AMA Computer University

This study examined the content delivery readiness of selected Philippine Higher Education Institution websites using public performance data from the Chrome User Experience Report (CrUX) and HTTP Archive. The study covered 30 official website origins, consisting of 10 State Universities and Colleges, 10 private HEIs, and 10 Local Universities and Colleges. Each website was validated and normalized before data extraction. CrUX data for May 2026 and HTTP Archive page-level data from the May 1, 2026 crawl were collected through BigQuery. Of the 30 websites, 25 had complete data from both sources, four had CrUX data only, and one had no usable match. The readiness framework combined field indicators related to actual user experience with page-level indicators describing technical delivery conditions. Among the 25 complete-data websites, five showed high readiness, 13 moderate readiness, and seven low readiness. Private HEIs recorded the highest average score among the three groups, although the comparison was interpreted descriptively because the corpus was purposively selected. The most common issues were heavy page weight, slow visual loading, high request count, slow Time to First Byte, and slow Largest Contentful Paint. Sensitivity testing using three alternative weighting schemes showed that 84 percent to 96 percent of classifications remained unchanged, while rank correlations ranged from 0.962 to 0.980. These results indicate that the main findings remained generally stable across the alternative models, with most category changes occurring near the score boundaries. The resulting framework provides a practical basis for identifying and prioritizing improvements in payload size, loading behavior, request management, server response, and routine website performance monitoring.

The study introduces FaceLog, a two-layer authentication framework developed to add digital security by incorporating biometric authentication and multi-factor authentication (MFA). The first security layer utilizes a Convolutional Neural Network (CNN)–based facial recognition model with liveness detection to verify user authenticity in real time. Using the Eye Aspect Ratio (EAR) method, the system detects natural eye blinks to distinguish live users from spoofing attempts involving static or digital images. Once facial verification is successful, the system proceeds to second layer of protection, either a One-Time Password (OTP) or a Time-Based One-Time Password (TOTP) for identity confirmation. This structure ensures that even if one authentication factor is compromised, unauthorized access remains effectively prevented. Evaluation results demonstrate high accuracy, precision, recall, and F1-score, supported by excellent ratings in functionality, usability, and compatibility based the criterion of ISO/IEC 25010 software quality model. The findings affirm that combining biometric authentication with multi-factor verification provides a robust, efficient, and user-centered approach to secure modern login systems, addressing the growing challenges of cybersecurity in digital platforms.

Financial literacy is increasingly recognized as an important determinant of individuals’ capacity to make informed financial decisions and prepare for retirement, particularly among migrant workers adapting to unfamiliar financial systems. This study examined the relationship between financial literacy and retirement planning among immigrant Filipino nurses in Missoula, Montana, United States. Using a quantitative descriptive-correlational research design, data were collected from 38 immigrant Filipino nurses through a structured questionnaire administered using purposive and snowball sampling. Financial literacy was assessed across three domains: personal financial knowledge, financial attitude, and financial behavior, while retirement planning was examined in relation to pension preparation, insurance, property investment, financial market participation, and business investment. Descriptive results indicated that respondents demonstrated high levels of personal financial knowledge and financial attitudes and high financial behavior, while retirement planning was rated as very important. Pearson correlation analysis demonstrated significant positive associations between retirement planning and personal financial knowledge (r = .426, p = .008), financial attitude (r = .534, p = .001), and financial behavior (r = .576, p < .001). Among the financial literacy dimensions, financial behavior exhibited the strongest relationship with retirement planning. However, respondents reported comparatively lower confidence regarding financial instruments such as stocks and bonds, indicating an area for strengthening investment literacy. Demographic characteristics, including age, gender, income, and educational attainment, were not significantly associated with financial literacy. The findings suggest that strengthening practical investment education, retirement planning knowledge, and workplace-based financial education may enhance long-term financial preparedness among immigrant Filipino nurses. The study contributes context-specific evidence to the literature on financial capability, migrant workers, and retirement preparedness.

The TRAINER system is a personalized health and fitness solution developed for the Fitness Zone Fitness Center in Antipolo, Philippines, to address the shortcomings of generic, “one-size-fits-all” training programs. By utilizing a content-based recommender system driven by machine learning, the platform integrates individual user profiles—including fitness levels, personal goals, and dietary preferences—with a curated repository of workouts and nutritional plans. The system features a web-based interface for real-time data collection and progress tracking, employing a continuous feedback loop to ensure recommendations remain dynamic and adaptive. Ultimately, TRAINER seeks to enhance client adherence and health outcomes by providing data-driven, context-aware guidance that bridges the gap between limited instructor availability and the diverse needs of fitness enthusiasts.

In recent years, the expansion of e-learning platforms has revolutionized the educational landscape, offering learners the flexibility to access educational resources anytime and anywhere. However, the abundance of content often overwhelms students, making it difficult to identify which learning materials best suit their individual needs. To address this challenge, this study proposes the development of a Personalized E-Learning Recommendation System that utilizes the K-Nearest Neighbor (KNN) algorithm to tailor learning content based on each learner’s profile, behavior, and preferences. The primary objective of this research is to design and implement a data-driven recommendation model that enhances learner engagement and academic performance by providing customized content suggestions. The system collects various types of user data from the e-learning platform, including quiz scores, subject interests, time spent on modules, and interaction history. Each learner is represented as a feature vector encapsulating these attributes. By applying the KNN algorithm, the system identifies students with similar learning patterns and preferences and recommends educational resources that have proven effective for those peers. The study follows a design and development research methodology, where the system is built, tested, and evaluated through iterative processes. The prototype is developed using Python and its machine learning libraries, while a web-based interface is created using Django and integrated with a backend database to store user data. Evaluation metrics such as recommendation accuracy, user engagement rate, and improvement in quiz performance are used to assess the system’s effectiveness. Initial testing shows that learners receiving personalized recommendations spend more time on the platform and demonstrate higher content retention compared to those accessing randomly assigned materials. This research contributes to the growing field of intelligent e-learning systems by highlighting the effectiveness of simple yet powerful machine learning techniques such as KNN in improving personalization. It also emphasizes the value of learner data in shaping adaptive educational environments that cater to individual learning styles and needs. Future work may include expanding the dataset, incorporating hybrid recommendation models, and exploring deep learning approaches to further improve recommendation quality. By offering a scalable and adaptable framework, this personalized recommendation system has the potential to significantly enhance the digital learning experience, making online education more targeted, efficient, and impactful.