Sustainable Development Goal 4 (SDG4) to provide inclusive, effective, and quality learning for the users through the collaboration of human and AI resources.

Addressing the worldwide crisis of inadequate physical education (PE) programs requires immediate attention. Despite the advocacy of international organizations like UNESCO and WHO, there still needs to be a significant gap in understanding the effectiveness of PE initiatives globally. Cultural, socio-economic, and policy differences further complicate evaluating and improving these programs. More comprehensive research is needed to promote academic achievement, well-being, and overall health. This is where Global Innovations in Physical Education and Health comes in, a groundbreaking solution poised to revolutionize PE on a global scale. This innovative book serves as a beacon of hope by exploring diverse teaching strategies and creative methods worldwide. Bridging critical research gaps empowers policymakers, educators, researchers, administrators, and health professionals with actionable insights to enhance the quality and inclusivity of PE programs. With its comprehensive coverage of topics such as adaptive PE, nutritional education, and global health initiatives, this book provides a roadmap for transforming PE into a catalyst for holistic health and lifelong well-being.

PE often finds itself overlooked in the broader discourse on educational innovation, despite its crucial role in fostering lifelong health and well-being. Unlike subjects that easily attract attention due to their academic prestige or technological allure, PE is sometimes seen as secondary, relegated to the sidelines of educational reform. Yet, in an age where global health challenges such as obesity, mental health disorders, and sedentary lifestyles are increasingly prevalent (World Health Organization, 2020), the significance of PE cannot be understated. The Global Innovations in Physical Education and Health book seeks to challenge this perception by highlighting the transformative potential of innovative approaches in PE and health education (Garcia, Lopez Cabrera, et al., 2023). It brings together 69 experts from nine countries—Indonesia, Philippines, China, Ireland, United Arab Emirates, Portugal, India, USA, and Canada—to explore how these innovations are reshaping the way we teach, experience, and benefit from PE, making it a field deserving of as much attention and innovation as any other educational domain. This book serves as a crucial resource in guiding the development of more effective, inclusive, and culturally aware health and PE programs. It sets the foundation for actionable teaching and learning models in various educational and cultural contexts. The coverage includes insights into how different countries approach PE, the integration of health education within these programs, and their impact on student health and academic achievement.

Power system protection is essential for maintaining the reliability and stability of electrical grids, ensuring continuous service, and preventing catastrophic failures. As power systems evolve to incorporate renewable energy and increasingly complex configurations, the role of Artificial Intelligence (AI) in enhancing protection mechanisms has become indispensable. This paper reviews the integration of AI in power system protection, highlighting its potential to improve fault detection, adaptive protection strategies, predictive maintenance, and real-time monitoring. AI techniques, including machine learning, deep learning, and expert systems, offer significant advancements in overcoming the limitations of traditional protection schemes. Furthermore, the integration of AI contributes to the development of resilient and sustainable infrastructure, supports innovation in intelligent urban systems, and enhances the reliability of modern power grids. Despite its promising potential, challenges such as data scarcity, model scalability, and real-time processing need to be addressed for effective implementation. This review synthesizes the current literature on AI applications in power system protection, comparing them with conventional methods, and provides information on future research directions and practical applications to improve energy reliability, sustainable urban development, and industrial innovation.