利用马尔可夫链模型探讨数学教育中毕业熟练程度的性别差异：对尼日利亚经济增长的影响
Exploring Gender Differences in Graduation Proficiency in Mathematics Education Using a Markov Chain Model: Implications for Economic Growth in Nigeria
摘要：
本研究采用事后研究设计，探讨数学教育专业应届毕业生学业成就性别差异的变动情况。毕业统计数据，共1106名毕业生的数学教育（923名男性和183名女性），从中北部尼日利亚的一所大学被用来设计一个离散时间马尔可夫链模型的运动的差异（d）在毕业熟练度从一个值域的值（国家）到其他。附加拟合优度检验（$χ$ 2 = 1.731，p = 0.99924）和t检验（t = 0.4055，p = 0.6852）揭示了d在研究中使用的12个毕业周期中基本保持不变，数学教育专业男女毕业生在毕业时的学业成绩差异是由哪些因素决定的多年来，周期基础基本保持不变。对该模型的进一步分析预测，在今后15个毕业周期内，观察到的性别差距将消失（第15页）。这项研究的结果特别强调了这样一个事实，即数学教育的女毕业生在推动尼日利亚经济教育分部门内外的增值服务方面与男毕业生一样熟练。根据这项研究的结果，有人建议，今后的工作可考虑深入调查可能影响模型中给出的具体概率的参数的敏感性。
英文摘要：
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 ($χ$ 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.
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