Towards a Reflection in Creative Experience Questionnaire
Open accessAbstract
Reflection is underexplored in Creativity Support Tool (CST) research, partly due to its ambiguous nature. We suggest that researchers could benefit from a measure of a CST’s capacity to support reflection. To this end, we detail the first stages of development of the Reflection in Creative Experience Questionnaire (RiCE) – a lightweight questionnaire for differentiating between creative user experiences which exhibit more or less moments of reflection. We develop RiCE through i) an expert review of questionnaire items (n=10) and ii) an exploratory factor analysis (n=300) of the reviewed items. We also present a user study testing RiCE (n=58) across two time points (one week apart) with novel interfaces designed for creative writing and music making. Although we do not confirm validity, we identify four factors for RiCE which we suggest are interpretable in a conceptually meaningful way. Our formative studies contribute towards supporting future explorations on reflection with CSTs.
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RiCE is designed to measure post-hoc how much of a certain type of reflection a person self-reports to have experienced, based on a set of statements scored on ordinal scales.
“RiCE is designed to measure post-hoc how much of a certain type of reflection a person self-reports to have experienced, based on a set of statements scored on ordinal scales.”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
Reflection-on-process is lower for the open-source DAW scores (mean=7.6) than the study's data (mean=8.1).
“Reflection-on-process is also lower for the open-source DAW scores (mean=7.6)than our data (mean=8.1)”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
Reflection-through-experimentation is higher for the open-source DAW scores (mean=7.1) than the study's data (mean=6.6).
“whilst reflection-through-experimentation is higher for the open-source DAW scores (mean=7.1)than our data (mean=6.6).”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
Our participants have the same overall RiCE score as the open-source data (mean=6.8).
“Overall, our participants have the same RiCE score as the open-source data (mean=6.8).”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
The DAWs scored in the RiCE survey [44] include Cubase (n=2), Garageband (n=2), Ableton (n=2), Logic and FL Studio.
“DAWs scored in the RiCE survey[44]include Cubase (n=2), Garageband (n=2), Ableton (n=2), Logic and FL Studio.”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
Our participants seem to show higher reflection-on-process scores than the open-source data, including for the subset of RiCE scores for DAWs.
“Our participants also seem to show higher reflection-on-process scores, including for the subset of RiCE scores for DAWs.”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
The first-person accounts helped the paper speculate on explanations for patterns observed from the RiCE questionnaire measures, and brought complementary insights to the findings from the interviews.
“The first-person accounts helped us in speculating on explanations for patterns we observed from the RiCE [44] questionnaire measures, and brought complementary insights to the findings from our interviews.”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
The citations to researchers' inspirations in the first-person accounts are not captured by the RiCE questionnaire nor other questionnaire measures typically used in creative HCI research.
“For example, the citations to researcher's inspirations in the first-person accounts are not captured by the RiCE questionnaire [44], nor other questionnaire measures typically used in creative HCI research [25,59].”
From Reflection Across AI-based Music Composition (2024) · cites this paper as [44]
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Reflection Across AI-based Music Composition
Ford, C., Noel-Hirst, A., Cardinale, S., Loth, J., Sarmento, P., Wilson, E., Wolstanholme, L., Worrall, K., & Bryan-Kinns, N. (2024) · Creativity and Cognition · doi:10.1145/3635636.3656185 · Cited by 22 · see citing works
Self-citation
- Authors
- Ford, C., & Bryan-Kinns, N.
- Affiliations
- Queen Mary University of London (GB)
- Queen Mary University of London, United Kingdom
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-16
- OpenAlex
- W4366549966
- Cited by
- 15
- References
- 51
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 4 · 2023: 1
- FWCI
- 3.48
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- University of the Arts London Research Online (University of the Arts London) (acceptedVersion, cc-by)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- UK Research and Innovation (EP/S022694/1)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- reflection, creativity support tools, questionnaire development, factor structure, expert review, exploratory factor analysis
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