Geliza Marie I. Alcober
10 Publications
Scopus ID: 105030541201
2025 23rd International Conference on ICT and Knowledge Engineering (ICT&KE), (2025), pp. 1-6
Conference Paper | Published: December 9, 2025
Abstract
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.
Scopus ID: 105030157237
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: March 12, 2025
Abstract
This study introduces a centralized Business Management System (BMS) tailored for small and mediumsized enterprises (SMEs), with an innovative approach due to the integration of a foot traffic detection system through video processing. The system allows businesses to access common business management features such as point of sales, staff scheduling, inventory management, and reports. With the integration of YOLOv7, foot traffic detection for customer count is made possible through LACAD. By automating data collection and providing foot traffic counts, alongside graphical reports, the system empowers SMEs to make better decisions for their businesses. This research highlights the strategic advantage of leveraging foot traffic insights to drive performance and competitiveness in the modern business landscape. As a guide for the study the researchers used Scrum methodology. The study was then evaluated through a quantitative survey using FURPS with 12 IT professionals and 7 beneficiaries as the respondents where the calculated total weighted mean for both the respondent types resulted in 4.60 which means that the users “Strongly Agree” with the system's overall components.
Scopus ID: 105000432387
TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON), (2024), pp. 1219-1222
Conference Paper | Published: January 1, 2024
Abstract
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.
Scopus ID: 85199880629
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Conference Paper | Published: January 1, 2023
Abstract
A case study of DITO telecoms is used to examine the effectiveness of door-to-door sampling caravans for telecoms firms to develop their subscriber base. The telecoms sector is changing; thus client acquisition strategies are being reconsidered. In this study, in- person engagement and personalized encounters are evaluated in the digital age. The mixed-methods research analyses quantitative and qualitative data. Customer comments and sales team observations during the door-to-door sample caravan provide qualitative data, while subscriber growth metrics, conversion rates, and demographic profiling provide quantitative data. Research shows that door-to-door sampling caravans may boost subscription growth. Personalized interactions build trust and rapport, which may overcome consumer skepticism about digital marketing. The technique also permits direct targeting of demographics and places to meet local requirements. However, scalability, resource allocation, and long-term effect must be considered. The research emphasizes strategic planning, training, and communication for caravan sales teams. The study found that although digital initiatives are important, offline approaches like door- to-door sampling caravans may provide a competitive edge. Telecommunications firms looking to vary their client acquisition techniques might learn from the research that in-person contact can maintain subscriber growth.
Scopus ID: 85199929124
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-5
Conference Paper | Published: January 1, 2023
Abstract
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.
Scopus ID: 85145507832
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
During the outbreak of COVID-19, the productivity gap between working in the office and at home has become a more critical issue in the labor market. For teachers with numerous vital responsibilities, the inescapable increased workload results in less productivity and efficiency. Following the reliance on productivity applications to lessen labor, we investigated the moderating effects of sociodemographic profiles of teachers through the Technology Acceptance Model. The demographic makeup of our participants (n = 513) was dominated by assistant professors, females, married, licensed teachers, aged 25 to 34 with a teaching experience of 6 to 10 years, and permanent and full-time in a public university. Our findings demonstrate that sociodemographic variables moderate the effects of perceived usefulness (PU) and perceived ease of use (PEOU) on teachers’ behavioral intention to use (BITU), except for the effects of gender in PEOU → BITU, teaching experience in PEOU → BITU, and educational attainment in PU → BITU.
Scopus ID: 85159442221
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
This project aims to aid students, teachers, and even non-teaching staff in terms of managing technological inquiries and maintaining communication to perform academic-related transactions by developing Tissa – a web-based helpdesk support system for tertiary institutions that integrates various time series analysis techniques in forecasting the estimated number of tickets to be filed by the stakeholders based on historical records. Specifically, it explores the effects of developing a dedicated ticket management system that incorporates both analytics and enhanced user experience to the users’ satisfaction, especially in a tertiary school setting. A total of 62 respondents were selected through purposive sampling. These respondents helped in evaluating the system's overall quality based on the ISO/IEC 25010 software quality model. The respondents' evaluation of the system's overall quality received an above-average score which means that the system passed the testing in terms of system requirements. The results gathered also suggest that 95.2% of the respondents agree that the inclusion of data visualization serves as valuable insights in performing strategic business decisions. Additionally, the system's user experience evaluation received the highest mean score of 4.82. The results for exponential smoothing yielded an 88% accuracy, linear regression was 80% accurate, simple moving techniques had an 85% accuracy rate, and 84% for the weighted moving average, which suggest that techniques used in this study can be considered reliable for future references.
Scopus ID: 85127556927
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Conference Paper | Published: January 1, 2021
Abstract
It is clear that since the COVID-19 pandemic started in the Philippines, education is one of the most affected areas. After more than a year of struggling with different community lockdowns and the alarming consistency with the increasing number of confirmed cases each day, students and teachers are now left with the choice to voice out their frustrations, activism, opinions, and ideas regarding online classes through different social networking sites, most especially Twitter. With the influx of tweets available in the internet sphere, the authors of this study decided to conduct a sentiment analysis to categorize the overall opinions of Filipino citizens about the current state of education after more than a year of adapting with the distance learning practices that are now considered as the new normal. The authors utilized rtweet, a built-in package available in R programming to perform opinion mining on Twitter data collected through the package related to online class during pandemic. Through sentiment lexicons available in R such as bing and afinn, the results show that most of the tweets about online learning in the Philippines turned out to be neutral. The positive responses are 55.77% while 44.23% of the sentiments collected are negative. To evaluate the accuracy rates of results, the authors used three classification techniques namely Naïve Bayes, logistic regression, and random forest. Naïve bayes and logistic regression both show 69.23% accuracy rate and random forest calculated 71.15% accuracy in identifying whether the given tweet is a positive, negative, or neutral sentiment.
Scopus ID: 85105023525
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-6
Conference Paper | Published: December 3, 2020
Abstract
Myocardial Infarction (MI) better known as heart attack, is considered as one of the most alarming diseases that used to harm a great percentage of the male populace. However, the number of female patients suffering from the condition that was formerly known as the “old-man disease” is gradually increasing at the present time. Taking that into consideration, the researchers gathered enough data to come up with a predictive model that could be utilized in identifying the risk indicators for the mortality of female patients suffering from MI. By using different tools in data mining that contribute to a lot of great data discoveries up to date, the researchers made use of logistic regression, random forest, and decision tree to evaluate which technique can generate a diagnostic model with a higher accuracy rate. The generated prognostic models were based on a total of 9 significant attributes that were used to determine the risk indicators for the mortality of female patients suffering from MI. Upon conclusion, it turns out that among the three data mining techniques used in this study, logistic regression has the highest accuracy rate of 79% while random forest and decision tree resulted in 77% and 73% respectively. Medical practitioners could also use this study in discovering the characteristics that made up the clusters or groups of Myocardial Infarction patients that survived and characteristics that made up the clusters or groups of Myocardial Infarction patients that didn't. Determining the risk indicators of a female patient surviving MI tailors a more personalized way of treating the disease.
Scopus ID: 85089752037
Proceedings of the 2020 3rd International Conference on Robot Systems and Applications, (2020), pp. 82-87
Conference Paper | Published: June 14, 2020
Abstract
The study aims to integrate neural network algorithm that predicts students' vulnerability of not having graduation on time to an adaptive learning management system. Neural network is one of the popular machine learning techniques because of its learning algorithm. The learning algorithm is focused on updating weights of the edges in order to produce minimal mean squared error between actual and predicted values. The integration of this platform could lead to much efficient learning management system as LMS is mainly driven to provide individualized and personalized learning tailored to specific requirements and learning preferences. The neural network algorithm is designed to classify students with learning difficulty so that administrators can formulate remediation and academic support policies.