Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22237
Title: Opening the doors of possibility for gifted/high-ability children with learning difficulties: Preliminary assessment strategies for primary school teachers
Contributor(s): Haines, MaryAnne  (author)orcid ; Cornish, Linley  (supervisor)orcid ; Bannister-Tyrrell, Michelle  (supervisor)orcid 
Publication Date: 2017-12-13
Handle Link: https://hdl.handle.net/1959.11/22237
Related Research Outputs: https://hdl.handle.net/1959.11/23098
Abstract/Context: Dataset contains questionnaires, interview recordings, and interview transcriptions relating to a study of gifted/high-ability children with learning difficulties. The data was collected to offer preliminary assessment strategies for primary school teachers.
Publication Type: Dataset
Fields of Research (FOR): 139999 Education not elsewhere classified
130312 Special Education and Disability
130105 Primary Education (excl. Maori)
Fields of Research (FoR) 2020: 399999 Other education not elsewhere classified
390407 inclusive education
390304 Primary education
Socio-Economic Objective (SEO): 930202 Teacher and Instructor Development
Socio-Economic Objective (SEO) 2020: 160303 Teacher and instructor development
Keywords: Gifted students
Learning difficulties
High-ability students
Twice-exceptionality
Location: Australia, Sydney
Format: 7 Adobe .pdf
7 Audio .mp3
Access rights: Mediated
Open Access Embargo: 2017-12-13
HERDC Category Description: X Dataset
Description: Access to the Thesis for this Dataset provided at the following link: https://hdl.handle.net/1959.11/23098
Project: Preliminary assessment strategies for primary school teachers
Dataset Managed By: School of Education
Rights Holder: University of New England
Rights Statement: Contact the Chief Investigator to request acccess.
Dataset Stored at: Univeristy of New England
Primary Contact Details: MaryAnne Haines - mahaines@bigpond.net.au
Dataset Custodian Details: MaryAnne Haines - mahaines@bigpond.net.au
Appears in Collections:Dataset

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