It was founded in 1938 as a law school by Florentino Cayco, Sr., the first Filipino Undersecretary of Public Instruction. The university was named after Cayetano Arellano, the first Chief Justice of the Supreme Court of the Philippines. It operates seven campuses located throughout Metro Manila and the main campus is located along Legarda Street, Sampaloc, Manila. The Arellano University School of Law is autonomous and managed by the Arellano Law Foundation. Its athletic team, the Arellano University Chiefs, is a member of the National Collegiate Athletic Association since 2009.
This study examines the alignment between Information Technology Governance (ITG) mechanisms and Good Governance Principles (GGP) among Local Government Units (LGUs) in Metro Manila. Anchored on IT governance theory and good governance frameworks, the research investigates how IT governance structures, processes, and relational mechanisms influence transparency and accountability. Using a quantitative, cross-sectional research design, survey data were collected from 472 LGU officials involved in IT management and governance functions. Reliability and validity assessments confirmed the robustness of the research instrument. Descriptive statistics, correlation analysis, and multiple regression analysis were employed to test the hypotheses. Results indicate that IT governance processes significantly and positively influence good governance principles, while relational mechanisms show a significant but negative association, and structural mechanisms exhibit no significant effect. The findings underscore the critical role of formalized IT processes in fostering transparency and accountability in the public sector. Thus, the study suggests path forward process-centric governance insights for policymakers and LGU administrators seeking to strengthen digital governance.
This study examined the effectiveness of contextualized Grade 7 reading instruction through the REACT Strategy in improving students' reading comprehension and determining their learning gains as inputs for the development of a reading training matrix. The study was anchored on Contextual Learning Theory (CTL), the REACT Strategy (Relating, Experiencing, Applying, Cooperating, and Transferring), and Coombs' System Approach. A quantitative quasi-experimental research design was employed involving 422 Grade 7 students from 13 sections of Pitogo High School during the School Year 2025–2026. A validated researcher-made 25-item pretest and posttest, supported by a Table of Specifications and implemented through contextualized Daily Lesson Logs, served as the primary research instruments. Data were analyzed using mean, standard deviation, and paired t-test. The findings revealed that students achieved a satisfactory level of learning before the intervention (M = 18.30, SD = 3.451) and a very satisfactory level after the implementation of contextualized reading instruction through the REACT Strategy (M = 21.983, SD = 1.994). The paired t-test confirmed a significant difference between the pretest and posttest scores, indicating that the intervention effectively improved students' reading comprehension. Students demonstrated enhanced ability to understand texts, identify key ideas and supporting details, infer meanings, and relate textual information to real-life experiences. These learning gains served as the basis for developing a teacher training matrix to strengthen contextualized reading instruction and improve instructional practices. The study concludes that contextualized reading instruction through the REACT Strategy is an effective approach for enhancing students' reading comprehension and recommends its continued implementation, supported by teacher training and regular monitoring. This study supports Sustainable Development Goal 4 (Quality Education) by promoting learner-centered, contextualized reading instruction that improves literacy and learning outcomes. Its sustainability impact lies in strengthening instructional practices and supporting the long-term development of effective, evidence-based reading programs in schools.
This study presented the development of a Smart Collaborative Learning Platform that integrated Natural Language Processing (NLP), deep learning models, and recommendation algorithms to automate content generation and enhance learning experiences. The system was designed to process uploaded learning materials and transform them into structured outputs such as summaries, quizzes, and flashcards. It utilized NLP techniques to analyze and understand semantic content, enabling accurate interpretation of user inputs and educational materials. Deep learning models were employed to generate meaningful summaries and insights that supported efficient studying. Additionally, a recommendation engine personalized learning by suggesting relevant topics based on user behavior and performance. The platform also incorporated collaborative features that allowed users to interact, share knowledge, and engage in real-time learning activities. The system was evaluated using ISO 25010 software quality standards, focusing on functionality, usability, reliability, and performance. Results indicated that the platform achieved high user satisfaction and demonstrated strong system performance. Findings showed that the system improved learning efficiency, reduced study time, and enhanced knowledge retention. Furthermore, the integration of AI technologies enabled adaptive and personalized learning experiences. The study highlighted the effectiveness of combining automation and collaboration in modern education. Overall, the proposed platform provided an innovative and scalable solution for improving digital learning environments.
The study examines the silencing of dropouts' voices. Specifically, this study investigates their perspectives on leaving school and how they navigate societal views, highlighting the influence of social and psychological factors on their experiences. Using ethnographic methods, the researcher immersed himself in the community as a participant-observer to gather insights into the central issue: how dropouts navigate schooling and the conditions that shape it. Findings reveal that: 1. The stigma around school dropouts stems from deeper structural inequalities and a history of marginalization. 2. Their identity centers on valuing practical over academic knowledge, which acts as a distinct form of cultural capital. The decision to drop out is deeply influenced by psychological and sociocultural factors. The study concludes that these dropouts' voices emerge from complex psychological, social, geographical, and political conditions they navigate as they construct their identities and occupy the margins of the educational system.
This research introduces Sort Out, an Intelligent Inventory Management System tailored to tackle the persistent issues of manual inventory tracking and product management in small grocery stores. Numerous small retailers encounter difficulties like excessive inventory, stockouts, erroneous recordkeeping, and slow reactions to changes in stock levels. To address these challenges, the system combines Descriptive Analytics with a Rule-Based Algorithm to automate essential tasks like product tracking, expiration monitoring, and report creation. The system's design is created according to the System Development Life Cycle (SDLC) approach. Information is collected from chosen mini grocery stores via interviews and observations to identify user needs. The development process employed PHP, MySQL, and XAMPP as main tools, with the interface design refined for ease of use and accessibility.
