Student Satisfaction and Productivity Analysis of the BS Computer Science Department of North Negros College

The increasing emphasis on student-centered quality assurance in higher education necessitates systematic evaluation of academic programs, particularly in technology-oriented disciplines. This study assesses student satisfaction within the Bachelor of Science in Computer Science (BSCS) Department of North Negros College, Inc., focusing on curriculum, facilities, and faculty performance as key determinants of academic productivity and service quality. Grounded in established Information Systems and organizational performance frameworks—including the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Task–Technology Fit (TTF), and the DeLone and McLean Information Systems Success Model—the study examines how instructional delivery, learning infrastructure, and program design influence overall student experience.
A descriptive-evaluative design was employed using survey data from 105 students out of a total population of 331 (31.72% response rate). The instrument demonstrated excellent reliability (Cronbach’s α = .966). Statistical analyses included descriptive statistics, subgroup comparisons, and multiple regression modeling. Results indicate consistently high satisfaction across domains, with faculty performance emerging as the strongest predictor of overall satisfaction (β = .680, p < .001), followed by facilities (β = .202, p < .05), while curriculum showed no significant predictive effect when controlling for other variables.
Findings suggest that while the department has achieved functional effectiveness in delivering academic services, its performance is shaped by contextual factors such as instructional quality and infrastructure adequacy. The study offers evidence-based recommendations for targeted improvements in faculty development, facility enhancement, and curriculum alignment to support sustained institutional growth and competitiveness.

student satisfaction, higher education, productivity analysis, information systems, academic quality

1. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. https://doi.org/10.2307/3150783 [Google Scholar] [Crossref]

2. Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. MIS Quarterly, 1(3), 17–32. https://doi.org/10.2307/248710 [Google Scholar] [Crossref]

3. Carifio, J., & Perla, R. (2008). Resolving the 50-year debate around using and misusing Likert scales. Medical Education, 42(12), 1150–1152. https://doi.org/10.1111/j.1365-2923.2008.03172.x [Google Scholar] [Crossref]

4. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. [Google Scholar] [Crossref]

5. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555 [Google Scholar] [Crossref]

6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

7. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. [Google Scholar] [Crossref]

8. https://doi.org/10.1080/07421222.2003.11045748 [Google Scholar] [Crossref]

9. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications. [Google Scholar] [Crossref]

10. Goodhue, D. L., & Thompson, R. L. (1995). Task–technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689 [Google Scholar] [Crossref]

11. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. [Google Scholar] [Crossref]

12. Heeks, R. (2009). The ICT4D 2.0 manifesto: Where next for ICTs and international development? Development Informatics Working Paper No. 42. University of Manchester. [Google Scholar] [Crossref]

13. Hilty, L. M., & Aebischer, B. (2015). ICT for sustainability: An emerging research field. Environmental Modelling & Software, 56, 158–165. https://doi.org/10.1016/j.envsoft.2014.07.002 [Google Scholar] [Crossref]

14. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575 [Google Scholar] [Crossref]

15. Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y [Google Scholar] [Crossref]

16. Organisation for Economic Co-operation and Development. (2019). Measuring the digital transformation: A roadmap for the future. OECD Publishing. https://doi.org/10.1787/9789264311992-en [Google Scholar] [Crossref]

17. Selwyn, N. (2016). Education and technology: Key issues and debates (2nd ed.). Bloomsbury Academic. [Google Scholar] [Crossref]

18. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd [Google Scholar] [Crossref]

19. Toyama, K. (2011). Technology as amplifier in international development. In Proceedings of the iConference (pp. 75–82). https://doi.org/10.1145/1940761.1940772 [Google Scholar] [Crossref]

20. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 [Google Scholar] [Crossref]

21. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory. MIS Quarterly, 36(1), 157–178. [Google Scholar] [Crossref]

22. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study