On Application of Absorbing Markov Chain Model to Determine Students’ Academic Progress: A Case Study of Mathematics Department, Ekiti State University, Nigeria

Authors

  • Kayode James ADEBAYO
  • Isaac Olabisi ADISA
  • Adejoke Olumide DELE-ROTIMI
  • Mathew Folorunsho AKINMUYISE
  • Samuel Olukayode AYINDE
  • Adigun Oyinloye ADEDEJI
  • Temitayo Emmanuel OLAOSEBIKAN

DOI:

https://doi.org/10.59573/emsj.8(3).2024.45

Keywords:

Transition Probability Matrix, Markov Chain, Fundamental Matrix, Probability of Absorption

Abstract

This paper investigates students’ enrolment pattern and their academic performance as they progress from the point of admission to the point of graduation focusing on the Mathematics Department, Ekiti State University, Ado Ekiti in Nigeria. In this research work the Transition Probability Matrix (TPM) was formulated over sequent periods of academic calendars and the probabilities of absorption that include both the Graduating Students and Withdrawn Students were captured. The paper is geared towards the obtainment of the fundamental matrix that will be employed to determine the anticipated length of stay of students in the institution before graduation. The analysis of the requisites was executed by employing the Markov chain model to student populations at different levels in the University including the freshmen, the sophomore transfers, the junior transfers, and the finalists. This was carried out to estimate the differences in the cohorts' behavior over the entire academic period. Based on the results and findings after the data smoothening, future predictions were made on the enrolment into the department as well as the academic performance of the students. Hinging on the analysis includes some prescriptions such as an intentional increase in the rate of retention of the freshmen and the boost given to the sophomores to take on the area with comparative advantage rather than leaving the institution. Other post-analysis discussions and necessary recommendations are included without overlooking the drawbacks of the results from the analysis.

References

Adam, R. Y. (2015). An Application of Markov Modeling to the Student Flow in Higher Education in Sudan. International Journal of Science and Research, 4(2), 49-54.

Adebanji, B., Ogunlade, B. O., Adetan, O., Adebayo, K. J., Fasina, E. T., & Akindele, O. D., (2021). Assessing the Impacts of Entrepreneurship Skills on Nigerian Engineering Students, European Journal of Education and Pedagogy, 2(4), 01-05.

Adeleke, R. A., Oguntuase, K. A., & Ogunsakin, R. E. (2014). Application of Markov Chain to the Assessment of Students’ Admission and Academic Performance in Ekiti State University. International Journal of Scientific & Technology Research, 3(7), 349-357.

Al-Awadhi, S. A. & Ahmed, M. A. (2002). Logistic models and a Markovian analysis for student attrition. Kuwait Journal of Science and Engineering, 29(2), 25-40.

Al-Awadhi, S. A. & Konsowa, M. (2007). An application of absorbing Markov analysis to the student flow in an academic institution. Kuwait J. Sci. Eng., 34(2A), 77-89.

Auwalu, A., Mohammed, L. B., & Saliu, A. (2013). Application of Finite Markov Chain to a Model of Schooling. Journal of Education and Practice, 4(17), 1-10.

Gani, J. (1963). Formulae for projecting enrolments and degrees awarded in universities. Journal of the Royal Statistical Society: Series A (General), 126(3), 400-409.

Hlavatý, R. & Dömeová, L. (2014). Students' Progress throughout Examination Process as a Markov Chain. International Education Studies, 7(12), 20-29.

Konsowa, M. & Al-Awadhi, F. (2010). Hitting times and the speed of random walks on trees. Proc. 2nd International Conference on Mathematical Sciences, No. Paper ID, 241.

Mashat, A. F., Ragab, A. H., & Khedra, A. M. (2012). Decision support system based Markov model for performance evaluation of students’ flow in FCITKAU. Proceedings of the ICCIT, 409-414.

Moody, V. R. & DuClouy, K. K. (2014). Application of Markov Chains to Analyze and Predict the Mathematical Achievement Gap between African American and White American Students. Journal of Applied & Computational Mathematics, 3, 161.

Oke, M. O., Ogunlade, T. O., Fadugba, S. E., & Adebayo, K. J., (2020). Queueing Model for Optimizing Vehicular Traffic Flow at a Signalized Intersection in a Developing Urban Center, International Journal of Advanced Science and Technology, 29(7), 13926-13937.

Poland, D. & Scheraga, H. A. (1970). Theory of helix-coil transitions in biopolymers.

Roberts, F. (1976). Discrete Mathematical Models. Practice-Hall, inc.

Shah, C., & Burke, G. (1999). An undergraduate student flow model: Australian higher education. Higher Education, 37(4), 359-375.

Symeonaki, M. & Kalamatianou, A. (2011). Markov Systems with Fuzzy States for Describing Students’ Educational Progress in Greek Universities. Isi, 1, 5956-5961.

Tijms, H. C., (2003). A first course in stochastic models. John Wiley and Sons.

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Published

2024-08-10

Issue

Vol. 8 No. 3 (2024)

Section

Articles

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