With the increasing popularity of Raspberry Pi (RPi) comes the increase in available use cases and projects that utilize this single-board computer. One of these uses cases is the RPi Cluster, where multiple nodes are connected to a local network to form one logical resource pool. Implementing this build, however, can be a tedious and needs manual intervention such that each node needs to be configured to the cluster, one-by-one. While easily implementable if the scale is relatively small, the task becomes complex if several nodes need to be configured all at once. To save time, as well as to automate the whole process, developers and hobbyists would use Docker Swarm as an alternative. This paper explores the possibility of going beyond the popular Docker Swarm by proposing the use of OpenStack, a Cloud Computing Platform, as an alternative. Document analysis and review of existing research were used to prove the build’s technical feasibility. After comparing five relevant use cases, it can be concluded that deploying OpenStack to the RPi Cluster, or OpenStack-on-RPi, is feasible, though further research and testing that will optimize this use case is recommended.

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.