User experience with digital musical instruments: a transferable method for longitudinal evaluation
Bibliographic record
Abstract
We present the development and demonstration of a transferable method for studying user experience (UX) with digital musical instruments (DMIs) over time. We introduce DMIs, music interaction, stakeholders, and observable experiential aspects of the user-instrument relationship (UIR), grounding the development of our method in theoretical frameworks from human–computer interaction and music technology. We discuss structured evaluation strategies for studying evolving experiential components of the UIR over time, noting the limitations of current approaches. We describe the development and structure of our method before reporting on the initial execution of the method in a limited context. Using a small sample of individuals with diverse musical backgrounds and a compressed time period, we demonstrate how the method can be used to collect rich qualitative data on dynamic aspects of the UIR with an unfamiliar DMI. Results from this initial demonstration suggest that the method is able to capture comparable experiential data from different perspectives and that participants’ backgrounds played a central role in their emotional and cognitive experience. We reflect on the limitations and successes of the demonstration, and offer specific suggestions for expanding the method in future, to more widely assess its transferability to different DMIs, participants, and real-world musical contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".