The Technical Education and Skills Development Authority (TESDA) in the Philippines issues National Certificates (NCs) which is an important credential for graduates and skilled workers, affirming their capabilities in line with defined competency standards. However, with the advancement in information technology and the availability of affordable editing tools in the market raised concerns about the creation of counterfeit documents including NCs. The study focused on creating a smart credentialing and verification system for issuing National Certificates using blockchain technology. Researchers used Polygon blockchain that implements Proof-of-Stake consensus algorithm for system's efficiency and security. Certificates generated by the system are stored on the blockchain, with each certificate assigned a unique address for verification purposes. The system was assessed using ISO/IEC 25010 standards, and respondents provided good feedback on a variety of parameters. Future development recommendations include integrating a mobile application for easier certificate access and verification, providing real-time updates, establishing a feedback mechanism, and implementing analytics to gain insights into certificate issuance and user engagement.

Wind forces on structures have the potential to cause significant damage. A database involving the distance from the ridge, enclosure classification, surface type, elevation above ground level, wind direction, basic wind speed, presence of wall/surface openings, and effective net wind pressure (ENWP) was created using computation fluid dynamics (CFD). This paper focuses on the development of a model for predicting ENWP using a backpropagation-artificial neural network (BP-ANN). Utilizing the Levenberg-Marquardt algorithm (LMA) and hyperbolic tangent sigmoid function (HTSF) as the model hyperparameters, the study investigated several network structures and the simulations revealed that the 7-20-1 is the best model among the topologies observed in this study. The results showed an R value of 0.99868, MSE and MAPE of 0000749 and 5.036%, respectively. Additionally, the Akaike Information Criterion (AIC) was used as another layer of metric to measure the effectiveness of the model. The least was observed in the 7-20-1 network structure indicating that this is the best among the topologies observed in this study. Moreover, a sensitivity analysis (SA) through Garson's Algorithm (GA) was performed to determine the relative contribution (RC) of the input parameters (IP) including the distance from the ridge, enclosure classification, surface type, elevation above ground level, wind direction, basic wind speed, and presence of wall/surface opening to the effective net wind pressure. The findings presented that the basic wind speed is the most significant parameter to the effective net wind pressure value. The results of this study can be utilized in considering appropriate configuration to minimize the effects of wind pressure in structures.

There have been discussions about the gender gap in STEM majors. While some fields (e.g., Biomedical Sciences) have a high proportion of women workers, the Computer Science (CS) and Software Engineering (SE) disciplines are lacking female specialists. Universities worldwide are implementing different practices to attract more women to the CS and SE programs. This literature review aims to collect literature on this topic, identify the research tendencies, and collect female-inclusive practices. This paper presents the main findings from analyzing 143 selected papers from five academic databases (IEEE, ACM, Web of Science, Science Direct, and Scopus). The analysis revealed the need for inclusivity across all education stages, emphasizing practical studies beyond the classroom. Twenty-eight gender-inclusive practices were identified.

The research examines the efficacy of multimedia technology in augmenting technology-enhanced learning and instruction to address educational disparities. The incorporation of multimedia components into educational settings has grown more widespread due to the fast progress of technology. Nevertheless, the degree to which multimedia technology aids in resolving educational inequalities has not been thoroughly investigated. The research used a mixed-methods approach, including both quantitative surveys and qualitative interviews, to investigate the experiences and attitudes of educators and learners on the incorporation of multimedia technology in educational settings. The research aims to ascertain the advantages and constraints of multimedia technology in promoting efficient learning environments, specifically in varied educational contexts. The research seeks to analyze the effects of multimedia technology on student engagement, comprehension, and information retention. The goal is to get a better understanding of how to maximize the use of multimedia technology in order to reduce educational inequities and improve learning outcomes. The results will provide valuable insights for educators, policymakers, and curriculum writers on successful methods to use multimedia technology in educational activities.

Educational institutions orchestrate a variety of in-school events and activities to enrich the student experience. Given their benefits, it is crucial to encourage student participation. With the recent advent of the metaverse, there is an opportunity to engage students due to their inclination toward adopting such technologies. However, the dynamics of celebrating school events within these virtual worlds remain largely unexplored. Our study sought to address this gap by examining school event celebrations in the metaverse through an appreciative inquiry approach. During Valentine's Day, we introduced a special edition of our educational metaverse (i.e., MILES Virtual World) tailored to celebrate the occasion. We discovered that conducting school events in the metaverse requires the integration of real-life social rituals to augment students' social experiences and foster a sense of community. Moreover, the need for realism and the mirroring of real-world traditions in virtual settings emerged as critical drivers for creating more emotionally satisfying and engaging user experiences. The challenge of encouraging student participation in physical events parallels the issue encountered in virtual worlds, where students may feel discouraged from participating if they do not observe their friends' presence within the metaverse. Our study also calls for collective engagement in shaping the virtual world to ensure more inclusive, engaging, and enriched educational metaverses. As we continue to navigate the evolving landscape of immersive digital environments, the insights gained from our research underscore the importance of collaboration, innovation, and student agency in shaping the future of education.

In the competitive area of sports, injuries not only jeopardize athletes' careers but also lead to substantial setbacks for teams and organizations. Addressing this critical issue, our study introduces an artificial intelligence (AI)-driven model that enhances injury management through the strategic implementation of rest periods during athletes' recovery phases. By leveraging data analytics to monitor athletes' health continuously, this model offers sports managers a predictive tool for a proactive and preventative approach to injury management. Our research analyzes athletes' performance and health data across various sports disciplines by employing advanced machine learning techniques to identify patterns related to training regimes, treatment strategies, and the consequent risk and severity of injuries. Our findings underscore the utility of AI in generating actionable insights, thereby enabling more informed decision-making that centers on athletes' well-being. Notably, they demonstrate the model's success in predicting injury risks with high accuracy, subsequently informing tailored intervention strategies that significantly reduce the incidence of injuries. Furthermore, our study highlights how AI technologies can revolutionize training environments by enhancing safety and improving the efficacy of injury prevention and rehabilitation strategies. By advocating for the adoption of AI and technology in sports science, our study not only contributes to enhancing athlete care but also paves the way for future research to optimize athlete performance and health. Overall, this research highlights the role of AI-driven analysis in advancing sports medicine by offering a blueprint for coaches, sports medicine professionals, and athletes alike to navigate the complexities of injury prevention and management.

To ensure students achieve timely and satisfactory graduation, it's essential to assess their future performance based on ongoing academic records and implement instructional interventions. Within the educational context, students fall into two categories: regular and irregular, each governed by distinct academic regulations. Regular students follow a predetermined curriculum, which provides a clear path to graduation and enhanced access to required courses, facilitating efficient progress toward degree completion. On the other hand, irregular students encounter challenges such as disruptions and delays, necessitating additional time and support to fulfill degree requirements. Guiding both regular and irregular students and improving their study plans require appropriate guidance and academic intervention. To address the existing research gap, this study presents a Criteria-based Recommender Platform for Achieving Optimal Time-to-Graduation Utilizing a Backward Chaining Algorithm. This platform automatically generates a personalized study plan by considering predefined criteria and parameters, enabling students to evaluate the timeline for completing their degree program. By leveraging the backward chaining algorithm, the platform's predictive model captures intricate relationships and dependencies within the data, providing valuable insights and predictions. This adaptive approach continuously refines predictions based on new data, enhancing accuracy and utility in guiding decision-making processes related to study plan generation.

Outcome-Based Education (OBE) emphasizes the importance of defining and assessing specific learning outcomes. Effective monitoring of these outcomes is crucial for ensuring student success and program effectiveness. Previous research has explored various approaches to enhance program outcome monitoring, however, have not fully addressed the need for individualized and comprehensive progress tracking that goes beyond binary pass or fail measurements. This paper presents a novel approach to enhance program outcome monitoring through the development of individualized dashboards and the application of an adaptive neuro-fuzzy logic (ANFIS) framework. Data were derived from CSV reports of students in a learning management system and Canvas New Analytics from a sample class in the pilot study. The ANFIS framework is based on formative and summative assessments, total and maximum page views and participation, and average weekly page views and participation. The ANFIS model and dashboard results demonstrate its effectiveness in providing students and educators with a deeper understanding of student progress in terms of program outcomes, enabling targeted interventions and personalized learning experiences. This comprehensive approach empowers educators with the tools and insights needed to optimize educational practices and ensure that all students achieve the desired learning outcomes.

Visual impairment continues to be a global health concern. People with visual impairment experience difficulty moving around indoors, especially in unfamiliar spaces. While existing assistive technologies like smart canes offer point-to-point navigation or rely on infrastructure like RFID tags or beacons, they lack the ability to provide comprehensive indoor navigation with obstacle detection and avoidance. This paper presents a novel indoor navigation system for visually impaired individuals using deep learning and audio guidance. The system utilizes 3D-printed glasses equipped with a Raspberry Pi v2 camera, audio user interface and a processing unit comprising a Raspberry Pi 4B and Google Coral Edge tensor processing unit (TPU). As validated in a controlled indoor environment, the deep learning models for localization, navigation, obstacle detection, and obstacle avoidance achieve high results in terms of accuracy, precision recall, and F1-score. Based on user tests using the System Usability Scale, this wearable assistive device appears to offer a promising solution for promoting independent navigation and spatial awareness among visually impaired individuals.

Amidst the clear-cut changes constantly happening in the educational landscape, Higher Education Institutions (HEIs) are continuously pursuing graduates that meet global standards. The rise of remote jobs from previous years opened a gateway of opportunities for Filipino graduates to ensure employment from various multinational employers. To maintain this, HEIs in the Philippines must be able to offer quality education and programs that meet exceptional standards. This study aims to address the inability of tertiary institutions to track the competencies that the students have gained by integrating the outcome-based education (OBE) framework through an online platform. This paper also enumerates the benefits of having an OBE Management system such as achieving a holistic view of evaluating students' competencies, the system integrates educational data from various sources such as grading system, Learning Management System (LMS), and surveys. The system development research process is conducted in this study. One of the objectives of this study is the integration of drill-down analysis into the OBE Management system. This allows users to create reports easily and faster, furthermore, it aids the country in achieving Sustainable Development Goal (SGD) 4 for Quality Education. The premise of the study also contributes to the impact of system development on attaining quality education for HEIs.