Semi-structured discussions were held with the participants at the end of the session to gather data about their perceptions and experiences. Discussions were video and audio recorded. A set of questions were generated to focus on general preferences, perceived differences between the speakers and headphones, use of the Personal and Public channels, awareness of each-other’s activities, roles and working strategies and spatial use of the shared on-screen workspace. Partici- pants were also invited to raise any other issues, talk about the experience in their own words, and ask their own questions. Analysis of interview transcripts used Grounded Theory methods, as
Feature Description Creations Creating a module Deletions Deleting a module
Cloning Cloning a module using the clone button Snapshots Taking a group snapshot of the music
Text chat Number of text chat events
Editing (raw) Modifying a module control (slider, button, etc.,) Patching to Public Connecting a module to the Public output Patching to Personal Connecting a module to a Personal output
Movement Movement of modules Number of Snapshots
Tempo changes
Table 6.2: Summary of log file features studied
adopted by many others for the study of digital musical interactions, CSCW and HCI (Dow et al., 2008, Laney et al., 2010, Luther and Bruckman, 2008, Michael Gurevich and Marquez-Borbon, 2010, Muller and Kogan, 2010, Thom-Santelli et al., 2009).
6.7 Participants and Recruitment
Thirty individuals were recruited and organised into ten groups of three people. These groups were scheduled to attend experiment sessions at Queen Mary University of London. Partici- pants received financial compensation of £10 for taking part. All participants were recruited via mailing lists related to art, electronic music, sonic arts, music technology and academic research concerning sound and music computing. The recruitment e-mail stated that a new music appli- cation was being developed at Queen Mary University of London, and that for testing purposes, the project required ‘people with an interest in creating music, for instance composers, musi- cians, DJs, and students of Music, Music Technology or related fields’. The message stressed that people would work in a group with two other people, and that they did not need to be able to play an instrument or have formal musical training to take part. The message invited people to make contact via e-mail if interested. In line with ethical procedures for the recruitment of human subjects, no individuals were directly approached or asked to participate.
Over a period of several months approximately eighty people replied to the recruitment e- mails, either expressing an interest in taking part, or asking for additional information. Each respondent was contacted via e-mail, with answers to any questions they had about the research. Respondents were then invited via e-mail to take part in an experiment session. Electronic copies
of the information sheet and consent form were sent to participants prior to the experiment ses- sion, and e-mail reminders were sent close to the date of the session. Participants were informed they did not need to bring instruments or equipment with them on the day of the session.
Demographic information for each participant was acquired via a computer administered pre- test questionnaire at the start of the experiment session. Participants were able to skip questions if they so wished. 25 of the 30 participants completed the questionnaire in full, while five partici- pants omitted details about age and/or occupation. 19 participants (68%) were male (based on 29 responses). The mean age of participants (based on 25 responses) was 33 years. 20 participants (69%) could play a musical instrument. 6 participants ( 22%) classified themselves as beginner level in their musical proficiency, 11 participants (40%) classified themselves as of ‘intermediate’ level, 5 participants (18%) classified themselves as ‘semi-professional’ and 5 participants (18%) classified themselves as ‘professional’ level. 25 participants (86%) had previously composed songs on their own. 4 participants (16%) had composed 2-5 pieces, and 21 participants (84%) had composed more than 10 pieces. 22 participants (75%) had previously composed songs with others. 2 participants (9%) had composed 1 piece with others. 4 participants (18%) had com- posed 2-5 pieces with others, 3 participants (14%) had composed 5-10 pieces with others, and 13 participants (59%) had composed more than 10 pieces with others.
One participant (3%) described their level of computer literacy as ‘beginner’. 17 participants (58%) identified their level of computer literacy as ‘intermediate’, and 11 participants (37%) identified as ‘expert’. 28 participants (97%) had previously used an Instant Messenger appli- cation to communicate online, and one participant had not used an Instant Messenger (3%). 8 participants (28%) had not used collaborative software before. 7 participants (25%) had played online multi-player computer games, 4 participants (14%) had used collaborative document ed- itors, 2 participants (7%) had used collaborative writing software, 14 participants (50%) had previously used some form of collaborative music making software, one participant (4%) had used another form of collaborative software (SVN)4.
Information about musical preference was collected by asking participants to select from a multiple choice list of sixteen musical genres. The most frequently selected genre of music was ‘Jazz’ (22 participants) closely followed by ‘Electronic’ (21 participants). The least pop- ular genres were ‘Metal’ (7 participants), ‘Country’ (8 participants) and ‘Rap’ (9 participants). The mean number of genres selected was 7.37, indicating that most participants appreciated a
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range of different musical styles. The Spearman rank-order correlation coefficient indicated no correlation between age and musical tolerance (n=24, rs=-0.0309, t=-0.15, df=2, p1=0.441066
p2=0.882131). In summary, whilst not all participants were instrumentalists, there was a high
level of musicality and musical experience within the participant sample, as well as a high level of computer literacy.
It might be expected that people interested in testing new computer music software would appreciate electronic music, yet it is also interesting that jazz was such a popular genre within the participants for this study. One possibilities is that the term ‘jazz’ is too broad and generic to reflect any specific style, while another explanation is that people willing to work with other musicians in an experimental musical context may also be inclined to enjoy and/or participate in music such as jazz, which frequently incorporates aspects of improvisation. Although data on musical preferences was collected by the pre-test questionnaire, this information was not factored into the analysis.
Figure 6.4: Participant Musical Genre Preferences