Generating Licensure Examination Performance Models Using JRip Classifier: A Data Mining Application in Civil Engineering
DOI:
https://doi.org/10.65141/jessah.v1i1.n2Keywords:
JRip, WEKA, Attribute, Licensure Examination, Civil engineeringAbstract
The purpose of this study is to create performance models for civil engineer licensure examinations using the JRip classifier. it identified the attributes that are significant to the response attribute; it generated prediction models using JRip classifiers of WEKA; and it determined how likely is a CE gradute to pass the CE licensure examination. The respondent was obtained from the CE graduate of Cavite State University Indang main campus whos took a CE board examination from November 2016 to May 2019. The results obtained indicated the significance of the subject AENG 65, as well as CENG 65B through CENG 130 in predicting the CE licensure examination. The CE graduate is predicted to fail if the grade of AENG 65 is greater than equal to 3 and CENG 135 is less than equal to 2.5 and If CENG 120A and MATH 21B is greater than equal to 2.75 and the CENG 106 is less than 1.75 it also further concluded that if DCEE27 is greater than equal to 2.5 and the CENG 22A is greater than equal to 3 and the grade of CENG 110A less than equal to 2.75 then the CE graduate will fail the Licensure exam
References
Ahmeda, R. A. E. D., Shehaba, M. E., Morsya, S., & Mekawiea, N. (2015). Performance study of classification algorithms for consumer online shopping attitudes and behavior using data mining. 2015 5th International Conference on Communication Systems and Network Technologies (pp. 1344–1349). IEEE. https://doi.org/10.1109/CSNT.2015.50
Amanonce, J. C. T., & Maramag, A. M. (2020). Licensure examination performance and academic achievement of teacher education graduates. International Journal of Evaluation and Research in Education, 9(3), 510–516. https://doi.org/10.11591/ijere.v9i3.20614
Bell, S. (2009). Experimental design. In International Encyclopedia of Human Geography (pp. 672–675). Elsevier.
Dayaday, M. G. (2018). Factors affecting the performance in the board examination of electronics engineering: University of Southern Mindanao graduates [Unpublished master’s thesis]. University of Southern Mindanao.
Dizon, N. (2013). Academic achievement in the learning domains and performance in licensure examination for engineers among LPU’s mechanical and electronics engineering graduates. International Journal of Management, IT & Engineering, 3(8), 347–378. https://research.lpubatangas.edu.ph/wp-content/uploads/2014/04/IJMIE-Academic-Achievement-in-the-Learning.pdf
Ferrer, F. P. (2015). Performance in the engineer licensure examinations [Master’s thesis, Pamantasan ng Lungsod ng Maynila, Philippines].
Fiscal, R. R., & Roman, A. G. (2022). Pre-licensure examination as predictor of licensure examination for teachers result. International Journal of Evaluation and Research in Education, 11(1), 136–142. https://doi.org/10.11591/ijere.v11i1.22063
Gupta, A., Mohammad, A., Syed, A., & Malka, N. (2016). A comparative study of classification algorithms using data mining: Crime and accidents in Denver City, USA. International Journal of Advanced Computer Science and Applications, 7(7), 374–381. https://doi.org/10.14569/IJACSA.2016.070749
Han, J., & Kamber, M. (2016). Data mining: Concepts and techniques (2nd ed.). Morgan Kaufmann.
Hanrahan, G., Zhu, J., Gibani, S., & Patil, D. G. (2004). Chemometrics and statistics – Experimental design. In Encyclopedia of Analytical Science (2nd ed., pp. 8–13). Elsevier.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
Hindle, A., German, D. M., Holt, R. C., & Godfrey, M. W. (2009). Automatic classification of large changes into maintenance categories. In Proceedings of the IEEE International Conference on Program Comprehension (pp. 30–39). IEEE.
Kovacic, J. Z. (2010). Early prediction of student success: Mining student enrolment data. In Proceedings of the 2010 InSITE Conference (pp. 647–655). Informing Science Institute.
Lascano, E. A., & Bansiong, A. J. (2017). Five-year licensure examination performance of library and information science graduates. Asian Journal of Contemporary Education, 1(1), 1–9.
Mellalieu, P. J. (2011, July–August). Predicting success, excellence, and retention from students’ early course performance: Progress results from a data-mining-based decision support system in a first year tertiary education programme. In International Conference of the International Council for Higher Education (Miami/Ft. Lauderdale, Florida) (pp. 1–9).
Mita, S., Yamazoe, Y., Kamataki, T., & Kato, R. (1981). Cancer letters data mining: The textbook.
Ong, M. B., Palompon, D. R., & Bañico, L. (2012). Predictors of nurses’ licensure examination performance of graduates in Cebu Normal University, Philippines. Asian Journal of Health, 2(1).
Roehrig, S. M. (1988). Prediction of licensing examination scores in physical therapy graduates. Physical Therapy, 68(5), 694–698.
Roman, A. G. (2018). Fractal dimension analysis of pre-board and licensure examination results of teacher education graduates. Asian Journal of Multidisciplinary Studies, 1(2), 40–49.
Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., & Wani, E. (2011). Prediction of student academic performance by an application of data-mining techniques. Management and Artificial Intelligence, 6, 110–114.
Slezak, D., Schaefer, G., Vuong, S. T., & Kim, Y. S. (Eds.). (2014). Active media technology: 10th International Conference, AMT 2014, Warsaw, Poland, August 11–14, 2014: Proceedings (Lecture Notes in Computer Science, Vol. 8610). Springer.
Soriano, H. (2009). Factors associated with the performance of USM College of Education graduates in the 2007 licensure examination for teachers. USM R&D, 17(2), 151–159.
Tarun, I. M., Gerardo, B. D., & Tanguilig, B. T. (2014). Generating licensure examination performance models using PART and JRip classifiers: A data mining application in education. International Journal of Computer and Communication Engineering, 3(3), 202–207.
UNESCO. (2010). Engineering: Issues, challenges and opportunities for development. UNESCO Publishing.
Veeralakshmi, V. (2015). Ripple-down rule learner (RIDOR) classifier for IRIS dataset. International Journal of Computer Science Engineering, 4(3), 79–85.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Education, Social Science and Allied Health
This work is licensed under a Creative Commons Attribution 4.0 International License.
