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.

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.

Computer programming is now one of the most critical competencies taught in computer courses. [1]. Students require any assistance they can get when learning programming in order to acquire the necessary abilities to excel in the field of computing [2]. This paper aims to investigate the C compiler error logs of Computer Science freshmen students. A prototype was developed and pilot-tested to obtain C source code snippets focusing on assignment statements. The dataset consisting of 1013 logs were extracted from the initial prototype then followed the data science approach of [3] for pre-processing. A Person correlational analysis was conducted on eight features to investigate the relationship between all variables in the dataset. Results of the study show that there is a strong relationship between wrong expression and operator (0.806), wrong expression and numeric value (0.794), operator and numeric value (0.663). Implications of this study is also helpful to computing instructors to improvise the delivery of their teaching pedagogy.