Predicting Success in Professional Licensure Examinations Through Discriminant Function Analysis

The study developed a data-driven predictive model for forecasting graduates’ success in professional licensure examinations using Discriminant Function Analysis (DFA). It employed a quantitative, predictive-correlational design to analyze institutional records of 933 graduates from five licensure programs in higher education institutions in the Philippines between 2020 and 2025. Cognitive-academic factors, including fluid intelligence, communication skills, reading comprehension, mathematical ability, professional knowledge, internship performance, and mock board exam performance, as well as contextual factors such as study duration and academic difficulty, were examined using descriptive statistics, independent samples t-tests, and DFA. Results revealed that licensure passers significantly surpassed non-passers across all variables (p < .001). The discriminant model was statistically significant (Wilks’ Λ = 0.430, χ² (9) = 781.573, p < .001; canonical r = 0.755) and accurately classified 88% of the cross-validated cases. The strongest predictors of licensure success were professional knowledge, mock board exam performance, study duration, and academic difficulty. The research concludes that cognitive mastery and academic persistence are crucial for licensure readiness. The study recommends integrating data-driven and AI-enabled early warning and academic analytics systems to identify at-risk examinees and support timely interventions aimed at optimizing readiness for professional qualifications.

Predictive modeling; Discriminant function

1. Acuning, A. F., Barrogo, J. P. B., & Penuela, I. B. (2023). Reading comprehension and academic performance of intermediate pupils [Undergraduate thesis, Capiz State University Pontevedra Campus]. CAPSU Institutional Repository. [Google Scholar] [Crossref]

2. Aiken, J., De Bin, R., Hjorth-Jensen, M. & Caballero, M. (2020). Predicting time to graduation at a large enrollment American university. PLOS One. https://doi.org/10.1371/journal.pone.0242334 [Google Scholar] [Crossref]

3. Alipio, M. M. (2020). National radiologic technology licensure examination performance: Predicting success using discriminant analysis. ResearchGate. https://www.researchgate.net/publication/340625251 [Google Scholar] [Crossref]

4. Anghel, G. A. (2023). Academic success - explanatory theories. Journal of Education, Society & Multiculturalism, 4, 135–143. https://doi.org/10.2478/jesm-2023-0023 [Google Scholar] [Crossref]

5. Asilestari, P., Henjilito, R., & Hardi, V. A. (2025). Enhancing reading comprehension through visual information: A longitudinal study in higher education. AL-ISHLAH Journal of Education. https://doi.org/10.35445/alishlah.v17i2.74900 [Google Scholar] [Crossref]

6. Atadjanov, J. M. (2018). Developing Professional pedagogical mastery of future initial classes teachers. Eastern European Scientific Journal. http://journale.auris-verlag.de/index.php/EESJ/article/view/883/0 [Google Scholar] [Crossref]

7. Bagabir, A., Zaino, M., Abutaleb, A. & Fagehi, A. (2021). Predictive analysis of higher education graduation and retention in Saudi Arabia using multinomial logistic regression. International Journal of Basic Sciences and Applied Computing, 3(6). https://www.ijbsac.org/wpcontent/uploads/papers/v3i6/F0466113621.pdf [Google Scholar] [Crossref]

8. Bansiong, A. J. & Balagtey, J. L. M. (2020). Predicting success in teacher education: Revisiting the influence of high school GPA, admission, and standardized test scores on academic and licensure performance. Journal of Research, Policy & Practice of Teachers & Teacher Education, 10(2). https://doi.org/10.37134/jrpptte.vol10.2.1.2020 [Google Scholar] [Crossref]

9. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. https://psycnet.apa.org/record/1985-98423-000 [Google Scholar] [Crossref]

10. Banua, A. (2017). Determinants of performance of nursing graduates in licensure examination. Bicol University Research & Development Journal, 20, 113-118. https://www.researchgate.net/publication/353751455_DETERMINANTS_OF_PERFORMANCE_OF_ NURSING_GRADUATES_IN_LICENSURE_EXAMINATION [Google Scholar] [Crossref]

11. Bellen, J., Abela, R., & Truya, R. (2018). Academic achievement as predictor in the performance of licensure examination for teachers. Asia Pacific Journal of Education, Arts and Sciences, 5(1), 77–81. https://www.researchgate.net/publication/330243240_Academic_Achievement_as_Predictor_in_the_Pe rformance_of_Licensure_Examination_for_Teachers [Google Scholar] [Crossref]

12. Braak, D. T., Lenes, R., Purpura, D. J., Schmitt, S. A., & Storksen, I. (2021). Why do early mathematics skills predict later mathematics and reading achievement? The role of executive function. Elsevier BV, 214. https://www.sciencedirect.com/science/article/pii/S0022096521002241 [Google Scholar] [Crossref]

13. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press. https://www.jstor.org/stable/j.ctv26071r6 [Google Scholar] [Crossref]

14. Cadosales, M. N., Sánchez, J. M., Córdova, M., Merin, J., & Augusto, W. (2023). Exploring the predictive influence of licensure examination results for beginning teachers’ performance: The case of the Philippines. Frontiers in Education, 8, 1252368. https://doi.org/10.3389/feduc.2023.1252368 [Google Scholar] [Crossref]

15. Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. https://doi.org/10.1037/h0046743 [Google Scholar] [Crossref]

16. Cazarez, R. L. U. (2022). Accuracy comparison between statistical and computational classifiers applied for predicting student performance in online higher education. Education and Information Technologies: Official Journal of the IFIP Technical Committee on Education. https://doi.org/10.1007/s10639-02211106-4 [Google Scholar] [Crossref]

17. Clinton-Lisell, V., Taylor, T., Seipel, B., Carlson, S., & Davison, M. (2022). Performance on reading comprehension assessments and college achievement: A meta-analysis. Journal of College Reading and Learning, 52(1), 3–20. https://doi.org/10.1080/10790195.2022.2062626 [Google Scholar] [Crossref]

18. Cresswell, C., & Speelman, C. P. (2020). Does mathematics training lead to better logical thinking and reasoning? A cross-sectional assessment from students to professors. PLoS ONE 15(7): e0236153. https://doi.org/10.1371/journal.pone.0236153 [Google Scholar] [Crossref]

19. Dator, W. L. (2016). Accreditation and its relationship to the performance in the nurse licensure examination of higher education institutions in the Philippines. International Journal of Evidence-based Healthcare, 14. https://doi.org/10.1097/01.xeb.0000511624.93121.49 [Google Scholar] [Crossref]

20. Dietrichson, J., Bøg, M., Filges, T. & Jørgensen, A. K. (2016). Protocol for a systematic review: Targeted school-based interventions for improving reading and mathematics for students with or at-risk of academic difficulties in grades 7 to 12: A systematic review,” Campbell Systematic Reviews, 12(1), 1–57. https://doi.org/10.1002/cl2.170 [Google Scholar] [Crossref]

21. Dionio, B. B., Dagpin, A. R. R., Cabillas, A. J. Q. B., Edullantes, J. R. T. & Baluyos, G. R. (2025). Predictors of graduates' performance in the licensure examination for teachers (LET). EduLine Journal of Education and Learning Innovation, 5(1), 79–103. https://doi.org/10.35877/454RI.eduline3535 22. Divjak, B., & Oreški, D. (2009, July). [Google Scholar] [Crossref]

22. Prediction of academic performance using discriminant analysis [Conference paper]. 31st International Conference on Information Technology Interfaces. https://www.researchgate.net/publication/224573955_Prediction_of_Academic_Performance_Using_D iscriminant_Analysis [Google Scholar] [Crossref]

23. Domiano, L. (2018). Common variables found among students who were unsuccessful on the NCLEXRN in a baccalaureate nursing program. Journal of Nursing and Education Practice, 8(7). https://doi.org/10.5430/JNEP.V8N7P1 [Google Scholar] [Crossref]

24. Ferrer, J. C. (2024). Academic performance and predictors of the licensure examination for teachers of the bachelor of elementary education. Malaysian Journal of Social Sciences and Humanities, 9(11). https://doi.org/10.47405/mjssh.v9i11.3091 [Google Scholar] [Crossref]

25. Gatpandan, M. A. C. C., Cruz, M. G. E. V., & Paguta, R. B. (2023). The factors affecting licensure passing rates of Philippine state universities and colleges – A panel regression analysis. International [Google Scholar] [Crossref]

26. Journal of Research in Engineering, Science and Management, 6(12), 83–94. https://journal.ijresm.com/index.php/ijresm/article/view/2884 [Google Scholar] [Crossref]

27. Gaylo, D., Anar, L., & Tantoy, O. (2022). A discriminative model for college admission test performance. Science International (Lahore) 34, 117–120. https://doi.org/10.5281/ZENODO.6416227 [Google Scholar] [Crossref]

28. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. https://www.perlego.com/book/2754480/multivariate-data-analysis-pdf [Google Scholar] [Crossref]

29. Igdon, J. P., Ballado, R. S., & Giray, A. L., Jr. (2024). Predictors of the licensure examination performance of graduates of a state university's external campus in the Philippines. International Multidisciplinary Research Journal. https://www.ioer-imrj.com/predictors-of-the-licensureexamination-performance-of-graduates-of-a-state-universitys-external-campus-in-the-philippines/ [Google Scholar] [Crossref]

30. Jang, H., Lee, S., Eom, W. (2024). The perceived improvement in learning competencies of lowachieving college students through participation in a board game design and development process. Korean Journal of General Education, 18(5), 367–383. https://doi.org/10.46392/kjge.2024.18.5.367 [Google Scholar] [Crossref]

31. Karalar, H., Kapucu, C. & Guruler, H. (2021). Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system. International Journal of Educational Technology in Higher Education. https://link.springer.com/content/pdf/10.1186/s41239-021-00300y.pdf [Google Scholar] [Crossref]

32. Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148–161. https://doi.org/10.1037/0022-3514.86.1.148 [Google Scholar] [Crossref]

33. Labad, V., Hiponia, J. L., & Gabales, B. (2019). Preservice teachers' entry credentials, SATT performance, and academic achievement: A discriminant analysis. Southeastern Philippines Journal of Research and Development, 24(2), 21-44. https://doi.org/10.53899/spjrd.v24i2.18 [Google Scholar] [Crossref]

34. Makiling, R. S., Abalos, C. G., Abad, M. L. I., & Cabuquin, J. C. (2025). The role of academic achievement in mathematically predicting licensure examination for teachers' outcomes: Insights from secondary education graduates. Journal Elemen, 11(1), 171–185. https://doi.org/10.29408/jel.v11i1.27698 [Google Scholar] [Crossref]

35. Martin, V. (2015). The Relationship Between Educational Mock Boards and Clinical Board Examinations. Idaho State University. https://www.proquest.com/openview/f1e52f46d7891e9333a3eece0dbd5d11/1?pqorigsite=gscholar&cbl=18750&diss=y [Google Scholar] [Crossref]

36. Martin, V. M., Rogo, E. J., Hodges, K. O., Piland, N. F., & Osborn Popp, S. E. (2017). The relationship between mock boards and clinical board examinations in dental hygiene education. Journal of Dental Education, 81(1), 54–64. https://doi.org/10.1002/j.0022-0337.2017.81.1.tb06247.x [Google Scholar] [Crossref]

37. Matusalem, N. M., Bandoy, M. M., Buama, C. A. C., Manaig, K. A., & Yazon, A. (2024). Board performance of fisheries graduates: logistic regression analysis for curriculum enhancement. Applied Quantitative Analysis, 4(2). https://doi.org/10.31098/quant.2620 [Google Scholar] [Crossref]

38. Mendez, M. L. S. P. (2025). Academic performance as a predictor of LET success: A regression analysis among BEED graduates at Davao Oriental State University Cateel campus. Asian Journal of Education and Social Studies, 51(8). https://journalajess.com/index.php/AJESS/article/view/2310 [Google Scholar] [Crossref]

39. Mercedes, R., Lehman, E., Kerley, P., Hall, C., Englert, S., Connelly, D., Baden, M., Cain, M., Chang, S. S., Thrasher, B., & Raman, J. D. (2024). Association between resident in-service exam scores by postgraduate year and subsequent board qualifying exam. Urology Practice, 12(1), 177–183. https://www.auajournals.org/doi/10.1097/UPJ.0000000000000708 [Google Scholar] [Crossref]

40. Niileksela, C. R., Hajovsky, D. B., He, Z., & Villeneuve, E. F. (2025). Cognitive-achievement relations with the Woodcock-Johnson V. Journal of Psychoeducational Assessment, 43(8). https://doi.org/10.1177/07342829251353032 [Google Scholar] [Crossref]

41. Oducado, R. M. F., Cendaña, D. P., & Belo-Delariarte, R. G. (2019). Institutional competency assessment and other factors influencing the nurse licensure examination. International Journal of Scientific & Technology Research, 8(12), 1–5. https://www.yumpu.com/en/document/view/63103529/institutionalcompetency-assessment-and-other-factors-influencing-the-nurse-licensure-examination [Google Scholar] [Crossref]

42. Ojating, H. (2022). Application and utility of linear discriminant analysis in educational statistics. Journal of Contemporary Research, 1(1), 42–48. https://storage.googleapis.com/productiondomaincom-v1-0-9/899/1324899/anDXyHHc/42c80e0f1f9f4722b58fb1d188fa82f8?fileName=APPLICATION [Google Scholar] [Crossref]

43. Ortiz-de-Villate, C., Rodríguez-Santero, J., & Torres-Gordillo, J. J. (2021). Contextual, personal and family factors in explaining academic achievement: a multilevel study. Sustainability, 13(20), 11297. https://www.mdpi.com/2071-1050/13/20/11297 [Google Scholar] [Crossref]

44. Osterhage, E., Usher, E. L., Douin, T. A., & Bailey, W.M. (2019). Opportunities for self-evaluation increase student calibration in an introductory biology course. Life Sciences Education, 18(2). https://doi.org/10.1187/cbe.18-10-0202 [Google Scholar] [Crossref]

45. Padullon, A. S. (2025). Performance profile of Bachelor of Science in Agribusiness Management graduates in the licensure examination for agriculturists. Davao Research Journal, 11(3). https://doi.org/10.59120/drj.v11i2.423 [Google Scholar] [Crossref]

46. Palmer, E., Esposito, E.R., Shin, M., Raake, S. E., Malcom, D.R., & Daugherty, K.K. (2020). Impact on intersession course remediation on NAPLEX/PCOA scores in an accelerated doctor of pharmacy program." Elsevier BV. https://doi.org/10.5688/ajpe7827 [Google Scholar] [Crossref]

47. Pizarro, J. B. & Talosig, A. T. (2025). Evaluating the effectiveness of mitigating actions in risk management: An assessment of interventions and outcomes in St. Paul University Philippines. International Journal of Social Science Research and Review, 8(8), 58–68. http://dx.doi.org/10.47814/ijssrr.v8i8.2746 [Google Scholar] [Crossref]

48. Polinar, E. L., Delima, A. J., Vilchez, R. (2020). Students' performance in board examination analysis using Naive Bayes and C4.5 Algorithms. International Journal of Advanced Trends in Computer Science and Engineering, 9(1). https://doi.org/10.30534/ijatcse/2020/107912020 [Google Scholar] [Crossref]

49. Professional Regulation Commission. (2024). 2024 Annual PRC Report. https://prc.gov.ph/sites/default/files/2024%20PRC%20Annual%20Report%20(1).pdf [Google Scholar] [Crossref]

50. Raven, J. C. (2000). Raven's Progressive Matrices and Raven's Progressive Matrices Plus (PM) Manual. Oxford: Oxford Psychologists Press. https://studylib.net/doc/27187921/rspm-manual-random [Google Scholar] [Crossref]

51. Reynolds, K., Bazemore, C., Hanebuth, C., Hendren, S., & Horn, M. (2021). The relationship of noncognitive factors to academic and clinical performance in graduate rehabilitation science students in the United States: A systematic review. Journal of Educational Evaluation for Health Professions. https://doi.org/10.3352/jeehp. 2021.18.31 [Google Scholar] [Crossref]

52. Rowtho, V. (2017). Early detection of at-risk undergraduate students through academic performance predictors. Higher Education Studies 7(3). https://doi.org/10.5539/hes.v7n3p42 [Google Scholar] [Crossref]

53. Sears, N. A., Othman, M., O'Neil, L. & Hopman, W. M. (2017). Canadian NCLEX-RN outcomes: A two-year cross-sectional exploratory study in Ontario. Journal of Nursing Education and Practice, 7(10). https://doi.org/10.5430/jnep.v7n10p36 [Google Scholar] [Crossref]

54. Sicuan, S., Ermino, J., & Junio, R. (2025). Analyzing career progress and achievements post-Licensure Examination for Teachers: A tracer study of PSU Binmaley Campus BSE graduates. International Journal for Multidisciplinary Research, 7(1). https://www.ijfmr.com/papers/2025/1/36619.pdf [Google Scholar] [Crossref]

55. Strowd, L., Gao, H., Williams, D. M., Peters, T. R., Jackson, J. (2022). Early pre-clerkship clinical skills assessments predict clerkship performance. The Journal of the International Association of Medical Science Educators, 32, 463–471. https://doi.org/10.1007/s40670-022-01519-8 [Google Scholar] [Crossref]

56. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson Education. https://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf [Google Scholar] [Crossref]

57. Tamayo, A. M. (2015). Estimating predictors of the Philippine licensure examination for engineering. SSRN Electronic Journal. https://www.scribd.com/document/513215867/SSRN-id2560685 [Google Scholar] [Crossref]

58. Terry, R., Hing, W., Orr, R. & Milne, N. (2017). Do coursework summative assessments predict clinical performance? A systematic review. BMC Medical Education, 17(40). https://link.springer.com/article/10.1186/s12909-017-0878-3 [Google Scholar] [Crossref]

59. Thaher, T., Zaguia, A., Al Azwari, S., Mafarja, M., Chantar, H., Abuhamdah, A., Turabieh, H., Mirjalili, S., & Sheta, A. (2021). An enhanced evolutionary student performance prediction model using whale optimization algorithm boosted with sine-cosine mechanism. Multidisciplinary Digital Publishing Institute, 11(21). https://doi.org/10.3390/app112110237 [Google Scholar] [Crossref]

60. Varpio, L., Farnan, J. M. & Park, Y. S. (2017). Summary: Research shows that diseases need holistic care. Journal of the Association of American Medical Colleges, 92(11). https://doi.org/10.1097/acm.0000000000001923 [Google Scholar] [Crossref]

61. Vermunt, J. D. (2005). Relations between student learning patterns and personal and contextual factors and academic performance. Higher education, 49(3), 205–234. https://link.springer.com/article/10.1007/s10734-004-6664-2 [Google Scholar] [Crossref]

62. Wang, M. T., & Sheikh-Khalil, S. (2014). Does parental involvement matter for student achievement and mental health in high school? Child Development, 85(2), 610–625. https://doi.org/10.1111/cdev.12153 [Google Scholar] [Crossref]

63. Wongupparaj, P., Kumari, V., & Morris, R. G. (2015). A cross-temporal meta-analysis of Raven’s progressive matrices: Age groups and developing versus developed countries. Intelligence, 49, 1 –9. https://doi.org/10.1016/j.intell.2014.11.008 [Google Scholar] [Crossref]

64. Yuan, H., Yuan, W., Duan, S., Yong, R., Jiao, K., Wei, Y., Leach, M., Li, N., Zhang, X., Lim, E. G., & Song, P. (2024). Navigating the uncertainty: the impact of a student-centered final year project allocation mechanism on student performance. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-024-03324-7 [Google Scholar] [Crossref]

65. Zachry, A. H., Lancaster, S., Hall, A. & Hilsdon, A. (2025). The relationship between cognitive factors and noncognitive factors, including grit, and NBCOT exam performance. Occupational Therapy in Health Care, 39(3), 558–574. https://doi.org/10.1080/07380577.2024.2352708 [Google Scholar] [Crossref]

66. Zerbini, G., van der Vinne, V., Otto, L. K. M., Kantermann, T., Krijnen, W. P., Roenneberg, T., & Merrow, M. (2017). Lower school performance in late chronotypes: underlying factors and mechanisms. Scientific Reports 7, 4385. https://doi.org/10.1038/s41598-017-04076-y [Google Scholar] [Crossref]

• Interplay of Students’ Emotional Intelligence and Attitude toward Mathematics on Performance in Grade 10 Algebra
• Numerical Simulation of Fitzhugh-Nagumo Dynamics Using a Finite Difference-Based Method of Lines
• Fixed Point Theorem in Controlled Metric Spaces
• Usage of Moving Average to Heart Rate, Blood Pressure and Blood Sugar
• Exploring Algebraic Topology and Homotopy Theory: Methods, Empirical Data, and Numerical Examples