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Abstract
This research examines the correlation between the academic achievement and licensing test outcomes of electrical engineering (EE) and mechanical engineering (ME) graduates from Nueva Vizcaya State University (NVSU) in the Philippines over a five-year span. This study used a quantitative research technique involving a descriptive-correlational approach, trend analysis, and path analysis to examine data from graduates who underwent licensing examinations for the first time during this period. The results showed a significant correlation between academic achievement in certain subject areas and success in licensing exams for graduates in electrical engineering (EE) and mechanical engineering (ME). The equation for calculating the Board Rating for EE graduates is: Board Rating = 125.430 - (17.581 * ESAS) + (12.208 * MATH) - (13.011 * EE). The logistic regression equation is P = 1/(1 + e^(-(24.99651 + (5.812567 * MATH) - (3.72252 * ESAS) - (10.1496 * EE)), while the discriminant equation is D = -13.577 - (3.943 * MATH) + (2.723 * ESAS) + (6.134 * EE). The formula for calculating the Board Rating for ME graduates is as follows: Board Rating = 121.578 - (10.387 * IPPE) - (5.980 * MATHA) - (0.721 * MACHINE). The logistic regression equation is P = 1/(1 + e^(-(16.65924 - 1.99212 * MATHA - 5.60296 * IPPE + 2.329647 * MACHINE)), while the discriminant equation is D = -11.573 + 5.823 * IPPE + 0.931 * MATHA - 2.592 * MACHINE. Path analysis clarified both the direct and indirect impacts of academic success on the licensing test results. Mathematical models provide useful insights for engineering education, highlighting the need for focused curriculum creation and student assistance in engineering education programs. This research emphasizes the importance of certain academic accomplishments as predictors of success in professional licensing exams.
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References
Aramburo V., B. B. (2016). Predictive factors associated with academic performance in college students. 7th International Con-ference on Intercultural Education “Edu-cation, Health and ICT for a Transcultural World. (pp. 945-949). Almeria, Spain: Elsevier Ltd. https://doi.org/10.1016/j.sbspro.2017.02.133
Ballado-Tan, J. (2014). Academic performance, aspirations, attitudes, and study habits as determinants of the performance in li-censure examination of accountancy graduates. International Journal of Edu-cation and Research, 61-70.
Burtner, J. (2005). The use of discriminant analysis to investigate the influence of non-cognitive factors on engineering school persistence. Journal of Engineer-ing Education. https://doi.org/10.1002/j.2168-9830.2005.tb00858.
Dayton, C.M. (1992). Logistic Regression Anal-ysis. Department of Measurement, Statis-tics and Evaluation, University of Mary-land.
De Winter, JCF., & Dodou, D. (2011). Predict-ing academic performance in engineering using high school exam scores. Interna-tional Journal of Engineering Education, 27(6), 1-9.
Dotong, C. (2019). Licensure examination per-formance of mechanical engineering graduates and its relationship with aca-demic performance. Asia Pacific Journal of Academic Research in Social Sciences, Vol. 4, 7-14.
Edge, O., & Friedberg, S. (1984). Factors affect-ing achievement in the first course in cal-culus. Journal of Experimental Education, 136-140. https://doi.org/10.1080/00220973.1984.11011882
Ferrer, F. P. (2016). Performance in the engi-neer licensure examinations: Philippines 2011-2015. International Journal of Ad-vancednavce Research in Science and En-gineering, 50-58.
Flores, J. L. (2015). Curricular program status of the mechanical and electrical engi-neering colleges of Cebu technological university in the licensure examination. Tropical Technology Journal Volume 18, Issue 1.
Fong Lam, U., Chen, W.-W., Zhang, J., & Liang, T. (2015). It feels good to learn where I belong: School belonging, academic emo-tions, and academic achievement in ado-lescents. School Psychology International, 36(4), 393-409. https://doi.org/10.1177/0143034315589649
Garson, D. (2008). Path Analysis. Raleigh, North Carolina.
Gietz, C., & McIntosh, K. (2014). Relations be-tween student perceptions of their school environment and academic achievement. Canadian Journal of School Psychology, 29(3), 161-176. https://doi.org/10.1177/0829573514540415
Haines, C. & Crouch, R. (2007). Mathematical modeling and applications: ability and competence frameworks. New ICMI Study Series. DOI: 10.1007/978-0-387-29822-1_46
Herrero, C. C. (2015). Influence of selected fac-tors on cpa licensure examination results. International Letters of Social and Hu-manistic Sciences, 87-93.
Juanatas, C.I. and Juanatas, R. A. (2019). Pre-dictive data analytics using logistic re-gression for licensure examination
performance. 2019 International Confer-ence on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, pp. 251-255, doi: 10.1109/ICCIKE47802.2019.9004386.
Laguador JM, Dotong CI. (2020). Engineering students’ challenging learning experienc-es and their changing attitude towards academic performance. European J Ed Res. 9(3):1127-1140. doi: 10.12973/eu-jer.9.3.1127
Maaliw, R. R. (2021). Early prediction of elec-tronics engineering licensure examina-tion performance using random forest. IEEE World AI IoT Congress (AIIoT), Seat-tle, WA, USA. pp. 0041-0047, doi: 10.1109/AIIoT52608.2021.9454213
Mohammed, M. P., & Mohammed, M. P. (2017). Licensure Examination Performance Evaluation of the Candidate Engineers as Basis for a Proposed Action Plan. Asia Pacific Journal of Multidisciplinary Re-search, Vol. 5, No. 2, 51-57.
Multiple Linear regression. Retrieved from: https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses
Multiple Linear Regression. Retrieved from (https://www.ibm.com/docs/en/cognos-analytics/11.1.0?topic=tests-multiple-linear-regression).
Neumaier, A. (2004). Mathematical Model Building, Chapter 3 in: Modeling Lan-guages in Mathematical Optimization (J. Kallrath, ed.), Applied Optimization, Vol. 88.
Raymond, M. R. (n.d.). Job Analysis and the Development of Test Specifications for Licensure and Certification Examinations. Educational Resources Information Cen-ter, 1995.
Salkind, N. (2010). Logistic Regression Analy-sis. Encyclopedia of Research Design.
Seabi, J. (2011). Relating learning strategies, self-esteem, intellectual functioning with academic achievement among first-year engineering students. South African Jour-nal of Psychology, 239-249.
Tamayo, A. M., & Canizares, R. (2014). Predic-tors of engineering licensure examination using logistic regression. Journal of Edu-cation, Society and Behavioural Science, 4(12), 1621–1629. https://doi.org/10.9734/BJESBS/2014/11343
Terano, H. (2018). Regression model of the licensure examination performance of the electronics engineering graduates in a state college in the Philippines. Advances and Applications in Mathematical Scienc-es, 197-204.
Ullman, J.B. (2006). Structural Equation Mod-eling. In B. G. Tabachnick & L. S. Fi-dell (Eds). Using Multivariate Statistics. 5th ed.
Zheng, C., Gaumer Erickson, A., Kingston, N. M., & Noonan, P. M. (2014). The relation-ship among self-determination, self-concept, and academic achievement for students with learning disabilities. Jour-nal of Learning Disabilities, 47(5), 462-474. https://doi.org/10.1177/0022219412469688