'Deep understanding' for anti‐racist school transformation: School leaders' professional development in the context of Black Lives Matter.
Published In: Curriculum Journal, 2023, v. 34, n. 1. P. 156 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Walker, Sharon; Bennett, Ian; Kettory, Pavenjit; Pike, Clare; Walker, Lee 3 of 3
Abstract
In June 2020, the world witnessed an upsurge in Black Lives Matter (BLM) demonstrations following the murder of George Floyd, an African American, by a White American police officer. The international response called for the global community to reassess the value of black lives blighted by racist social systems. The mass sentiment acted as a catalyst for educational institutions, including those in the UK, to mount a response. It is in this context that a School Partnership Group representing primary and secondary schools in East London embarked on developing a workshop series for the professional development of school leaders. The sessions were aimed at school transformation through anti‐racist educational approaches. In this article, we present a discussion of the workshop series held in the academic year 2020–2021, which brought school leaders together in a reflective community of practice. Drawing on data from focus group conversations carried out following the end of the series, this paper argues for school leaders' professional development that prioritises 'deep understanding' supported by reflective communities of practice as a pre‐requisite for effective anti‐racist practice and sustained school transformation. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Curriculum Journal. 2023/03, Vol. 34, Issue 1, p156
- Document Type:Article
- Subject Area:Biography
- Publication Date:2023
- ISSN:0958-5176
- DOI:10.1002/curj.189
- Accession Number:161311205
- Copyright Statement:Copyright of Curriculum Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.