The surge in the digital arts industry has brought forth critical challenges related to intellectual property (IP) and copyright protection. This paper introduces “Stella Vee,” an innovative isometric action-adventure video game developed to illuminate these pressing issues. Stella Vee immerses players in scenarios that mirror the trials faced by artists grappling with intricate IP dilemmas. This project not only presents Stella Vee but also extracts instrumental theoretical, practical, and technical insights cultivated from its evaluation. First, theoretical insights delve into player engagement and the role of visual aesthetics in gaming. Then, practical recommendations guide game developers in enhancing visual design as well as refining user interface (UI) and control mechanisms. Finally, on a technical level, implications extend to UI development and input method refinement. Within the confines of this game project, we aim to provide insights into the development of captivating, instructive, and user-centric video games poised to confront the paramount challenges surrounding IP and copyright preservation in our burgeoning digital epoch.

Among computer programmers and developers, the user-oriented question-and-answer website of Stack Overflow is a useful platform for sourcing solutions to programming problems, exchanging insights, and accessing a wealth of shared knowledge. However, the timeliness of responses on this platform is frequently a limiting factor that ChatGPT could potentially address. The goal of this study was to explore the preferences of novice programmers between these platforms for finding answers to their programming questions. Anchored in the Technology Acceptance Model (TAM) and the Information Foraging Theory (IFT), the study investigates users' perceptions of usefulness, ease of use, information scent, cognitive effort, as well as overall preferences. Our findings show discernible variations in preferences within the group of students (i.e., application and website developers). In line with these results, we discussed theoretical and practical implications and suggested a dual-pronged approach to leverage both environments as coding assistants in computer programming education.

Serious game is one of the pedagogical media capable of transferring knowledge to its players. This game genre requires a support system that adaptively selects the appropriate scenario for players to increase their interest and comfort. Therefore, this study proposed an adaptive scenario selection (ASS) system using a finite state machine based on an artificial neural network (ANN). The game scenario is selected by ASS based on five player preferences, including work, hobbies/interests, origin, group members, and repetition. Furthermore, the multi-layer perceptron (MLP) architecture was used in the scenario selection process for the proposed ANN method. The experimental stage was carried out using the theme of travel in several tourism destinations in Batu City, East Java, Indonesia. The experimental results show that ASS succeeded in generating adaptive game scenario choices for players based on their preference data with an accuracy of 67.25%.

This study presents a profound exploration into the synthesis of cultural mythology and video gaming, epitomized by the creation of “Anito: Battle of the Gods.” With meticulous attention to detail, the game artfully merges immersive gameplay with the captivating narrative of the Philippines' rich cultural heritage. Grounded in the Scrum methodology, the project unfolded through iterative collaboration, ensuring both efficiency and alignment with player preferences. Extensive quantitative data was amassed through game evaluation sheets and purposive sampling, spotlighting the game's diverse aspects. Results unveiled resounding satisfaction among participants in categories spanning gameplay, aesthetics, user interface, player experience, and website usability. Theoretical implications accentuate the efficacy of cultural storytelling within games, while practical insights highlight the successful amalgamation of entertainment and education. Technically, the study underscores the potency of agile methodologies in crafting an engaging gaming experience. These findings hold far-reaching significance, demonstrating the power of culturally infused games to captivate and educate players. In this tapestry of research, the study signifies the convergence of creativity, culture, and technology, elevating gaming beyond leisure to an instrument of cultural exploration and enrichment.

Providing support is one thing, generating an automatic ticket category based purely on the textual data provided is another. This study is working towards encouraging the educational landscape to start integrating AI in further enhancing the way students learn and the way teachers are giving their lessons. The focus of this study is to use the subset of AI that concentrates on making machines understand how humans talk which is known as NLP. By using several Python libraries, 3 text classification algorithms – namely SVM, Naïve Bayes, and logistic regression were used to train the previously collected dataset and choose the model that will be integrated to the web-based helpdesk system called HelpTech. With the help of the model, the system instantly categorizes the issue submitted by the end users resulting to an easier way to use the educational tools available which assist the stakeholders in developing their digital literacy.

As the educational technology director of our institution, I often find myself at the forefront of discussions surrounding the integration of artificial intelligence (AI) and its impact on our lives. Recently, a former student approached me with a thought-provoking question: "In times of grief and loss, can ChatGPT offer the comfort and consolation we seek?" The weight of this inquiry bore down on me, for I realized that answering it was not a task I could take lightly. I hesitated, acutely aware that I was not a health professional equipped with the expertise to navigate the depths of grief and loss. Moreover, my role as an educational technology director means that I have had the opportunity to witness the transformative potential of AI, leading me to wonder whether I possess a natural inclination to embrace technology as a solution. Therefore, I felt compelled to engage health professionals, the true authorities on matters of emotional well-being and mental health, to join me in an open and honest exploration of this complex question.

Rainfall is crucial for flood prevention and comprehending the correlation between rainfall and flooding. Cavite province in the Philippines is vulnerable to flooding caused by heavy rainfall and climate change impacts. Early detection of flooding through early warning systems can prevent excessive damage loss and potentially save lives. It can also provide major savings in terms of monetary benefit and increased interagency coordination for rapid decision-making. Machine learning is an important tool for predicting rainfall which can be used to predict rainfall in the province. The objective of this study is to conduct a comparative analysis of various models for predicting daily rainfall, using relevant atmospheric features such as maximum, minimum, and mean temperature, relative humidity, wind speed, wind direction, cloud cover, pressure, and evaporation. The study seeks to identify the most effective model for accurately predicting rainfall in the Cavite Province to benefit the local community. Among the five machine learning models evaluated, the Gaussian Process Regression model demonstrated the highest accuracy in predicting daily rainfall. The findings of this study can be leveraged to mitigate the damage caused by flooding in the Cavite Province and serve as a useful reference for similar studies in other regions prone to flooding.

Diabetic retinopathy is a serious complication needing prompt diagnosis and medication to avert vision loss. Lesions caused by the condition are difficult to track because they are hidden behind the eye's structure in small and subtle forms. To extract relevant features., we created a robust pipeline using multiple preprocessing techniques., image segmentation architecture (DR-UNet) with atrous spatial pyramid pooling., and an attention-aware deep learning convolutional network with different modules based on ResidualNet. Empirical results show that our framework has segmentation accuracies of 87.10% (intersection over union) and 84.50% (dice similarity coefficient). Moreover., classification performance of 99.20% provided better results than existing schemes., as reinforced by the smooth convergence of training/validation loss and accuracy. This study has the potential to supplement traditional diagnosis to identify better the ailment in its early and advanced stages.

Nanolearning (NL) is a promising approach to education and training as it delivers small, bite-sized chunks of learning content that can be easily consumed and retained by learners. This allows quickly accessing specific pieces of information and knowledge, which can be delivered through a variety of mediums, such as videos, podcasts, or mobile applications, etc. NL has significant potential in educational and training settings, where learners or trainers can quickly upskill or reskill in specific contexts, improving their productivity and mastering some topics. This study provides an overview of NL, addressing the design of NL educational materials and its implementation in several educational applications. It also highlights some considerations and issues. In conclusion, it is recommended that reliable learning resources be used by teachers, the content be closely assessed, the source format be considered, bias be checked for, and learner feedback be obtained to ensure the quality of NL materials. By following the proposed NL framework, teachers can provide their learners with top-notch and productive NL resources.

When it comes to asynchronous online learning, the literature recommends multimedia content like videos of lectures and demonstrations. However, the lack of emotional connection and the absence of teacher support in these video materials can be detrimental to student success. We proposed incorporating talking heads and annotations to alleviate these weaknesses. In this study, we investigated the cognitive and affective effects of integrating these solutions in asynchronous video lectures. Guided by the theoretical lens of Cognitive Theory of Multimedia Learning and Cognitive-Affective Theory of Learning with Media, we produced a total of 72 videos (average = four videos per subtopic) with a mean duration of 258 seconds (range = 193 to 318 seconds). To comparatively assess our video treatments (i.e., regular videos, videos with face, videos with annotation, or videos with face and annotation), we conducted an educational-based cluster randomized controlled trial within a 14-week academic period with four cohorts of students enrolled in an introductory web design and development course. We recorded a total of 42,425 total page views (212.13 page views per student) for all web browsing activities within the online learning platform. Moreover, 39.92% (16,935 views) of these page views were attributed to the video pages accumulating a total of 47,665 minutes of watch time. Our findings suggest that combining talking heads and annotations in asynchronous video lectures yielded the highest learning performance, longest watch time, and highest satisfaction, engagement, and attitude scores. These discoveries have significant implications for designing video lectures for online education to support students’ activities and engagement. Therefore, we concluded that academic institutions, curriculum developers, instructional designers, and educators should consider these findings before relocating face-to-face courses to online learning systems to maximize the benefits of video-based learning.