Secondary education influences on higher education performance.
This article reports on a mixed methods study that followed a cohort of students through an undergraduate medical programme. It investigated demographic variables (sex, age, race, language, finance, education level, high school and matric point score) as influences on students' assessment marks. The...
| Publicado en: | South African Journal of Higher Education Vol. 28; no. 3; pp. 923 - 940 |
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| Formato: | Artículo |
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Sabinet Online Limited
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=110022387&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 110022387 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 10113487 SG3 jtl: South African Journal of Higher Education issn: 10113487 maglogo: N pubinfo: dt: 2014 vid: 28 iid: 3 pid: 31334 pub: Sabinet Online Limited artinfo: ui: 110022387 ppf: 923 ppct: 17 formats: tig: atl: Secondary education influences on higher education performance. aug: au: Sommerville, T. E. affil: Departments of Anaesthesia/Medical Education, University of KwaZulu-Natal, Durban, South Africa su: Secondary education Higher education Generalized estimating equations Demographic characteristics Educational attainment Differences sug: subj: Secondary education Higher education Generalized estimating equations Demographic characteristics Educational attainment Differences keyword: diversity education higher education secondary education social class ab: This article reports on a mixed methods study that followed a cohort of students through an undergraduate medical programme. It investigated demographic variables (sex, age, race, language, finance, education level, high school and matric point score) as influences on students' assessment marks. The marks were analysed by means of the general linear method (GLM) for within-group differences, and by a generalised estimating equation (GEE) for overall significance. Most variables showed significantly different subgroups on the GLM analysis. However, only high school quintile, previous higher education, the assessments themselves and matric point scores showed overall significance on the GEE analysis. Semi-structured student and staff interviews explored the surprising influence of students' high schools on their assessment marks. The differences seen between students from schools in different quintiles may be attributable to the schools' financial resources. However, the author argues that Coleman's (1966) and Bernstein's (1971) observations of class differences are apposite, and that school quintile stands here as a surrogate for socioeconomic class. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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