Computational analysis of engineering licensure outcomes under curricular change using propensity score matching

Arturo J. Patungan; Vitus Paul De Jesus; Regina Zyra F. Fajardo; Micaela Allena T. Valondo

doi:10.32890/jcia2025.4.2.5

Authors

Arturo J. Patungan Jr. University of Santo Tomas, Philippines
Vitus Paul L De Jesus University of Santo Tomas, Philippines
Regina Zyra F. Fajardo University of Santo Tomas, Philippines
Micaela Allena T. Valondo University of Santo Tomas, Philippines

DOI:

https://doi.org/10.32890/jcia2025.4.2.5

Keywords:

curriculum evaluation, engineering licensure, licensure examination, propensity score matching, Philippines

Abstract

The Philippine higher education sector underwent a significant curriculum transition with the implementation of the K–12 program, which reduced the engineering curriculum from five to four years. This study examines the impact of this change on the licensure examination performance of engineering graduates. A causal-comparative design was used, with propensity score matching (PSM) applied to control for confounding variables such as age, sex, academic performance, exam timing, and degree program. Both nearest-neighbor and full matching methods were tested, with full matching selected for its superior covariate balance and sample retention. Using the matched dataset, logistic regression modeling revealed that graduates under the new 4-year curriculum had significantly lower odds (OR = 0.94) of passing the board exam than those under the 5-year track. The marginal mean comparison showed a 6.06 percentage point decrease in passing probability for the new curriculum group (p < .001), with a Cohen’s d of –0.53, indicating a moderate negative effect. These findings suggest that transitioning to the restructured 4-year engineering curriculum has not yet translated into improved licensure performance. A closer review of curriculum content, implementation fidelity, and academic support programs is needed to address this gap and strengthen outcomes under the reformed education system.

References

Austin, P. (2011). An introduction to propensity score methods for reducing confounding effects in observational studies. National Center for Biotechnology Information. https://doi.org/10.1080/00273171.2011.568786

Cabaces, D. (2018). Engineering education in the Philippines. International Journal of Advanced Research and Publications, 2(12), 27–31.

Camello, N. C., Castro, E. L., Chavez, N. H., Dotong, C. I., Laguador, J. M., & Prenda, M. B. (2016). Tracer study of engineering graduates of one higher education institution in the Philippines for the academic year 2009–2012. European Journal of Engineering and Technology, 4(4), 37.

Durban, J. M., & Catalan, R. D. (2012). Issues and concerns of Philippine education through the years. Asian Journal of Social Sciences and Humanities, 1, 61–69.

Frost, J. (2022). Content validity: Definition, examples & measuring. Statistics By Jim. https://statisticsbyjim.com/basics/content-validity/

Griffen, A. S., & Todd, P. E. (2017). Assessing the performance of nonexperimental estimators for evaluating Head Start. Journal of Labor Economics, 35(S1), S56–S97. https://doi.org/10.1086/691602

Inquirer. (2018). What went before: The K-12 program. Inquirer.net. https://newsinfo.inquirer.net/980733/what-went-before-the-k-12-program

Joint Foreign Chambers. (2010). Arangkada Philippines 2010: A business perspective. https://www.investphilippines.info/arangkada/wp-content/uploads/2011/06/30.-Part-4-General-Business-Environment-Social-Services-Education.pdf

Luvsandorj, Z. (2023). A beginner’s guide to propensity score matching. Built In. https://builtin.com/data-science/propensity-score-matching

Maaliw, R. R. (2021). Early prediction of electronics engineering licensure examination performance using random forest. In 2021 IEEE World AI IoT Congress (AIIoT) (pp. 41–47). IEEE. https://doi.org/10.1109/AIIoT52608.2021.9454178

Maheshwari, V. K. (2018). Causal-comparative research. http://www.vkmaheshwari.com/WP/?p=2491

Mallari, G. C., & Bueno, D. C. (2018). Factors affecting the Civil Engineering Licensure Examination performance: A theoretical perspective. Institutional Multidisciplinary Research and Development Journal, 1, 1–10.

McCombes, S. (2021). What is a research design? Scribbr. https://www.scribbr.com/methodology/research-design/

Nathan, S. K., & Rajamanoharane, S. W. (2016). Enhancement of skills through e-learning: Prospects and problems. The Online Journal of Distance Education and e-Learning, 4(3), 10–20.

Navarro, D. (2022). ‘Experimental’ K-12 grads prove worth. Daily Tribune. https://tribune.net.ph/2022/10/23/experimental-k-12-grads-prove-worth/

Official Gazette. (n.d.). The K to 12 basic education program. Official Gazette of the Republic of the Philippines. https://www.officialgazette.gov.ph/k-12/

Orbeta, A., & Potestad, M. (2020). On the employability of the senior high school graduates: Evidence from the Labor Force Survey (Discussion Paper No. 2020-40). Philippine Institute for Development Studies.

Parrocha, A. (2023). DepEd to give updates on K-12 curriculum on Jan. 30. Philippine News Agency. https://www.pna.gov.ph/articles/1192378

Permatasari, D. A., & Mardapi, D. (2019). Competency profile of electrical engineering graduates relevant to the industry. In Proceedings of the 2019 International Conference of Science and Technology for the Internet of Things. European Alliance for Innovation.

Private Education Assistance Committee. (n.d.). RA 10533. Official text of Republic Act 10533. https://ovap.peac.org.ph/about/ra10533

Professional Regulation Commission. (2022). History. Professional Regulation Commission (PRC). https://www.prc.gov.ph/history

Professional Regulation Commission. (2024). Republic of the Philippines Professional Regulation Commission. https://www.prc.gov.ph/

Qadir, J., Kok-Lim, A. Y., Imran, M. A., & Al-Fuqaha, A. (2020). Engineering education, moving into 2020s: Essential competencies for effective 21st century electrical & computer engineers. In 2020 IEEE Frontiers in Education Conference (FIE). IEEE. https://doi.org/10.1109/FIE44824.2020.9274092

Reyes, F. C. (2000). Engineering the curriculum: A guidebook for educators and school managers. De La Salle University Press.

Rivera, E. R. (2021). Exploratory data analysis (EDA) of the performance of Philippine higher education institutions in the Electronics Engineering Licensure Examinations. IECEP Journal, 4(1), 1–8.

Setiawan, H., & Raharjo, F. (2019). Knowledge, skills and attitudes of civil engineers in Indonesia. IOP Conference Series: Materials Science and Engineering, 615(1), 012030. https://doi.org/10.1088/1757-899X/615/1/012030

Southeast Asian Ministers of Education Organization, Regional Center for Educational Innovation and Technology (SEAMEO INNOTECH). (2012). K to 12 toolkit: Resource guide for teacher educators, school administrators and teachers. https://www.seameo-innotech.org/wp-content/uploads/2020/05/k-12-toolkit.pdf

Sundjaja, J. H., Shrestha, R., & Krishan, K. (2022). McNemar and Mann-Whitney U tests. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/books/NBK560699/

Terano, H. J. R. (2018). Regression model of the licensure examination performance of electronics engineering graduates in a state college in the Philippines. Advances and Applications in Mathematical Sciences, 18(2), 197–204.

World Economic Forum. (2011). The Global Competitiveness Report 2011–2012: Digital TV – Higher Education and Training. https://www3.weforum.org/docs/GCR2011-12/17.GCR2011-2012DTVHigherEducationandTraining.pdf

Wulandari, M., Nusantara, D. A. D., Rahmadyanti, E., & Mulyono, W. D. (2022). Tracer study of alumni competencies in Civil Engineering Department Universitas Negeri Surabaya. In Proceedings of the International Joint Conference on Science and Engineering 2022. Atlantis Press. https://doi.org/10.2991/978-94-6463-092-3_2