The emergence of the metaverse has expanded the vital role of virtual worlds in education. It extends the traditional configuration of virtual spaces by offering a more immersive, interactive, and social experience. Unfortunately, there is insufficient literature on how to develop a metaverse application aligned with academic standards and best practices. In this study, we adopted a co-design approach in remodeling our mobile educational metaverse to draw a blueprint for existing and future metaverse technology adopters. We sent a follow-up survey to stakeholders inviting free-text comments. This survey focused on the challenges, issues, and expected features from the perspectives of developers, teachers, and students, respectively. Our results revealed several challenges that developers face when building a metaverse, such as technical infrastructure, student engagement, avatar customization, and performance optimization. Meanwhile, teachers emphasized potential issues that may arise from the use of metaverse technology in educational settings, such as learning outcomes, health and safety, digital citizenship, and ethics and morality. Finally, students expected several key features of an educational metaverse application, such as socialization and collaboration, virtual world interactions, metaverse optimization, realistic graphics, and minigames and activities. The implications of these findings are significant and can help other educational institutions seeking to integrate metaverse technology into their academic services. Overall, our study presents an initial foundation for further exploration and advancement within the rapidly evolving field of metaverse technology.

With the aggravation of the Russia-Ukraine conflict and the rising involvement of foreign powers, it has become more substantial to identify whether an endorsement or condemnation of war efforts is the universal message. This goal is empowered by the clear literature on the vital linkage between public opinion and international relations. Thus, we investigated the sentiments and emotions of the international community on the Russian invasion of Ukraine. A total of 27,894 tweets posted within the first day in the #UkraineRussia hashtag were analyzed. Results show that "war", "people", "world", "putin", and "peace" were some of the most frequently occurring words in the tweets. There were more negative sentiments than positive sentiments, and sadness was the most salient emotion. To date, this study is the first to examine the Russo-Ukrainian War and one of the few sentiment and emotion analyses for exploring Twitter data in the context of modern war.

The paper aims to examine the factors that affects the successful implementation of the Mastery-based Individualized Learning Enhancement System (MILES) in the Far Eastern University (FEU) Institute of Technology. Two periods were analyzed which are the initial implementation, and this is the start of the pandemic period, and the year after the initial implementation of MILES. The Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO)-based Sensitivity Analysis (SA) was utilized to determine the relative importance (RI) index among the influencing factors that affects the students’ level of satisfaction of the MILES implementation. Survey questionnaires were deployed through the canvas platforms and were answered by the students. In the initial survey, a total of 5763 students responded. For the SY 2020-2021, it was observed that the most influential variable to the student’s performance during the MILES Implementation is Course Adviser Rating while the parameters with the least impact to the student’s performance is the Student’s Status as regular or irregular student. For the survey on SY 2021-2022, the highest relative index is for the lesson preference while the lowest importance index is for the opportunities. Findings of the study shows that the use of NN-PSO based sensitivity analysis is an effective tool for establishing the significance of each variable to a target output.

Assistive technologies gained traction in the medical field over the last few decades. Novel approaches have been developed in order to support people with disability to communicate effectively. However, little research has been conducted on the other side of the coin, that is, assistive technologies to help people who do not have a disability to understand and comprehend the language of disabled. This study describes the early development of a hand alphabet recognition that intends to accomplish a functioning dactylology conversion from sign language to English print in a live streaming video. Through a video analysis, each frame is processed using a segmentation technique to partition it into different segments (e.g., pixels of hand gesture). The dactylology conversion algorithm was implemented in a mobile application where users can watch video containing an on-screen sign language interpreter and understand fingerspelling used as a communication by hearing- and speech-impaired people. Through the sample dataset of 13 videos of American Sign Language manually collected (N=10) and recorded (N=3), the application was tested for its accuracy in detecting the alphabet in a video (94.16%), and the correctness of conversion of the detected alphabet into English print (89.65%). This study contributes to the list of existing novel approaches that aims to promote social positive effects as well as improve the quality of life for both disabled and all the people they socialize with.

This research aims to determine the demographics that affect the solid waste management behavior in San Martin Subdivision in Angono, Rizal, conducted during the time of pandemic. This aims to improve sustainability and help create programs that will improve the participation of the households towards positive waste management. Currently, there are limited studies involving waste management, especially in developing countries like the Philippines, where waste management problems are more prevalent, adding more solid wastes during the pandemic period. This will help developing countries improve their waste management system as it will open areas for research and development. The researchers seek through the study of the demographics that affects the attitude, knowledge, and practices of the households to create a more targeted solution for the area. Online questionnaires were distributed to the respondents and were analyzed using the Kruskal-Wallis test. The findings revealed the relevant demographics in the target area and created a conceptual framework that the administrators can adopt. For the knowledge, attitude, and practice of the households, the income of the family showed significance. The other significant demographics were the household size for the attitude section; educational attainment for the knowledge section; and all the demographics for the practice section. Knowing this relevance, the community can create policies that are catered more to the effective group equating to greater chance of success in the implementation. The significance of the study is that the household wastes comprise of more than half of the waste generation of the Philippines hence, targeting this sector of the society will greatly impact the solid waste problem in the country.