Research Article

Mathematical Models in Predicting Retention of STEM Students in Pre-Calculus

Norman Cuello Barroso 1 *
More Detail
1 Tanza National Comprehensive High School, PHILIPPINES* Corresponding Author
International Journal of Pedagogical Development and Lifelong Learning, 1(1), 2020, ep2004,
OPEN ACCESS   2862 Views   3302 Downloads
Download Full Text (PDF)


This study aimed to determine the mathematical models in predicting retention of STEM students in Pre-Calculus. The study utilized a non-experimental research specifically a cross-sectional predictive design. The independent variables in the study are the Grade Point Average (GPA) in Mathematics 10, General Weighted Average (GWA) in grade 10, National Career Assessment Examination (NCAE) - mathematical ability, NCAE - STEM results, gender and family monthly income. The dependent variable is the retention of STEM students in Pre-Calculus. The instruments in the study are Pre-Calculus Retention Test (PRT), interview and documentary analysis. The PRT was validated by five experts and underwent reliability testing with a Cronbach alpha value of 0.524. Percentage, mean, standard deviation, Pearson Product Moment Coefficient of Correlation and Multiple Regression Analysis were applied in the study. The researcher used IBM SPSS version 20 in analyzing the data gathered. The study developed two mathematical models that can predict retention of STEM students in Pre-Calculus. Using the standardized coefficients, the formula in predicting retention of STEM students in Pre-Calculus are y = 0.035x1 + 0.632x2 - 31.462 and y = 0.033x1 + 0.599x3 - 28.370 where y is PRT scores of the STEM students, x1 is NCAE-Mathematical Ability scores, x2 is GPA in Mathematics 10 and x3 is GWA in grade 10. . It can be gleaned on the mathematical models that the best predictor of the retention of STEM students in Pre-Calculus are GPA in Mathematics 10 and GWA in grade 10.


Barroso, N. C. (2020). Mathematical Models in Predicting Retention of STEM Students in Pre-Calculus. International Journal of Pedagogical Development and Lifelong Learning, 1(1), ep2004.


  1. Ahmed Gubbad, A. (2010). The effect of cooperative learning on the academic achievement and retention of the mathematics concepts at the primary school in Holy Makkah. Educational Science and Islamic Studies, 22(2), 13-23.
  2. Akaazua, J. T., Bolaji, D. C., Kajaru, K., Musa, M., & Bala, K. (2017). Effect of concrete manipulative approach on attitude, retention and performance in Geometry among junior secondary school students in Benue State, Nigeria. IOSR Journal of Research & Method in Education, 7(6), 80-175.
  3. Alkhasawneh, R., & Hargraves, R. (2014). Developing a Hybrid Model to Predict Student First Year Retention in STEM Disciplines Using Machine Learning Techniques. Journal of STEM Education, 15(3). Retrieved on May 31, 2020 from
  4. Chan, J. G. (2019). The labor code of the Philippines, annotated, labor standards and social legislation (Volume 3). ChanRobles Publishing Company.
  5. Ferrer, F. P., & Dela Cruz, R. J. (2017). Correlation of STEM students’ performance in the National Career Assessment Examination and academic subjects. People: International Journal of Social Sciences, 3(1), 532-541.
  6. Ford, F. (2011). The effect of family poverty on children’s academic achievement: Parental discussion and neighborhood poverty as mediating variables. Graduate School-Camden Rutgers, The State University of New Jersey. New Jersey, USA.
  7. Furner, J. M. (2017). Helping all Students Become Einstein’s using Bibliotherapy when Teaching Mathematics to Prepare Students for a STEM World. Pedagogical Research, 2(1), 01.
  8. Hassan, M. N., Abdullah, A. H., Ismail, N., Suhud, S. N. A., & Hamzah, M. H. (2019). Mathematics Curriculum Framework for Early Childhood Education Based on Science, Technology, Engineering and Mathematics (STEM). International Electronic Journal of Mathematics Education, 14(1), 15-31.
  9. Kashahu L. (2011). The impact of gender, some demographic characteristics and parent-teacher relationship on adolescent academic achievement. “Aleksander Moisu” University. Durres, Albania.
  10. Kurumeh, F. S., Onah, F. O., & Mohammed, A. S. (2012). Improving students’ retention in junior secondary school Statistics using the ethno-mathematics teaching approach in Obi and Oju local government areas of Benue State, Nigeria. Greener Journal of Educational Research, 2(3), 54-62.
  11. Laurista, J. L. (2012). National Career Assessment Examination (NCAE) performance in Mathematics: It’s predictors and congruence to students’ course (Masters Thesis, Arts in Education Major in Secondary Mathematics), West Visayas State University. Iloilo, Philippines.
  12. Mbuva, J. M. 1. (2011). An examination of student retention and student success in high school, college, and university. Journal of Higher Education Theory & Practice, 11(4), 92-101.
  13. Modebelu, M., & Ogbonna C. (2014). Reform-Based-Instructional method and learning styles on students’ achievement and retention in Mathematics: Administrative Implications. International Journal of Education and Literacy Studies, 2(2), 48-52.
  14. Mutai, C. (2020). Gender differences in Mathematics performance among secondary school students in Bureti Sub - County, Kericho County Kenya (Master of Education Thesis). Kenyatta University. Nairobi, Kenya.
  15. Nneji, S. (2013). Effect of Polya George’s problem-solving model on students’ achievement and retention in Algebra. Journal of Educational and Social Research, 3(6), 41-48.
  16. Ogunkunle, R., & Henrietta, O. (2014). Effect of differentiated instructional strategies on students’ retention in Geometry in FCT senior secondary schools Abuja, Nigeria. Global Journal of Educational Research, 13, 1-7.
  17. Radunzel, J., & Noble, J. (2013). Differential effects on student demographic groups of using ACT® College readiness assessment composite score, ACT benchmarks, and high school grade point average for predicting long-term college success through degree completion (ACT Research Report No. 2012-2). Iowa City, IA: ACT, Inc.
  18. SEI-DOST, & MATHTED. (2011). Framework for Philippine mathematics teacher education. Manila: SEI-DOST & MATHTED.
  19. Sharma, H. (2016). Effectiveness of educomp smart classroom teaching on retention in mathematics at elementary level. International Journal of Multidisciplinary Research and Development, 3(6), 160-164.
  20. Siregar, N. C., Rosli, R., Maat, S. M., & Capraro, M. M. (2020). The Effect of Science, Technology, Engineering and Mathematics (STEM) Program on Students’ Achievement in Mathematics: A Meta-Analysis. International Electronic Journal of Mathematics Education, 15(1), em0549.
  21. Staus, N. L., Falk, J. H., Penuel, W., Dierking, L., Wyld, J., & Bailey, D. (2020). Interested, Disinterested, or Neutral: Exploring STEM Interest Profiles and Pathways in A Low-Income Urban Community. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1853.
  22. Thurston, A., & Topping, K. (2007). Peer Tutoring in Schools: Cognitive Models and Organizational Typography. Journal of Cognitive Education and Psychology, 6(3), 356-372.
  23. Topping, K., Dehkinet, R., Blanch, S., Corcelles, M., & Duran, D. (2013). Paradoxical effects of feedback in international online reciprocal peer tutoring. Computers & Education, 61, 225-231.