This arts-based research thesis sought to understand two children with special needs, through their musical offerings within the context of their music therapy sessions. The process of understanding the children came through firstly listening to and extracting meaningful musical data from recordings of their sessions. This data included both actual excerpts of the musical interplays between the child and music therapist (myself), and more broadly, underlying themes drawn from the recorded session material. The data was then creatively ‘melded’, resulting in two original instrumental works, herein referred to as 'songs'. Before, during and after writing the songs, rigorous analyses were undertaken utilising both a formal approach, via the use of a contextual question framework, and two creative approaches: free-form narration and data-led imagery. The contextual question framework, involving the repetitive use of two key questions - where? and why? - sought to understand the data’s context, its purpose for inclusion, and its influence on the respective song. One of the creative approaches, that of free-form narration, sought to, rather than analysing the song through formal structures, instead ‘tell the song’s story’, narratively conveying the experience of being with the child in his music. The other creative approach, that of data-led imagery, involved creating images during and after being ‘immersed’ in the musical data, employing instinctive or subconscious means to further develop the therapist’s understanding of the child’s musical offerings, and moreover, the child himself. Upon completion of the two songs, it was discovered that clinical themes present from the sessions strongly influenced the song creation process. Results also strongly supported the validity of arts-based research as a viable means of analysing music created with children with special needs.
Identifer | oai:union.ndltd.org:ADTP/288092 |
Date | January 2008 |
Creators | Cammell, Shane |
Source Sets | Australiasian Digital Theses Program |
Language | English |
Detected Language | English |
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