This study examines the Canvas’ usability in Learning Management System (LMS) from the perspectives of students and teachers, focusing on experiences across Progressive Web App (PWA) and native mobile platforms. A task-based usability testing approach was employed, combining quantitative measures of task completion and time with qualitative insights from observations and participant feedback. Findings indicate that both platforms supported high task completion, though clear differences emerged in efficiency and feature accessibility. Teachers achieved a 91.7% completion rate on the mobile app compared to 100% on the PWA. The mobile app was faster for grading and assignment creation, while the PWA provided broader feature coverage, particularly for analytics, though some users reported navigation difficulties. For students, performance differences were more pronounced: average task completion time on the PWA was 1.24 minutes compared to 5.72 minutes on the mobile app. Tasks such as replying to announcements and checking grades were completed up to ten times faster on the PWA. Overall, the mobile app demonstrated greater stability and efficiency for routine functions, whereas the PWA offered extended functionality and cross-platform access but with tradeoffs in responsiveness and interface clarity. These results highlight the role of platform choice in shaping user experience and suggest directions for optimizing Canvas LMS for both teaching and learning contexts. By advancing usability in digital learning platforms, this research contributes to Sustainable Development Goal (SDG) 4: Quality Education, while also supporting SDG 9: Industry, Innovation, and Infrastructure through insights on mobile technology design, and SDG 10: Reduced Inequalities by emphasizing accessibility across diverse devices and connectivity conditions.

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.

Following recent advancements in automatic speech recognition (ASR) technologies, we replicated an experiment four decades ago that utilized voice as an input modality for computer programming. We also extended this experiment by investigating the pedagogical effectiveness of ‘programming by voice’ in terms of attitude, self-efficacy, code correctness, and coding speed. A total of 96 students from an institute of technology in the capital region of the Philippines were randomly selected to participate in a quasi-experimental study using a one-group pretest-posttest design. We subjected students to programming activities with different levels of difficulty to compare voice and keyboard. Our results show that although voice decreases negativity, it likewise decreases control, which means that both attitude and self-efficacy are positively and negatively affected, respectively. Using voice as an input modality also allows students to code faster when the activities are easy but not when they are moderate or difficult. Code correctness analysis shows that voice is only preferable for easy and moderate machine problems. With the deviation of our findings from an experiment four decades ago, we can now conclude that ASR technologies and voice as input modality provide substantial implications and new opportunities for teaching and learning computer programming.

The combination of multimedia technology and Internet provides an amiss channel for pornographic contents accessible by certain sensitive groups of people. Furthermore, the same channel provides the easiest medium to distribute illicit images and videos without an autonomous content supervision process. In this study, an application was developed grounded from a pixel-based approach and a skin tone detection filter to identify images and videos with a large skin color count and considered as pornographic in nature. With nudity detection algorithm as the foundation of the system, all multimedia files were preprocessed, segmented, and filtered to analyze skin-colored pixels by processing in YCbCr space and then classifying it as skin or non-skin pixels. Afterwards, the percentage of skin pixels relative to the size of the frames is calculated to be part of the mean baseline for nudity and non-nudity materials. Lastly, the application classifies the files as nude or not, and then filter it. The application was evaluated by supplying a dataset of 1,239 multimedia files (Images = 986; Videos = 253) collected from the Web. On the final testing set, the application obtained a precision of 90.33% and accuracy of 80.23% using the supplied dataset.