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.

Online learning has become fundamental to modern academic and professional development. Amidst its widespread adoption, there is increasing integration of artificial intelligence (AI) to enhance the learning experience. Understanding student engagement within these AI-powered digital platforms is crucial, as it directly influences learning outcomes and satisfaction. This chapter provides a narrative review of key theories and models essential for analyzing engagement in virtual learning contexts. Particularly, it focuses on constructivist learning theory, social learning theory, cognitive load theory, flow theory, technology acceptance model, self-determination theory, cognitive theory of multimedia learning, and feedback intervention theory. By examining these frameworks through an epistemological lens, the chapter explores how knowledge acquisition, cognitive processing, and social learning principles interact within AI-enhanced educational contexts. The insights reported here can serve as a guide for optimizing AI to maximize student involvement and educational efficacy.

Assessing the preparedness of Ghana's health sector is a crucial task that requires a comprehensive and multi-faceted approach. Ghana's health sector faces many challenges, including limited resources, inadequate infrastructure, and workforce shortages, which can impede the delivery of quality healthcare services to the population. Thus, building a strong health resilience system is essential to cope with catastrophic events, and the capacity to prepare and effectively respond to pandemics. The COVID-19 pandemic has highlighted the critical role of digital technologies in managing public health emergencies. In the context of long-term pandemic management, digital transformation can provide numerous benefits, such as improving the speed and efficiency of response, enhancing communication and collaboration, and enabling remote access to essential services. Empirically, our study found that individual and systemic resilience are significant predictors of long-term pandemic management. Conversely, community resilience in times of crisis is not a significant predictor.

Serious games offer an innovative blend of entertainment and learning in medical education. This chapter examines the technological underpinnings, challenges, and potential future developments in this domain. Drawing from publications between 2018 and 2023, this systematic review highlights the role of technologies such as web and mobile applications, game engines, augmented reality, virtual reality, mixed reality, and artificial intelligence in personalizing and enhancing the learning experience. However, the use of serious games in medical education also faces several challenges, including the need for adequate technological infrastructure, complex effectiveness assessments, and integration into existing curricula. Moreover, this chapter outlines projections for further research. The authors reveal how serious games have the potential to transform medical education to be more engaging, interactive, and effective, and inspire future research in the development of innovative technologies and learning methods.

With the increasing popularity of artificial intelligence (AI) applications in medical practices, the integration of AI technologies into medical education has garnered significant attention. However, there exists a noticeable research gap when it comes to providing comprehensive guidelines and recommendations for its successful integration into this domain. Addressing this research gap is crucial as the responsible and effective incorporation of AI in medical education not only ensures that current and future healthcare professionals are well-prepared for the demands of modern medicine but also upholds ethical standards, maximizes the potential benefits of AI, and minimizes potential risks. The objective of this chapter is to fill this gap by offering practical tips and actionable insights for incorporating AI into medical education, encompassing practical, ethical, pedagogical, and professional implications. Consequently, it equips medical educators and learners alike with the knowledge and tools necessary to navigate the evolving landscape of medical education in the age of AI.

This chapter examines the potential of serious games in physical education, focusing on technology integration, implementation, and evaluation. It explores how serious games enhance learning outcomes, curriculum integration, and user engagement by merging AI technologies with learning theories. Aimed at educators, researchers, and developers, the chapter uses a systematic literature review and case studies to illustrate practical applications. It highlights various technologies like exergaming, game-based learning models, and computer-aided tools, showing their impact on student motivation and engagement. Despite challenges like cost and training needs, the chapter underscores the promise of AR, VR, MR, and immersive tools in revolutionizing physical education. Through adaptive programs, culturally responsive pedagogies, and diverse evaluation methods, the chapter demonstrates the effectiveness of serious games in creating inclusive and engaging physical education programs while addressing the need for cost-effective solutions and comprehensive training for educators.

The application of natural fibers in reinforced composites to create sustainable materials is gaining more attention due to their appealing attributes, such as low cost, low density, and good mechanical properties. The development of Self-Compacting Concrete (SCC) in the construction industry continuously improved over the last decade due to its advantage of having self-consolidation capability. However, a lack of studies on how SCC behaves when introducing natural fiber still arises. Hence, the present work investigates the effect of Coir fiber (CF) as reinforcement to SCC. In addition, this study provides information on the extraction process, surface characterization using Scanning Electron Microscopy (SEM), and tensile strength of the Coir fiber. Finally, the coir fiber was applied to SCC by varying the dosage ranging from 0%, 1%, 1.5%, and 2% by weight of cement to determine the fresh properties and compressive strength. The fresh properties were assessed using slump flow, T500, L-box, and GTM Screen stability tests. Furthermore, the compressive strength of the new composite was determined after 28 days. The results reveal that adding coir fiber significantly affects the fresh properties of SCC because of its hydrophilic nature. Despite the reduction in the flowability, passing ability, and segregation resistance as the fiber increases, the SCC with CF is within the acceptable ranges specified on the EFNARC standard except for the mixture with 2% coir fiber. Additionally, incorporating 1% coir fiber achieves 12.84% higher compressive strength with less cracking pattern and failure mode. Thus, it emphasizes that it can be utilized fully in the construction industry to reinforce SCC.

Massive open online courses (MOOC) have gained continuous popularity in the emergence of Industry 4.0. These online courses allow individuals to enroll and learn at their own time and pace. However, studies show that the completion rate of these courses could be much higher. In this study, the researchers approached MOOC learners from different educational and professional levels and used thematic analysis to determine the motivational factors contributing to the retention and completion of MOOCs among Filipino learners. The study showed that independence, engagement, course content, and rewards are the motivational factors that mainly contribute to course completion. These results are supported by the self-determination theory and several researchers who delve into topics like this study. This research contributes to the field as it sheds some light on the perspectives of learners from a country with very scarce local MOOC providers. Lastly, results from this study may be utilized by Philippine higher education institutions and government agencies when designing MOOCs for Filipino learners.

Education for Sustainable Development (ESD) is essential for promoting sustainability and environmental stewardship among students. However, the intent of Filipino teachers to incorporate ESD principles into Physical Education and Health remains underexplored. This study examines the relationships between attitudes towards ESD, subjective norms, perceived behavioral control, behavioral intentions, self-reported behavior, subjective task value, ESD knowledge, and ESD integration beliefs among 363 educators. Utilizing PLS-SEM, the study finds perceived behavioral control as the strongest predictor of both behavioral intentions and self-reported behaviors, underscoring its role in enabling educators to implement ESD practices. ESD knowledge significantly influences perceived behavioral control, suggesting that enhancing knowledge could boost educators' confidence in ESD integration. Additionally, ESD integration beliefs impact attitudes and behavioral intentions. These findings offer insights for targeted interventions to support ESD integration within PE and Health curricula.