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.

This study centers on the essential role played by Speech-Language Pathologists (SLPs) in the diagnosis and treatment of speech and language disorders within the Philippines. It underscores the significant difficulties resulting from the limited availability and effectiveness of Augmentative and Alternative Communication (AAC) tools, particularly in the context of the Filipino language. These limitations impede the progress of Filipino children struggling with speech delay disorders. The study aims to develop AAC software integrated with an intelligent tutoring system in Filipino. This innovative approach incorporates Filipino AAC tools such as AAC boards, assessments, client management, and identification of rapidguessing behavior on AAC assessments on different difficulty levels using a decision tree algorithm, providing a structured and personalized therapy approach. The software was evaluated using FURPS with a total of 50 participants, whom are the 30 or 60 % speech-language pathologists, 8 or 16 % Information Technology and Computer Science (IT/CS) professionals, 2 or 4 % CS Professors, and 10 or 20 % Parents/Guardians. The computed Cronbach's alpha (α
) was 0.95 which indicates the FURPS instrument has excellent internal consistency. The grand mean of the software evaluation was rated at 4.63 which highlights the generally positive evaluation of the system. Precision, recall and F1-score assess the model's performance in binary classification. For the class labeled “0,” the model achieved a precision of 0.99, a recall of 1, and an F1 score of 0.99. This indicates that the model has high accuracy in predicting instances belonging to class “0.” For the class labeled “1,” the model achieved a precision of 0.95, a recall of 0.92, and an F1-score of 0.93, indicating slightly lower performance than class “0.” The findings of this study and the developed software have significant implications in the field of AAC. Additionally, this study's contribution serves as a foundation for future advancements in AAC-related technologies, driving innovation and improvement in the field.

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.

This descriptive-developmental study enables the authors to create a graduate tracer monitoring platform. The paper aims to provide a centralized channel to monitor institutions' graduates in terms of their job employment, assessing academic programs using modified instruments which determine necessary interventions that may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study used a Decision Support System and mapping recommendation analysis using time series analysis to evaluate the results of alumni program evaluation on five areas or dimensions such as curriculum, faculty, facility, laboratory, and student services. The study may set the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implement the interventions for each area. A content management system was also used in this paper to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. The developed web-based system includes an evaluation of the Alumni Program across key areas such as Curriculum, Faculty, Facility, Laboratory, and Student Services. This study employed a purposive sampling technique to identify the group of respondents. There are a total of 152 respondents who participated in this study from the Information Technology department and IALAP office. The study results indicate that no interventions are necessary in any of these areas, as both the mean and the composite mean surpasses the 3.50 threshold set in the system. Among the five areas, the faculty received the lowest passing mean, followed by student services and the laboratory. This underscores the potential for continuous improvement in these specific areas influencing the employability rate and skills of the alumni-participants. The time series analysis was conducted on a two-year dataset, covering 6 trimesters. The analysis revealed a positive improvement in evaluation scores as the trimesters progressed across five dimensions of alumni program evaluation. This suggests that alumni respondents consistently agreed in their evaluations of appreciation on the improvements made by the school administration which enhances their life experiences and technical skills during their stay in the campus.

Coding regardless programming languages is an expected skill evaluated in computing courses [1]. The majority of programming research has been on student challenges and mistakes that compromise technical accuracy [2]. Prior study has shown that debugging approaches are utilized to create response in learning coding [3]. The goal of this paper is to build a web-based intelligent tutoring system (ITS) application for freshmen learning C language that predicts tinkering activity using constraint-based modeling (CBM) and machine learning algorithm. The system was created for capturing C code samples focused on assignment statements, which would then be sent to the tutor model for submitted code assessment and response generation using CBM. This paper expanded the first system prototype of Arguson et al. [4] which was pilot tested on 2 synchronous coding classes among 31 freshman tertiary students composed of 9 BS Computer Science and 22 BS Information Technology respondents. The dataset was taken from the aforementioned first prototype, whilst the student model was built using the data science approach for building the student model. Arguson et al. [5]’s study was used to construct a modified CBM that centers on assignment statements. The model statistically predicted tinkering on both numerical value problems and incorrect expressions performed by beginner programmers, according to the results. In the development of the final ITS prototype, a novel detection of tinkering activity in the context of C coding focused on assignment statements was deployed. Adhering to Scrum framework, the authors were able to supervise the software project in this study. It emerged that tinkering activity can be predicted using compiled inexperienced C programming logs as well as tailored-fit feedback is required to comprehend the aforementioned programming language.

This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm (DSA) course which is based on student lifestyle to analyze the factors that affect the high or low performance result. A total number of 251 Bachelor of Science in Computer Science (BSCS) students participated in the study where 207 or 82% were male and 44 or 18% were female. A oneshot case study was conducted that led to data collection through the administration of an online survey on former enrollees of the said course. The dataset was extracted with 43 features and was analyzed using Python on Jupyter Notebook. Randomly selected 70% of these, 176 observations, are used to train the classifier models. The remaining 30%, 75 observations, were used as the test data. In order to classify academic performance students, eight machine learning algorithms were applied based on random forest (RF), decision tree (DT), support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), Gaussian Naive Bayes (GNB), stochastic gradient descent (SGD), and perceptron. Although SGD and Perceptron classifier models show comparably low classification performances, both random forest and decision tree classifiers provided the highest metric performance. The study indicated that the lifestyles of students contributed to whether the student performance became high or low in their grade performance.

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.

Computer programming is one of the fundamental skills in the field of computing [1]. In a computing class, students are expected to learn the skills rather than remembering materials only. This study aims to develop a constraint-based student model (CBM) by analyzing the computing students' C compilation error logs. The proposed modified CBM will be used as input to develop a user behavior of an ongoing study for an intelligent tutoring system. The prototype was developed to obtain compilation error logs from the selected students, it contains five (5) C programming questions that focus on assignment statements. The prototype of the study was pilot tested on two (2) online programming classes with a total of thirty-one (31) freshman college students composed of nine (9) BSCS and twenty-two (22) BSIT participants with a mean age of 18.68, where nineteen (19) or 61.3% are males and twelve (12) or 38.7% are females. The study uses convenience sampling to determine the total number of student participants. The dataset was extracted from the prototype and feature identification was performed on one thousand thirteen (1013) C programming logs which resulted to obtain eight (8) error types. The paper of Khodeir, Wanas, & Elazhary (2018) [2] and Karaci (2018) [3] on constraint-based modeling was reviewed to develop a proposed constraint-based model in the context of C programming focusing on assignment statements. By mapping a student error on the suggested constraint relevance (Cr) and constraint satisfaction, the database for constraints was finished (Cs).