Modelling School Factors and Performance in Mathematics and Science in Kenyan Secondary Schools Using Canonical Correlation Analysis

Jeremiah M Mucunu
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Abstract

The level of performance and participation in Science, Technology, Engineering and Mathematics (STEM) career subjects remains low in Kenya despite STEM’s critical role in economic development. Numerous factors contribute to students’ academic achievement in STEM education. This study focusses on modelling school factors that affect the performance in mathematics and science in Kenyan secondary schools using Canonical Correlation Analysis (CCA). The objectives of the study include determining: the magnitude of the relationship between school factors and performance in STEM education, the most influential subject in describing the level of STEM education, the most contributing school factor to STEM education and a model to predict performance in STEM education given school factors. This research utilised data from 9,834 candidates of year 2015 Kenya Certificate of Secondary Education (KCSE) from 77 public secondary schools in Nairobi County. CCA is a multivariate data analysis technique that seeks to establish whether two sets of variables, predictor and criterion, are independent of each other. Given that the two sets of variables are dependent, CCA is able to represent a relationship between the sets of variables rather than individual variables. From the 2015 KCSE data, CCA revealed that school factors significantly correlate with the level of performance in STEM education. Based on standardised canonical coefficients and canonical loadings, the subjects that mainly influence the level of performance in STEM education were found to be mathematics and physics. Further assessment of the canonical cross loadings from the two variate pairs revealed that the proportion of students with mean grades of C+ and above and the proportions of students taking biology and physics were found to contribute very highly to the level of performance in STEM education. The study recommends increased staffing in physics due to the fact that physics is an optional subject yet it has comparatively larger loadings than biology and chemistry which have higher levels of participation. Also, the study recommends that further studies should be done to establish the relationship between individual factors and participation and performance in STEM career subjects. iv
使用典型相关分析模拟肯尼亚中学数学和科学中的学校因素和表现
在肯尼亚,尽管科学、技术、工程和数学(STEM)职业科目在经济发展中发挥着关键作用,但其表现和参与水平仍然很低。许多因素影响着学生在STEM教育中的学业成绩。本研究的重点是利用典型相关分析(CCA)对影响肯尼亚中学数学和科学成绩的学校因素进行建模。该研究的目标包括确定:学校因素与STEM教育绩效之间关系的大小,描述STEM教育水平的最具影响力的主题,对STEM教育贡献最大的学校因素以及在给定学校因素的情况下预测STEM教育绩效的模型。这项研究利用了来自内罗毕县77所公立中学的9834名2015年肯尼亚中等教育证书(KCSE)考生的数据。CCA是一种多变量数据分析技术,旨在确定两组变量(预测器和标准)是否相互独立。考虑到这两组变量是相互依赖的,CCA能够表示这两组变量之间的关系,而不是单个变量。根据2015年的KCSE数据,CCA显示学校因素与STEM教育的表现水平显著相关。基于标准化典型系数和典型负荷,发现主要影响STEM教育表现水平的科目是数学和物理。对两个变量对的典型交叉负荷的进一步评估显示,平均成绩为C+及以上的学生比例和学习生物和物理的学生比例被发现对STEM教育的表现水平贡献很大。该研究建议增加物理专业的人员配备,因为物理是一门选修科目,但它比参与程度更高的生物和化学的负荷相对更大。此外,该研究建议,应该进行进一步的研究,以确定个人因素与STEM职业科目的参与和表现之间的关系。4
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