Home⇛Isabela State University Linker: Journal of Education, Social Sciences and Allied Health⇛vol. 1 no. 1 (2024)

Unraveling the Link: Investigating the Correlation Between the Core Subjects of Architecture and Licensure Exam Performance in BS Architecture Graduates

Ronald Andrew R. Tolentin | Edwin R. Arboleda | Christian Jhay D. Legaspi | Janella Rose M. Noceda

Discipline: Education

 

Abstract:

This research paper aimed to investigate the relationship between the History, Theory, and Practices Subjects within the BS Architecture curriculum and the corresponding licensure exam results. By employing data mining techniques and leveraging the WEKA platform, this study identified the most influential subject within the History, Theory, and Practices domain for architecture students, using data from the academic years 2013 to 2019. The Decision Stump classifier was utilized, achieving an accuracy rate of 75% with a training time of 0 second. The attribute evaluator employed a search mode ranker, while the mean of the History, Theory, Planning, and Practices subjects was calculated. The findings of this study can assist college administrators and faculty members in guiding their students toward better performance in the licensure exam by identifying strengths and weaknesses in the specific subject area, as well as the overall performance variation. By gaining insights into the impact of these subjects on examination outcomes, educational institutions can enhance their curriculum and teaching methodologies to better prepare future architects for success in their professional endeavors.



References:

  1. Abu-Ghazzeh, T. M. (1997). Vernacular architecture education in the Islamic society of Saudi Arabia: Towards the development of an authentic contemporary built environment. Habitat International, 21(2), 229-253. https://doi.org/10.1016/S0197-3975(96)00056-2
  2. Adier, G. M. L., Reyes, C. A., & Arboleda, E. R. (2020). Discrimination of civet coffee using visible spectroscopy. Jurnal Teknologi dan Sistem Komputer, 8(3), 239-245. https://doi.org/10.14710/jtsiskom.2020.13734
  3. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. https://doi.org/10.1016/j.tele.2019.01.007
  4. Antiojo, L. P. (2017). Performance of education graduates in the licensure examination for teachers (LET). PEOPLE: International Journal of Social Sciences, 3(2), 1363-1384. https://doi.org/10.20319/pijss.2017.32.13631384
  5. Arboleda, E. R. (2023). Classification of immature and mature coffee beans using texture features and medium K nearest neighbor. Journal of Artificial Intelligence and Technology, 3(3), 114-118. https://doi.org/10.37965/jait.2023.0203 ojs.istp-press.com+1
  6. Bachhal, P., Ahuja, S., & Gargrish, S. (2021). Educational data mining: A review. Journal of Physics: Conference Series, 1950(1). https://doi.org/10.1088/1742-6596/1950/1/012022
  7. Callena, E., Gabales, B., Tutor, R., Villanueva, S., Gonzales, C., De Vera, A., Caberte, S., Nillas, V. B., Acerbo, J., & Pantaleon, A. (2019). Predictors of passing probability in the licensure examination for selected programs in the University of Southeastern Philippines. Southeastern Philippines Journal of Research and Development, 24(1), 1-16.
  8. Danaci, H. M. (2015). Creativity and knowledge in architectural education. Procedia – Social and Behavioral Sciences, 174, 1309-1312. https://doi.org/10.1016/j.sbspro.2015.01.752
  9. Dayaday, M. G. (2018). Factors affecting the performance in the board examination of electronics engineering – University of Southern Mindanao graduates. International Journal of Current Research, 10(9), 73710-73715.
  10. Geollegue, K. W. V., Arboleda, E. R., & Dizon, A. A. (2022). Seed of rice plant classification using coarse tree classifier. IAES International Journal of Artificial Intelligence, 11(2), 727-735. https://doi.org/10.11591/ijai.v11.i2.pp727-735
  11. Johnson, T. III. (2020). Machine learning evaluations using WEKA (Honors thesis). Elizabeth City State University. https://libres.uncg.edu/ir/ecsu/f/Johnson_Thomas_Honors%20Thesis%20_Spring%202020.pdf
  12. Polinar, E. L., Delima, A. J. P., & Vilchez, R. N. (2020). Students’ performance in board examination analysis using Naïve Bayes and C4.5 algorithms. International Journal of Advanced Trends in Computer Science and Engineering, 9(2).
  13. Rustia, R. A., Cruz, M. M. A., Burac, M. A. P., & Palaoag, T. D. (2018). Predicting students’ board examination performance using classification algorithms. ACM International Conference Proceeding Series, 1(1), 233-237. https://doi.org/10.1145/3185089.3185101
  14. Silvestri, L. A., Clark, M. C., & Moonie, S. A. (2012). Using logistic regression to investigate self-efficacy and the predictors for National Council Licensure Examination success for baccalaureate nursing students. Journal of Nursing Education and Practice, 3(6), 21-34. https://doi.org/10.5430/jnep.v3n6p21
  15. Su, Y. S., & Lai, C. F. (2021). Applying educational data mining to explore viewing behaviors and performance with flipped classrooms on the social media platform Facebook. Frontiers in Psychology, 12, 653018. https://doi.org/10.3389/fpsyg.2021.653018
  16. Tamayo, A., Bernardo, G., & Eguia, R. (2014). Readiness for the licensure exam of the engineering students. SSRN Electronic Journal, 3(1), 1-12. https://doi.org/10.2139/ssrn.2395037
  17. Tamblyn, R., Abrahamowicz, M., Dauphinee, W. D., Hanley, J. A., Norcini, J., Girard, N., Grand’Maison, P., & Brailovsky, C. (2002). Association between licensure examination scores and practice in primary care. JAMA, 288(23), 3019-3026. https://doi.org/10.1001/jama.288.23.3019
  18. Zwarenstein, M., Reeves, S., & Perrier, L. (2005). Effectiveness of pre-licensure interprofessional education and post-licensure collaborative interventions. Journal of Interprofessional Care, 19(1), 148-165. https://doi.org/10.1080/13561820500082800

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