The Impact of a Unit in Algorithmic Composition Software on Music Education Majors' Attitudes Towards Technology and Computer Self-efficacy

 

Camilo I. Leal, University of Florida

cleal@ufl.edu

 

Technology has become a tool for teaching and learning, a tool for listening to music, practicing, recording and notating, and performing and creating. Studies have suggested, however, that music educators’ use of technology concentrates on administrative resources and Computer Assisted Instruction (CAI) (Dorfman, 2008; Jassmann, 2004; Meltzer, 2001; Ohlenbusch, 2001; Reese & Rimington, 2000). These studies suggest that, in terms of musical instruction, the use of software focuses primarily on auxiliary activities such as CD burning and music notation, and considerably less usage of musical production software such as MIDI sequencers and Digital Audio Workstations (DAW) (Jassmann, 2004; Meltzer, 2001; Reese & Rimington, 2000).

 

One type of software that has been particularly neglected by music education researchers is that specific for algorithmic composition. Algorithmic Composition Software (ACS) are powerful audio and midi capable software which allow users to manipulate several parameters using a variety of data types, such as simple math, as input. Moreover, ACS with a graphical user interface present alternative ways of creating and producing music (less linear than most DAW’s and MIDI sequencers) and give teachers the power to create and customize their own applications.

 

Studies in music educator’s use of music technology suggest that professional development can have a positive effect on teacher’s comfort with technology, which results in an increased use and a heightened self-efficacy (Bauer, Reese, & McAllister, 2003; Dammers, 2009; Ubovich, 2015). These studies, however, have not explored the impact of the use of and training in specific technologies on the overall attitudes towards technology and perceptions of computer self-efficacy. Attitudes towards technology and perceptions of computer self-efficacy have been associated with increased use of technology (e.g. Beas & Salanova, 2006; Conrad & Munro, 2008; Celik & Yesilyurt, 2013; Torkzadeh, Pflughoeft, & Laura Hall, 1999).

 

The purpose of this study is to explore the impact of attending an online unit in the use of Algorithmic Composition Software (ACS) on music education majors’ attitudes towards technology and perceived computer self-efficacy. The study will consist of a parallel convergent mixed methods design, which will involve a treatment in the form of an online unit in the use of Max/MSP (an algorithmic composition software) and the administration of a survey (pre-test and post-tests) to collect both quantitative and qualitative data. The quantitative portion will measure (a) attitudes towards technology and (b) computer self-efficacy using and adaptation of the Computer and Technology Use Scale (Conrad & Munro, 2008) and the effect, if any, of the treatment on the scores for both constructs. In addition, an ANOVA will be conducted using attitudes as the independent variable. The qualitative portion will include open ended questions regarding the perceived affordances and constraints of using ACS in music education, which will be analyzed in relationship to the two constructs studied.

 

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