{"title":"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","authors":"Jeremiah M Mucunu","doi":"10.12785/ijcts/050201","DOIUrl":null,"url":null,"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","PeriodicalId":373764,"journal":{"name":"International Journal of Computational and Theoretical Statistics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelling School Factors and Performance in Mathematics and Science in Kenyan Secondary Schools Using Canonical Correlation Analysis\",\"authors\":\"Jeremiah M Mucunu\",\"doi\":\"10.12785/ijcts/050201\",\"DOIUrl\":null,\"url\":null,\"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\",\"PeriodicalId\":373764,\"journal\":{\"name\":\"International Journal of Computational and Theoretical Statistics\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computational and Theoretical Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12785/ijcts/050201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational and Theoretical Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12785/ijcts/050201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelling School Factors and Performance in Mathematics and Science in Kenyan Secondary Schools Using Canonical Correlation Analysis
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