The proposed research aims to develop a hybrid learning analytics model that incorporates clickstream and sentiment mining techniques to accurately predict student retention in Learning Management Systems (LMS) in Manila-based HEIs. This proposed research aims to improve the limitations of single prediction modeling. The proposed research utilized a quantitative explanatory research design and was applied to undergraduate students taking general education courses. The proposed research utilized stratified sampling to select a total of 345 students. Clickstream and text-based interaction data were analyzed to obtain sequence and sentiment-based metrics to train the hybrid prediction model using Logistic Regression and XGBoost. The engagement tool was also able to reach Cronbach's alpha = 0.91, which is a good measure of internal consistency. This shows that the click-only model had an AUC of 0.78, but the hybrid model was enhanced to 0.84, with the top predictors being attempts at quizzes per week and negative sentiment ratio. This shows the importance of multimodal analytics in the improvement of early warning systems and proactive interventions. The implication of this can be seen in the administration of the institution and the academic support, which is a basis for evidence-based retention and fairness-based predictive models in Philippine Higher Education.

This study extended a previous paper that focuses on the acceptability of selected Bachelor of Science in Computer Science (BSCS) and Information Technology (BSIT) students on the use of Intelligent Tutoring System (ITS) as an educational technology tool for C/C++ Programming. A one-shot case study research design was carried out in 5 programming classes taught by the author. A Slovin's formula computation from the population was 35.54. A stratified sampling method was employed with the 4 intervals between students to mitigate bias. The study involved 39 participants, out of which 74.36% were male and 25.64% were female computer science and IT students. Utilizing the Technology Acceptance Model (TAM) as an evaluation tool online enabled importing the dataset into IBM SPSS for finding the correlations and factor loading calculations. Cronbach alpha was conducted by the author with a value of 0.947, which signifies the measure of internal consistency. The seven (7) factors of TAM were analyzed to reveal coefficient values for comparisons and derive their relative implications. Research indicates that every factor significantly influences the acceptance of ITS among BSCS and BSIT students. Interestingly, PerUse→Att has the highest coefficient value (0.883) next in the rank was SocNor→Att by a factor of 0.822 signifying their impact on ITS (Att), leaving SocNor→PerEas ranking last amongst relations with a 0.630 coefficient value. Finally, the results implied CS and IT students are open to the notion of incorporating intelligent teaching tools into their laboratory sessions to supplement their programming activity and increase their efficiency when building console applications.

Air quality monitoring has become increasingly critical in urban environments, particularly in densely populated megacities like Manila, Philippines. This research presents the design and conceptual framework for a comprehensive web-based air quality monitoring and mapping system that leverages fuzzy logic algorithms to provide intelligent, real-time assessment of atmospheric conditions across Metro Manila. The proposed system addresses the inherent uncertainties and complexities associated with environmental data by implementing a sophisticated fuzzy inference system specifically calibrated for Manila's unique atmospheric conditions, pollution sources, and regulatory requirements. The research encompasses a thorough analysis of Manila's current air quality challenges, including the identification of primary pollutants such as particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground level ozone (O3). The proposed system architecture integrates multiple technological components including a distributed sensor network, centralized data processing infrastructure, fuzzy logic engine, web-based visualization platform, and real-time mapping capabilities. The fuzzy inference system is specifically designed to accommodate Manila's tropical climate conditions, high population density, and diverse pollution sources ranging from vehicular emissions to industrial activities. The methodology incorporates adaptive membership functions that adjust to seasonal variations and local environmental patterns, ensuring accurate and contextually relevant air quality assessments. The system design emphasizes scalability, real-time processing capabilities, and user accessibility through responsive web interfaces optimized for both desktop and mobile platforms. The technical implementation framework encompasses comprehensive hardware specifications for sensor deployment, software architecture for data processing and visualization, database design for efficient time-series data management, and API development for system integration and third-party access. Expected outcomes of this research include improved public awareness of air quality conditions, enhanced decision-making capabilities for environmental authorities, and the establishment of a robust foundation for future environmental monitoring initiatives in Manila and similar urban environments. The fuzzy logic approach provides a more nuanced and human-interpretable assessment of air quality compared to traditional crisp methodologies, enabling better communication of environmental risks to diverse stakeholder groups. This comprehensive study contributes to the growing knowledge in environmental informatics and smart city technologies, demonstrating the practical application of artificial intelligence techniques in addressing real-world environmental challenges. The research provides a detailed roadmap for implementing intelligent air quality monitoring systems in developing urban environments, with particular emphasis on cost-effectiveness, technological accessibility, and community engagement.

Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Inadequate research on Cebuano religious text has hindered linguistic documentation and understanding its grammar and vocabulary. This study introduces a Parts-of-Speech Tagging for Cebuano utilizing a Modified Viterbi Algorithm. The researchers also apply a method for handling unfamiliar words. Results indicate that the algorithm performs exceptionally well on a religious text corpus comprising 50,000 datasets, achieving an accuracy of93%,precision of90%, recall of 90. 52%, and an F1-score of92%. These results highlight the algorithm's effectiveness in tackling language challenges within specific genres. Furthermore, the research supports the Sustainable Development Goals (SDGs) by promoting linguistic diversity and advancing inclusive language technologies. The study also provides valuable insights into Cebuano's linguistic characteristics and grammatical structures, laying a solid foundation for future research in natural language processing.

In this study, we use various RNN architectures namely, RNN, Bi-LSTM, and GRU — alongside BERT to analyze sentiment across university departments. Our aim is a comparative analysis of these models in sentiment classification within education. We collected and pre-processed textual data from multiple departments for balanced training and validation. Results showed that traditional RNNs achieved 90% accuracy, Bi-LSTM 93%, and GRU 89%. BERT, leveraging its Transformer architecture, outperformed with 94% accuracy. These findings highlight the superiority of BERT in capturing complex language patterns for sentiment analysis. This study underscores the potential of advanced neural network architectures to gain insights into departmental sentiments, informing policy decisions and educational strategies. Aligning with sustainable development goals in education, we aim to use AI models to develop effective, inclusive, and responsive educational strategies, enhancing quality and accessibility.