Abstract

Data Mining is the extraction of knowledge using solid data available in workplaces. This is also applied in the educational system to predict the academic performance of students. In this paper a prediction of unit earners performance in the Board Licensure Examination for Professional Teachers is conducted to know the chances of non- education graduates who wanted to pursue teaching. The researchers find out the performance of unit earners who passed the BLEPT for the past five years (2012-2016) with a total of 10 examination batches but not to include re-takers. The predictors included are the general weighted average in their undergraduate program and all grades earned in their professional courses. The data mining algorithms used came from the supervised classification algorithm category and the researcher included at least 3 classification algorithms to work on. The algorithm which has the probability accuracy will be recommended in the study.

References

• Data Mining Technology. http://www.megaputer.com/site/data_mining.php
• www.ched.gov.ph (2009). CHED MEMORANDUM ORDER. http://www.ched.gov.ph/wp-content/uploads/2013/07/CMO-No.11-s2009.pdf
• Bhardwaj B.K., Pal S. (2011), Data Mining: A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011 https://arxiv.org/ftp/arxiv/papers/1201/1201.3418.pdf
• Al-Radaideh, Q. A., Al-Shawakfa, E. W., Al-Najjar, M. I. (2006). Mining Student Data Using Decision Trees. https://repensarlafisica.files.wordpress.com/2015/10/mining-student-data-using-decision-trees.pdf
• Pandey, U.K. and Pal, S. (2011).Data Mining: A prediction of performer or underperformer using classification. (IJCSIT) International Journal of Computer Science and Information Technology, Vol. 2(2), pp.686-690.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.206.2659&rep=rep1&type=pdf
• Bharadwaj, B. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, https://arxiv.org/ftp/arxiv/papers/1201/1201.3417.pdf
• Tarun, I. M. , Gerardo, B. D. , Tanguilig III, B. T. (2014). Generating Licensure Examination Performance Models Using PART and JRip Classifiers: A Data Mining Application in Education. http://www.ijcce.org/papers/320-CS2004.pdf
• Pascua, J. and Navalta, J. (2011). Determinants of L.E.T. Performance of the Teacher Education Graduates in a State University. JPAIR Multidisciplinary Journal. Volume 6, http://www.eisrjc.com/documents/Determinants_of_LET_Performance_1325756724.pdf
• Pachejo, S. and Allaga, W. (2013). Academic Predictors of the Licensure Examination for Teachers’ Performance of the Rizal Technological University Teacher Education Graduates. International Journal of Educational Research and Technology. www.soeagra.Original Article com/ijert/ijert.htm
• Garcia, G. (2010). Academic Performance as Determinant to Pass the Licensure Examination for Teachers. International Journal of Educational Research and Technology. www.soeagra.Original Article com/ijert/ijert.htm
• Figuerres, O. (2010). An Analysis of the Performance of the University of Northern Philippines in the Licensure Examination for Teachers. International Journal of Educational Research and Technology. www.soeagra.Original Article com/ijert/ijert.htm
• Visco, D. A. (2015). Determinants of Performance in the Licensure Examination for Teachers (LET) of Abra State Institute of Sciences and Technology. International Journal of Management Research and Business Studies, Vol. 2, Issue 1. http://ijrmbs.com/vol2issue1/dionisio.pdf
• Visco, D. A. (2015). Predictors of Performance in the Licensure Examination For Teachers of the Graduates of Higher Education Institutions in Abra. International Journal of Management Research and Business Strategy, Vol. 4, No. 1, January 2015. https://www.ijmrbs.com/ijmrbsadmin/upload/IJMRBS_54d8591038ba7.pdf
• Umair Shafique, Haseeb Qaiser (2014). A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA).https://www.researchgate.net/publication/268770881_A_Comparative_Study_of_Data_Mining_Process_Models_KDD_CRISP-DM_and_SEMMA
• Amatriain X., Jaimes A., Oliver N. (2011). Chapter 2: Data Mining Methods for Recommender Systems. http://www.springer.com/cda/content/document/cda_downloaddocument/9780387858197-c1.pdf?SGWID=0-0-45-1007442-p173841681.
• Mrs. M.S. Mythili, Dr. A.R.Mohamed Shanavas (2014). An Analysis of student’s performance using classificationalgorithms.https://www.researchgate.net/publication/314445897_An_Analysis_of_students'_performance_using_classification_algorithms