Prediction Model on the Relationship of Undergraduate Grades and Licensure Examination Performance of BS Agriculture and Biosystems Engineering
Kim Darell R. Salgado | Edwin R. Arboleda | Mariella Janelle D. Cabardo | Chenna Mae P. Obejera | Jesusimo L. Dioses Jr.
Discipline: engineering (non-specific)
Abstract:
This study employed WEKA software and data mining techniques in order to identify the critical grade subject(s) needed for passing the Licensure Examination for the BS Agricultural and Biosystems Engineering. The study's proponents examined the ABE licensure examination and grades of 84 BSABE graduate students between September 2015 and October 2019 in order to ascertain if higher scores on undergraduate exams correlate with passing the licensure examination. Researchers also created a dataset covering all academic subjects within the BSABE Program, such as Engineering Mathematics, Science Subjects, Major Subjects, General Subjects, and Competency Appraisal subjects. Their results suggested that undergraduate students who scored highly on these subjects during their undergraduate studies stood a better chance of passing the ABE Licensure Examination.
References:
- 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
- Alkharusi, H., & Al-Hinai, A. (2018). Predicting students’ academic performance using their demographic and academic data: A case study. International Journal of Information and Education Technology, 8(4), 268-273.
- Antonio, J., Malvar, R., Ferrer, M., & Pambuena, E. (2016). Licensure examination for teachers results from 2010 to 2013 of PUP San Pedro’s Bachelor in Secondary Education major in Mathematics and English graduates and its relationship on their academic performance. Asia Pacific Journal of Multidisciplinary Research, 4(44), 17-22.
- 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
- Bae, R. E., Arboleda, E., Andilab, A., & Dellosa, R. M. (2019). Implementation of template matching, fuzzy logic and K nearest neighbour classifier on Philippine banknote recognition system. International Journal of Scientific & Technology Research, 8(8), 1451-1453. https://www.ijstr.org/final-print/aug2019/Implementation-Of-Template-Matching-Fuzzy-Logic-And-K-Nearest-Neighbor-Classifier-On-Philippine-Banknote-Recognition-System-.pdf
- Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), Article 17. https://doi.org/10.20429/ijsotl.2010.040217
- Banluta, J. (2013). Relationship of the academic rating and board examination performance of the Electronics Engineering graduates [Conference paper]. IETEC ’13 Conference, Ho Chi Minh City, Vietnam. Available from https://www.researchgate.net/publication/324416083_Relationship_of_the_Academic_Rating_and_Board_Examination_Performance_of_the_Electronics_Engineering_Graduates
- Batuctoc, L. V. (2021). Effectiveness of metacognition-based reading enrichment program to students’ reading comprehension. International Journal of Advanced Research, 9(2), 111-135. https://doi.org/10.21474/IJAR01/12425
- Bruce, S., Crawford, E., Wilkerson, G., Dale, R. B., Harris, M., & Rausch, D. (2019). Prediction modelling for board of certification exam success for a professional master’s athletic training program. Journal of Sports Medicine and Allied Health Sciences: Official Journal of the Ohio Athletic Trainers’ Association, 5(2). https://doi.org/10.25035/jsmahs.05.02.07
- CEIT News and Updates – Cavite State University.” (2022, January). CvSU CEIT News and Updates. Retrieved from https://cvsu.edu.ph/ceit-news-and-updates/
- Chan-Rabanal, G. (2016). Academic achievement and LET performance of the Bachelor of Elementary Education graduates, University of Northern Philippines. International Journal of Scientific and Research Publications, 6(6), 455-461.
- Dagdag, J. (2018). Predictors of performance in the Licensure Examination for Agriculturists: Bases for a proposed plan of action. Asia Pacific Journal of Multidisciplinary Research, 6(2), 113–120.
- Ferrer, F. P. (2016). Performance in the Engineer Licensure Examinations: Philippines, 2011-2015. International Journal of Advance Research in Science and Engineering, 5(1), 5–58. Available at https://www.ijarse.com/images/fullpdf/1454832385_238S.pdf
- 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
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10-18. https://dl.acm.org/doi/10.1145/1656274.1656278
- Ladia, M., & Nool, N. R. (2020). Performance in the Licensure Examination for Teachers among the graduates of Isabela State University, Echague, Isabela, Philippines. Journal of Critical Reviews, 7(11). Retrieved from https://www.academia.edu/7963956/Performance_in_the_Licensure_Examination_for_Teachers_of_State_Universities_and_Colleges_in_Region_III
- Landrum, R. E., & Harrold, R. L. (2015). Predicting performance on the Counselor Preparation Comprehensive Examination from undergraduate academic performance. Counselor Education and Supervision, 54(3), 154-166.
- Laguador, J., & Dizon, N. C. (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 and Engineering, 3(8), 347-378. https://research.lpubatangas.edu.ph/wp-content/uploads/2014/04/IJMIE-Academic-Achievement-in-the-Learning.pdf
- Maaliw, R. R. (2021). Early prediction of electronics engineering licensure examination performance using random forest. In 2021 IEEE World AI IoT Congress (AIIoT 2021) (pp. 41–47). IEEE. https://doi.org/10.1109/AIIoT52608.2021.9454213
- Manalo, V. M., Arboleda, E. R., Dioses, J. L., & Dellosa, R. M. (2019). Differentiation among lettuce (L. sativa) seed varieties grown in gourmet farms, Silang Cavite, Philippines, using image processing with fuzzy logic and KNN as classifiers. International Journal of Scientific & Technology Research, 8(10), 318-321.
- Nicolas, H. J., De Guzman, J., Tejada, R. C., & Capalad, R. (2020). Student determinants in the licensure examination for agriculturists of a state college in the Philippines. International Journal of Educational Sciences, 28(1-3), [page numbers not provided]. https://doi.org/10.31901/24566322.2020/28.1-3.1117
- Oducado, R. M. F., Sotelo, M. G., Ramirez, L. M. M., Habaña, M. P., & Belo-Delariarte, R. G. (2020). English language proficiency and its relationship with academic performance and the nurse licensure examination. Nurse Media Journal of Nursing, 10(1), 46-56. https://doi.org/10.14710/nmjn.v10i1.28564
- Pau, A., Jeevaratnam, K., Chen, Y. S., & Fallis, D. (2017). The impact of study habits on academic performance: A study of students in a pharmacy program. Currents in Pharmacy Teaching & Learning, 9(2), 199-206.
- Rabe K.E., Arboleda, E., Andilab, A., & Dellosa, R. (2019). Fuzzy logic based vehicular congestion estimation monitoring system using image processing and KNN classifier. International Journal of Innovative Technology and Exploring Engineering, 8(12), 2278-3075.
- Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353-387.
- Rienecker, L., & Stray Jørgensen, P. (2019). Studying at university: A guide for first-year students (2nd ed.). Gyldendal Akademisk.
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 318-362). MIT Press.
- Rustia, R., Cruz, M. M., Burac, M. A., & Palaoag, T. (2018). Predicting students’ board examination performance using classification algorithms. In 2018 7th International Conference on Software and Computer Applications (ICSCA 2018), ACM International Conference Proceeding Series (pp. 233-237). https://doi.org/10.1145/3185089.3185101
- Sánchez-Martín, J., Suárez-Cantero, C., & Delgado-Rodríguez, M. (2019). Influence of academic performance and academic self-efficacy on the preparation for university entrance exams: A study among Spanish students. Higher Education Research & Development, 38(6), 1195-1209.
- Simbulas, L. C., Regidor, B. T., & Catulpos, R. (2015). Reading comprehension and mathematical problem solving skills of University of the Immaculate Conception freshmen students. UIC Research Journal, 21(2), 67-70. Retrieved from https://ejournals.ph/article.php?id=12835
- Sverdlik, A., & Hall, N. C. (2016). Motivation and self-regulated learning: A multilevel analysis. Learning and Individual Differences, 45, 161-168.
- Swaen, B. (2019, May 4). Developing a conceptual framework for research example. Scribbr. Retrieved from https://www.scribbr.com
- Su, Y.-S., & Lai, C.-F. (2021). Applying educational data mining to explore viewing behaviours and performance with flipped classrooms on the social media platform Facebook. Frontiers in Psychology, 12, Article 653018. https://doi.org/10.3389/fpsyg.2021.653018
- Tamayo, A., Bernardo, G., & Eguia, R. (2014). Readiness for the licensure exam of the engineering students. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2395037
- Tarun, I. M. (2017). Prediction models for licensure examination performance using data mining classifiers for online test and decision support system. Asia Pacific Journal of Multidisciplinary Research, 5(3), 10-21.
- Zeidner, M., & Klug, H. J. (2013). The role of study time and study strategy in predicting academic achievement outcomes: A review. Educational Psychology Review, 25(3), 375-405.
- Zhang, X., Koponen, T., Räsänen, P., Aunola, K., Lerkkanen, M.-K., & Nurmi, J.-E. (2013). Linguistic and spatial skills predict early arithmetic development via counting sequence knowledge. Child Development, 85(3), 1091-1107. https://doi.org/10.1111/cdev.12173
ISSN 3082-3684 (Online)
ISSN 3082-3676 (Print)