The Technology Acceptance Model (TAM) serves as a foundational framework for assessing user acceptance of emerging technologies, particularly in organizational settings. In education, digital governance has become a transformative tool for enhancing operations within State Universities and Colleges (SUCs). However, many SUCs face challenges due to their reliance on commercial applications that lack the capacity for comprehensive and accurate reporting. To address this issue, the researcher developed an enterprise-level information system tailored to integrate research, teaching, and extension functions for SUCs in the Philippines. Utilizing the Agile Development Model, which emphasizes iterative progress through continuous improvement, the study employed a descriptive developmental approach to system creation and evaluation. The TAM criteria guided the evaluation process, resulting in an overall weighted mean of 3.55, interpreted as “Acceptable.” While the system has been positively received, the findings highlight the need for further refinement to optimize its effectiveness. Despite this, the system is strongly recommended for deployment in SUCs, as it offers a comprehensive, customizable solution for enhancing digital governance in higher education. This study underscores the importance of applying frameworks such as TAM to evaluate and refine technological innovations, ensuring their alignment with organizational needs and their contribution to improving digital governance within SUCs.

This study presents an e-commerce platform with customer analysis using hybrid collaborative filtering, developed specifically for the EveryDay streetwear brand to enhance their online presence and improve their business operations. With the rise of online shopping and social commerce in the Philippines, the platform aims to enhance user experience by providing tailored product suggestions using hybrid collaborative filtering. It integrates features like order, payment, inventory management, and product customization, allowing users to personalize their shopping experience, while customer behavior analysis and monthly reporting help improve decision-making and operational efficiency. To guide the development of the platform, the team used the SCRUM methodology. The system's architecture emphasizes data security, user privacy, and reliability through the ISO 25010 Software Quality Model. Surveys were conducted with a total of 75 respondents to measure the system's performance based on the model's parameters. The system scored a weighted mean of 4.81 from customers, 5.00 from both staff and admin, and 4.88 from IT experts, resulting in an overall rating of “Excellent.”. This study highlights the potential of hybrid recommender systems in enhancing e-commerce platforms and driving customer engagement and sales.

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.

Forest devastation is a critical environmental issue with severe ecological consequences. The Philippines, in particular, faces significant challenges in conservation efforts, compounded by a lack of public awareness about the effects of deforestation. To address this gap, we developed “Leshy: A 3D Action-Adventure Game Using Character Switching Mechanics for Promoting Awareness on Combating Forest Devastation.” This game immerses players in a virtual environment that reflects the real-world impacts of deforestation, leveraging character-switching mechanics to enhance engagement and education. “Leshy” aligns with Sustainable Development Goal (SDG) No. 15 - Life on Land, offering a unique and interactive approach to inspire action against forest devastation. The game's effectiveness was evaluated based on Gameplay, Aesthetics, User Interface (UI), Sound Design/Audio, and Storyline, achieving an overall average score of 4.44, rated as “Excellent.” This high rating indicates the game's success in engaging players with its captivating gameplay, appealing visuals, intuitive interface, immersive sound, and compelling narrative. The accompanying website, assessed using the FURPS model (Functionality, Usability, Reliability, Performance, and Supportability), received an overall average score of 4.50, also rated as “Excellent.” This demonstrates the website's user-friendly interface, dependable reliability, and strong performance, ensuring a seamless user experience. In conclusion, “Leshy” effectively addresses the issue of forest devastation by combining educational content with an engaging gaming experience. This project highlights the potential of interactive media in raising environmental awareness and inspiring conservation efforts. Further development and promotion of similar educational games are recommended to enhance their impact on environmental conservation.

This descriptive-developmental study enables the authors to create a graduate tracer monitoring platform. The paper aims to provide a centralized channel to monitor institutions' graduates in terms of their job employment, assessing academic programs using modified instruments which determine necessary interventions that may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study used a Decision Support System and mapping recommendation analysis using time series analysis to evaluate the results of alumni program evaluation on five areas or dimensions such as curriculum, faculty, facility, laboratory, and student services. The study may set the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implement the interventions for each area. A content management system was also used in this paper to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. The developed web-based system includes an evaluation of the Alumni Program across key areas such as Curriculum, Faculty, Facility, Laboratory, and Student Services. This study employed a purposive sampling technique to identify the group of respondents. There are a total of 152 respondents who participated in this study from the Information Technology department and IALAP office. The study results indicate that no interventions are necessary in any of these areas, as both the mean and the composite mean surpasses the 3.50 threshold set in the system. Among the five areas, the faculty received the lowest passing mean, followed by student services and the laboratory. This underscores the potential for continuous improvement in these specific areas influencing the employability rate and skills of the alumni-participants. The time series analysis was conducted on a two-year dataset, covering 6 trimesters. The analysis revealed a positive improvement in evaluation scores as the trimesters progressed across five dimensions of alumni program evaluation. This suggests that alumni respondents consistently agreed in their evaluations of appreciation on the improvements made by the school administration which enhances their life experiences and technical skills during their stay in the campus.

Caring for a child with Autism Spectrum Disorder (ASD) presents numerous challenges for parents, often leading to psychological distress, depression, anxiety, and other health issues. Understanding and addressing the various behaviors exhibited by children with ASD can be particularly difficult. Despite advancements in the diagnosis and treatment of ASD, many families still struggle to access specialized care and support. To address these challenges, we developed ASDvisor, an innovative application designed to provide comprehensive support for parents of children with ASD. ASDvisor integrates valuable information, efficient documentation, decision support, educational resources, data tracking tools, and enhanced communication to improve the management of ASD care through a user-friendly web and mobile platform. The system's quality was evaluated using the FURPS model, which evaluates functionality, usability, reliability, performance, and supportability of the system. ASDvisor received excellent ratings, scoring 4.33 for the web application and 4.37 for the mobile application. Key findings highlighted the application's robust performance in tracking and managing ASD-related activities, offering valuable decision support through its Care Decision module, and fostering community engagement among users. ASDvisor effectively addresses the identified challenges, providing a reliable, efficient, and cost-effective tool for enhancing the quality of life for children with ASD and their families. This research demonstrates the potential for technology to significantly improve ASD care management.

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.

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.

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.