Exploring Gender Differences in Graduation Proficiency in Mathematics Education Using a Markov Chain Model: Implications for Economic Growth in Nigeria

TL;DR

This study models gender differences in mathematics education graduation proficiency in Nigeria using a Markov chain, finding the gender gap remains stable but is predicted to close in 15 cycles, highlighting female graduates' economic potential.

Contribution

It introduces a Markov chain model to analyze gender achievement differences over multiple graduation cycles in Nigerian mathematics education.

Findings

Gender gap in proficiency remains stable over 12 cycles

Model predicts closure of gender gap in 15 cycles

Female graduates are as proficient as males in contributing to the economy

Abstract

This study employs an ex-post facto research design to explore the fluctuations of gender difference in academic achievement among graduating students of mathematics education. Graduation statistics for a total of 1106 graduating students of mathematics education (923 males and 183 females) from a University in North Central Nigeria were used to design a discrete-time Markov chain model for the movement of the difference (d) in graduating proficiency from one range of values (states) to the other. Additional goodness of fit test ($\chi$ 2 = 1.731, p = 0.99924) and t-test (t = 0.4055, p = 0.6852) unveiled that d has stayed much the same over the 12 graduation cycles used in the study, and that whatever factors determine the difference in academic achievement between male and female graduating students of mathematics education on a graduation cycle basis have remained much the same over the years. Further analysis of the model predicted the closure of the observed gender gap in the next 15 graduation cycles (p 15). The results of this study has specifically highlighted the fact that female graduates of mathematics education are as proficient as their male counterparts in driving value added services in and beyond the education sub-sector of the Nigerian economy. Based on the findings of this study, it was recommended that future work may consider an in-depth investigation of the sensitivity of parameters that may have influenced specific probabilities given in the model.