Using Markov chains in modeling student progress through Philippine secondary education
List of Authors
  • Lance Calvin Gamboa , Patricia Angela Abu

Keyword
  • student progress, student withdrawal, education, K-12, absorbing Markov chains, state-based modeling

Abstract
  • In 2011, the Philippine government started transitioning towards a 13-year basic education model which added two additional years of high school to the previous model. This policy change necessitated an assessment of how student progress across the educational system has been affected by the shift in curriculum. One method applied towards similar goals is the use of absorbing Markov chains to better understand the attributes of student flow within an education system. To date, no published work seems to have explored the potential of Markov analysis in the context of the Philippine educational landscape. This study thus used enrollment data from the past decade to construct absorbing Markov models that compared student movement through the Philippine secondary education system before and after the execution of the K-12 program. Results show that although student withdrawals from high school decreased under the new education program, there was a noticeable increase in the number of students repeating grade levels. This insight points to a need for the Philippine Department of Education to reinforce efforts in helping students meet academic expectations and assisting them in finishing school on time.

Reference
  • 1. A. F. Mashat, A. H. Ragab, and A. M. Khedra. “Decision Support system based Markov model for performance Evaluation of students flow in FCIT-KAU,” in Proceedings of the International Conference on Convergence Information Technology, 2012, pp. 409–414.

    2. A. Brezavšček, M. P. Bach, and A. Baggia. “Markov analysis of students’ performance and academic progress in higher education,” Organizacija, vol. 50, pp. 83-95, 2017.

    3. Philippine Statistics Authority. “Glossary of official definitions for statistical purposes annex: Education statistics,” 2006. [Online]. Available: https://psa.gov.ph/sites/default/files/attachments/Annex_BR-14-20 06-01.pdf

    4. United Nations Educational, Scientific, and Cultural Organization. “Education for all (EFA) 2000 assessment: Technical guidelines,” 1998. Accessed on: April 23, 2022. [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000113746

    5. A. Brezavšček and A. Baggia. “Analysis of students’ flow in higher education study programmes using discrete homogeneous Markov chains,” in Proc. of the 13th Symp. on Op. Res. in Slovenia, 2015.

    6. F. Crippa, M. Mazzoleni, and M. Zenga, “Departures from the formal of actual students' university careers: An application of non- homogeneous Fuzzy Markov chains,” Journal of Applied Statistics, vol. 43, no. 1, pp. 16–30, 2015.

    7. M. G. Nicholls, “Assessing the progress and the underlying nature of the flows of doctoral and master degree candidates using absorbing Markov chains,” Higher Education, vol. 53, no. 6, pp. 769–790, 2007.

    8. J. A. Gonzales-Campos, C. M. Carvajal-Muquillaza, J. E. Aspeé- Chacón. “Modeling of university dropout using Markov chains,” Uniciencia, vol. 34, no. 1, 2020.

    9. Philippine Statistics Authority. “Secondary Completion Rate, Region and Sex.” Accessed on: April 23, 2022. [Online]. Available: https://openstat.psa.gov.ph/Metadata/3E3D4070

    10. D. S. Maligalig, R. B. Caoli-Rodriguez, A. Martinez, and S. Cuevas. “Education outcomes in the Philippines,” in Asian Development Bank Economics Working Paper Series, May 2010.

    11. E. Jimenez and Y. Sawada, “Public for private: The relationship between public and private school enrollment in the Philippines,” Economics of Education Review, vol. 20, no. 4, pp. 389–399, 2001.

    12. Department of Education. “Discussion paper on the Enhanced K+12 Basic Education Program,” October 2010. [Online]. Available: https://swedta.files.wordpress.com/2011/09/k12new.pdf

    13. Department of Education. "Briefer on the Enhanced K to 12 Basic Education Program,” in Official Gazette of the Philippines, 2010. [Online]. Available: https://web.archive.org/web/20101112125854/http://www.gov.ph/2010/11/02/briefer-on-the-enhanced-k12-basic-education-program/

    14. H. C. Tijms, Technometrics, vol. 47. Chichester: John Wiley & Sons, 2003.

    15. M. C. Ledwith. “An application of absorbing Markov chains to the assessment of education attainment rates within Air Force Materiel Command civilian personnel,” unpublished thesis, 2019.

    16. R. Fewster. “Course notes on STATS 325: Stochastic processes,” in The University of Auckland: Department of Statistics, 2014. [Online]. Available: https://www.stat.auckland.ac.nz/~fewster/325/notes/325book.pdf

    17. J. L. Romeu. “A Markov model to study college re-opening under Covid-19,” in Statistical, Simulation and Optimization Modeling of Environmental Problems, 2020. [Online]. Available: https://web.cortland.edu/matresearch/MarkovForCollegeUnderCovid19.pdf

    18. G. Burke. “A study in the economics of education with particular reference to the supply of secondary teachers for government schools in Victoria,” unpublished PhD thesis, 1972.

    19. M. Symeonaki and A. Kalamatianou. “Markov systems with fuzzy states for describing students’ educational progress in Greek universities,” Isi, vol. 1, pp. 5956–5961, 2011.

    20. R. A. Adeleke, K. A. Oguntuase, and R. E. Ogunsakin “Application of Markov chain to the assessment of students’ admission and academic performance in Ekiti State University,” International Journal of Scientific & Technology Research, vol. 3, no. 7, pp. 349-357, 2014.

    21. R. Rahim, H. Ibrahim, M. M. Kasim, and F. A. Adnan. “Projection model of postgraduate student flow,” Applied Mathematics and Information Sciences, vol. 7, no. 2, pp. 383–387, 2013.