Exposing the patterns of statistical blindness:centring Indigenous standpoints on student identity, motivation, and future aspirations
This article engages with an Indigenous Quantitative Methodological Framework to examine links between a positive sense of cultural identity, future aspirations, and academic motivational tendencies. Utilising a sample of Aboriginal, non-Aboriginal and First Generation (Migrant) Australian students...
Published in: | Australian Journal of Education |
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Main Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2017
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Subjects: | |
Online Access: | https://researchers.mq.edu.au/en/publications/cc237dd1-04e6-44a0-95c5-cb3e609c860c https://doi.org/10.1177/0004944117731360 http://www.scopus.com/inward/record.url?scp=85035813986&partnerID=8YFLogxK |
Summary: | This article engages with an Indigenous Quantitative Methodological Framework to examine links between a positive sense of cultural identity, future aspirations, and academic motivational tendencies. Utilising a sample of Aboriginal, non-Aboriginal and First Generation (Migrant) Australian students in years 7–10, results showed strong psychometric properties across the three groups for the measures utilised. Whilst few differences were identified between the First Generation and non-Aboriginal Australian students, Aboriginal students consistently had lower future aspirations and less adaptive motivational tendencies than the two other student groups. Importantly though, Aboriginal students held a stronger sense of cultural identity. Key links between motivation and cultural identity were identified, and both were associated with stronger educational and life aspirations. The implications suggest that researchers and teachers need to recognise the importance of cultural identity as a positive driver for schooling motivation and future aspirations, and that First Nations theory and research should be engaged to override the erasing effects of Western epistemological standpoints when utilising statistical methods. |
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