Outcome-Based Education (OBE) emphasizes the importance of defining and assessing specific learning outcomes. Effective monitoring of these outcomes is crucial for ensuring student success and program effectiveness. Previous research has explored various approaches to enhance program outcome monitoring, however, have not fully addressed the need for individualized and comprehensive progress tracking that goes beyond binary pass or fail measurements. This paper presents a novel approach to enhance program outcome monitoring through the development of individualized dashboards and the application of an adaptive neuro-fuzzy logic (ANFIS) framework. Data were derived from CSV reports of students in a learning management system and Canvas New Analytics from a sample class in the pilot study. The ANFIS framework is based on formative and summative assessments, total and maximum page views and participation, and average weekly page views and participation. The ANFIS model and dashboard results demonstrate its effectiveness in providing students and educators with a deeper understanding of student progress in terms of program outcomes, enabling targeted interventions and personalized learning experiences. This comprehensive approach empowers educators with the tools and insights needed to optimize educational practices and ensure that all students achieve the desired learning outcomes.

Visual impairment continues to be a global health concern. People with visual impairment experience difficulty moving around indoors, especially in unfamiliar spaces. While existing assistive technologies like smart canes offer point-to-point navigation or rely on infrastructure like RFID tags or beacons, they lack the ability to provide comprehensive indoor navigation with obstacle detection and avoidance. This paper presents a novel indoor navigation system for visually impaired individuals using deep learning and audio guidance. The system utilizes 3D-printed glasses equipped with a Raspberry Pi v2 camera, audio user interface and a processing unit comprising a Raspberry Pi 4B and Google Coral Edge tensor processing unit (TPU). As validated in a controlled indoor environment, the deep learning models for localization, navigation, obstacle detection, and obstacle avoidance achieve high results in terms of accuracy, precision recall, and F1-score. Based on user tests using the System Usability Scale, this wearable assistive device appears to offer a promising solution for promoting independent navigation and spatial awareness among visually impaired individuals.

Amidst the clear-cut changes constantly happening in the educational landscape, Higher Education Institutions (HEIs) are continuously pursuing graduates that meet global standards. The rise of remote jobs from previous years opened a gateway of opportunities for Filipino graduates to ensure employment from various multinational employers. To maintain this, HEIs in the Philippines must be able to offer quality education and programs that meet exceptional standards. This study aims to address the inability of tertiary institutions to track the competencies that the students have gained by integrating the outcome-based education (OBE) framework through an online platform. This paper also enumerates the benefits of having an OBE Management system such as achieving a holistic view of evaluating students' competencies, the system integrates educational data from various sources such as grading system, Learning Management System (LMS), and surveys. The system development research process is conducted in this study. One of the objectives of this study is the integration of drill-down analysis into the OBE Management system. This allows users to create reports easily and faster, furthermore, it aids the country in achieving Sustainable Development Goal (SGD) 4 for Quality Education. The premise of the study also contributes to the impact of system development on attaining quality education for HEIs.

Social relationships are important to human well-being and interaction. Recently, there has been growing interest in how they are formed in various digital media, including social media and video games. Amid the rise of digital engagement, the metaverse has emerged as an essential virtual environment for social interaction. Unfortunately, there is limited understanding of how social relationships are developed and maintained within the metaverse. In this study, we explored the dynamics of social relationship development in a metaverse world. Using a one-shot case study, we assessed the roles of immersion and embodiment, as well as the moderating effect of copresence, in building social relationships through multiple regression analysis. Our findings show that with higher levels of immersion and embodiment, the formation of social relationships in the metaverse is significantly improved. Copresence further intensifies these effects, which is indicative of its crucial role in virtual social interactions. These results indicate that enhancing the immersion, embodiment, and copresence elements in metaverse environments can encourage stronger social bonds among users. Overall, this study advances our understanding of online social relationship formation in the metaverse environments and its design and development.

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. Limited research on Cebuano has hindered linguistic documentation and understanding of its grammar and vocabulary. This study introduces a Cebuano POS tagger using the Hidden Markov Model (HMM) to improve Cebuano text processing. The researchers also propose a method for handling unfamiliar words. Results show the algorithm performs well on a news text corpus of 25,000 datasets, with an accuracy of 84 %, precision of 80%, recall of 81.52%, and F1-score of 82%. These outcomes demonstrate the algorithm's effectiveness in addressing language challenges in specific genres. Additionally, the research contributes to the Sustainable Development Goals (SDGs) by promoting linguistic diversity and fostering inclusive language technologies. The study provides insights into Cebuano's linguistic traits and grammatical structures, offering a foundation for further research in natural language processing.

This research study uses the Multinomial Nave Bayes (MNB) algorithm to categorize and analyze the user experience (UX) of users of mobile commerce applications. The goal of the study is to give business owners insightful information on how well their mobile applications are performing. The study's goals are to establish evaluation standards for categorizing user experiences, use MNB to classify user experience reviews to their appropriate UX elements, analyze the results of the classification, and suggest areas for improvement to enhance the usability of m-commerce. The research plan consists of a number of sprints, including data extraction, data cleaning, classification system creation using the Multinomial Naive Bayes algorithm, and model accuracy rate evaluation. The proposed system integrates the algorithm and uses data from m-commerce applications. The results of the analysis provide insights into the different UX elements such as Value, Adoptability, Desirability, and Usability. The analysis's findings shed light on many UX components like Value, Adoptability, Desirability, and Usability. The classification model was evaluated for accuracy, achieving a result of 89.243%. This means that the model correctly classified 89.243% of the user experience reviews in the evaluation dataset, indicating a satisfactory level of accuracy. However, there were some misclassifications in the remaining 10.757% of the reviews. Therefore, the research successfully developed a system that analyzed and classifies user experiences from customer reviews using MNB. The classification model demonstrated a satisfactory level of accuracy. The findings provide valuable insights and recommendations for improving the mobile application browsing experience based on user feedback and experiences.

Field trips are steadily declining due to limited funding, time constraints, safety concerns, and other logistical issues. Many schools are resorting to a virtual field trip (VFT), especially when education is disrupted due to public health concerns, natural disasters, or other unforeseen significant events. Virtual reality as a common form of VFT is likely not an option for many schools due to cost and other barriers. The purpose of our study was to explore the potential of going in a VFT using 360-degree (360°) videos as an alternative to a physical field trip in primary education. We recruited third-grade pupils (aged 8–9) from two private elementary schools to experience VFTs using 360° videos (360V) and regular videos (REGV). Using a switching-replications experimental design, we compared their content recall (assessment tests) and VFT experience (attitude, perceived usefulness, involvement, inquiry, video engagement, and virtual guide) across four-time points. Our results show that the increase in content recall scores of 360V groups after VFTs was consistently higher compared to REGV groups at all time points, although it was only significant in one quarter. We also found pupils’ video engagement, involvement, and attitude as significant factors in their VFT experience. These results call attention to a possible implementation of VFTs and continue the long-standing tradition that has been acknowledged as a student-centered, interactive instructional method.

The food service industry has increasingly adopted e-commerce as a means of improving service and accessibility for customers. The popularity of e-commerce mobile applications has also surged due to their convenience on mobile devices, which have become an integral part of people's daily lives. These applications enable businesses to enhance their market presence and attract a larger customer base. Additionally, food businesses are utilizing Financial Management Systems (FMS) to effectively track cash inflows and outflows. FMS is typically integrated with various features such as sales and inventory tracking. The primary objective of this study was to develop an e-commerce coupled with a financial management system and its mobile application counterpart. The employed methodology for this study was descriptive research. A survey was conducted to evaluate the acceptability of the system based on functionality, usability, reliability, performance, and supportability (FURPS) criteria. The developed system received a favorable rating, with a weighted mean of 4.05. This indicates that the system successfully executed required tasks, offered user-friendly features, effectively monitored orders, consistently performed, and was accessible from any device.

Learning programming languages like Python can be difficult, especially for those who have no background in the subject. This study intends to design a mobile application and online Python learning lesson to solve the challenges experienced by senior high school S.T.E.M. students. S.T.E.M. students in senior high school who participated in a survey found that academic pressure, a lack of engagement, and a lack of practice chances were the main issues affecting their learning experience. For beginner to advanced levels, the PySkuwela provides a user-friendly interface with video tutorials, hands-on activities, and assessments. The PySkuwela forum promotes student cooperation and knowledge exchange. Students may study Python conveniently and without added academic pressure thanks to the self-paced learning strategy. The FURPS evaluation gave the system an overall acceptability rating of 4.21, which is a high level of agreement. According to the study’s findings, the created system is advantageous and appropriate for senior high school S.T.E.M. pupils, offering a thorough platform for learning Python.

The field of health and medical sciences has witnessed a surge of published research exploring the applications of ChatGPT. However, there remains a dearth of knowledge regarding its specific potential and limitations within the domain of nutrition. Given the increasing prevalence of nutrition-related diseases, there is a critical need to prioritize the promotion of a comprehensive understanding of nutrition. This paper examines the potential utility of ChatGPT as a tool for improving nutrition knowledge. Specifically, it scrutinizes its characteristics in relation to personalized meal planning, dietary advice and guidance, food intake tracking, educational materials, and other commonly found features in nutrition applications. Additionally, it explores the potential of ChatGPT to support each stage of the Nutrition Care Process. Addressing the prevailing question of whether ChatGPT can replace healthcare professionals, this paper elucidates its substantial limitations within the context of nutrition practice and education. These limitations encompass factors such as incorrect responses, coordinated nutrition services, hands-on demonstration, physical examination, verbal and non-verbal cues, emotional and psychological aspects, real-time monitoring and feedback, wearable device integration, and ethical and privacy concerns have been highlighted. In summary, ChatGPT holds promise as a valuable tool for enhancing nutrition knowledge, but further research and development are needed to optimize its capabilities in this domain.