Determinants of Successful Admission to the Information Systems Program in State Colleges and Universities (SUCs) of the Philippines
Higher education is a cornerstone of national development, promoting innovation, equipping individuals with future-ready skills, and addressing global challenges such as equitable access to quality education. In the Philippines, State Universities and Colleges (SUCs) aim to democratize higher education, but systemic barriers persist, limiting access for marginalized groups. This study investigates the determinants of successful admission to the Bachelor of Science in Information Systems (BSIS) program at three SUC campuses. Using a quantitative research design, logistic regression analysis was applied to a dataset comprising socio-demographic, academic, and family-related variables from 499 applicants during the academic year 2024–2025. The analysis identified key predictors of admission, such as demographics, high school strands, family dynamics, and access to personal computers. Results indicate that being an Indigenous applicant significantly increases the likelihood of admission, underscoring the effectiveness of affirmative action policies. Additionally, family circumstances positively influence admission outcomes. Conversely, applicants from public high schools and female students exhibit lower odds of admission, highlighting potential inequities in the selection process. The study also reveals that STEM strand graduates are less likely to be admitted than those from TVL strands, suggesting a misalignment between high school curricula and BSIS program requirements. While the logistic regression model achieved an accuracy of 72%, it demonstrated challenges in correctly identifying non-admitted applicants.
Authors
- El Jireh P. Bibangco (ORCID: https://orcid.org/0000-0002-2298-7665)
- Norberto L. Mondero
Institutions
- Carlos Hilado Memorial State University (PH)
Publication Details
- Published
- 2025-04-13
- DOI
- https://doi.org/10.1109/itikd63574.2025.11004812
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69