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.

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.

Wave energy derived from oceanic kinetic forces is a highly promising renewable energy source. As global efforts to incorporate renewable energy into the grid increase, accurate wave energy forecasting becomes essential for optimizing energy harvesting and grid integration. This paper examines the latest developments in machine learning (ML) approaches, focusing on deep learning (DL), ensemble methods, and hybrid models used for forecasting ocean wave energy. It highlights the strengths and weaknesses of various approaches in capturing the complex nonlinear dynamics of ocean waves, including predicting energy flux, significant wave height (SWH), and wave period. Additionally, the paper explores how hybrid models, combining physical models with ML, have emerged as powerful tools for improving forecast accuracy over traditional methods. This review concludes with insights into future directions, emphasizing the potential of advanced techniques like transformers, generative adversarial networks (GANs), and real-time data assimilation for enhancing prediction reliability and computational efficiency.

The Philippines is advancing its renewable energy goals to achieve a 35% share by 2030. This study evaluates solar photovoltaic (PV), and wind power output forecasting methods currently employed in the Philippines, aiming to assess their accuracy against electricity market standards and identify potential improvements. The study systematically reviews articles emphasizing forecasting methods, including physical, statistical, machine learning, and hybrid models. The methodologies encompass a range of forecasting horizons and utilize a diverse set of input variables that influence forecasting accuracy. A key finding from the literature is the variability in the accuracy of these forecasting models, with many not meeting the stringent Mean Absolute Percentage Error (MAPE) threshold of 18% set by the Philippines' Wholesale Electricity Spot Market (WESM). This emphasizes the need for enhanced forecasting models to mitigate economic losses and improve grid stability significantly. Furthermore, this study suggests integrating more sophisticated, data-driven forecasting models to improve accuracy. Such advancements are critical for managing the intermittent nature of solar and wind energy and making informed decisions on energy policy and investment in the Philippines. The study also identifies gaps in current forecasting practices and recommends avenues for future research, particularly in developing models that align better with the operational standards and real-time demands of the energy market.

3D printed shelters offer a promising approach to accelerating shelter provision and enhancing resilience in disaster-affected regions. This study investigates the potential of 3D printing technology in revolutionizing shelter construction, particularly for disaster relief and humanitarian aid. By examining the technical feasibility, social implications, and environmental impacts of 3D printed shelters, this research aims to identify key challenges and opportunities for their widespread implementation. The study explores the integration of essential utilities into 3D printed shelters, their adaptability to various environmental conditions, and the importance of community engagement in the construction process. Additionally, the economic viability and environmental sustainability of this technology are assessed. Through a comprehensive analysis of existing research and case studies, this paper provides insights into the potential of 3D printing to address the critical need for rapid, affordable, and resilient shelter solutions in disaster-affected areas.

The adoption of innovative technologies is critical for improving teaching practices and student learning outcomes. Among these, artificial intelligence (AI) is emerging as a transformative tool capable of reshaping traditional educational paradigms. ChatGPT, a sophisticated language model developed by OpenAI, offers numerous opportunities for educators to enhance pedagogical effectiveness and streamline lesson preparation processes. This study explores the efficacy of ChatGPT in lesson preparation by surveying and interviewing teachers at Dr. Josefa Jara Martinez High School in the Philippines. It aims to understand their attitudes towards and experiences with integrating ChatGPT into their teaching practices. Despite the promising potential of AI in education, the adoption of such technologies in the Philippines faces significant barriers, including limited awareness, access issues, and concerns about technology integration. The findings reveal that while teachers recognize the benefits of using ChatGPT, such as improved efficiency and personalized instruction, challenges like lack of training and ethical concerns remain prevalent. The study underscores the need for comprehensive professional development programs and robust ethical guidelines to support the effective and responsible use of AI tools in education. The results show that teachers have a wide range of opinions, but many of them agree that ChatGPT has the potential to make lesson planning easier, offer individualized learning resources, and keep students interested in class. On the other hand, issues with consistency with curriculum requirements, dependability, and general efficacy were also apparent. The study sheds light on the challenges associated with integrating AI into education and makes recommendations for professional development, focused assistance, and ethical considerations to help high schools adopt AI technologies responsibly. Teachers can optimize learning experiences, improve teaching effectiveness, and give students the tools they need to succeed in the digital age by tackling these issues and utilizing AI's transformative potential.

This study focuses on the development and implementation of an enterprise-level information system for State Universities and Colleges (SUCs) in the Philippines, specifically addressing the mandates of Instruction, Research, and Extension. The study adopts a sequential exploratory mixed-method approach, utilizing the Agile System Development Model for system development. The system's effectiveness and acceptability were evaluated using quantitative data from 20 IT experts and 100 end-users, and qualitative data from interviews and secondary data. The study also conducted a survey to assess the system's acceptability in terms of flexibility and configuration. The findings reveal that the system received an average weighted mean of 3.44 for flexibility and 3.39 for configuration, indicating a good level of acceptability among end-users. The study also identifies several strategic implementation strategies for the deployment of the system to interested SUCs, including policy integration and risk management. The study provides valuable insights into the development and implementation of enterprise-level information systems in educational institutions, highlighting the importance of aligning digital governance with institutional mandates and requirements.

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements— creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

This study aims to identify the most critical attributes influencing retention and dropout risk in the Alternative Learning System (ALS) by analyzing various demographic, socio-economic, academic, and behavioral factors. Using Gradient Boosting Decision Trees (GBDT) for predictive modeling, the research explores feature importance scores to rank and prioritize the key attributes. The researcher used Knowledge Discovery in Databases as analytics methodology. Using principal component analysis, it was identified that regular attendance, availability, financial support, parental cohabitation (living together), and internet access positively influence retention. Furthermore, attending public schools, having a widowed parent, and possibly other features like distance to school are linked to increased dropout risk. The results provide insights into the main factors affecting student success, enabling more focused and data-driven interventions. The findings can help ALS administrators and educators develop personalized support plans for at-risk students and allocate resources more effectively.

Seepage is a critical factor influencing the stability of earth dams, as uncontrolled seepage can result in internal erosion, piping, and structural failure. This paper proposes assessing how effective a filter drain is in reducing the exit gradient and managing seepage near the downstream slope of a homogenous earth dam. The study utilizes SEEP/W software for modeling and analyzing seepage dynamics in a homogenous and isotropic earth dam. The results indicate that without a filter drain, seepage flow is directed toward the toe of the dam, a particularly vulnerable point where structural collapse or damage is most likely to occur. However, with the installation of a filter drain, the seepage flow direction and the phreatic line are shifted away from the toe, thereby reducing the risk of instability. The findings also reveal that variations in the length of the filter drain influence the exit gradient, while the assumed permeability values have a minimal impact on the exit gradient. These results provide valuable insights into optimizing filter drain design for improving the stability and safety of earth dams.