User-Driven Techniques for the Design and Evaluation of New Musical Interfaces
Bibliographic record
Abstract
The merits of user-driven design have long been acknowledged in the field of human–computer interaction (HCI): Closely involving target users throughout the lifecyle of a project can vastly improve their experiences with the final system. Thus, it comes as no surprise that a growing number of music technology researchers are beginning to incorporate user-driven techniques into their work, particularly as a means of evaluating their designs from the perspectives of their intended users. Many, however, have faced the limitations that arise from applying the task-based, quantitative techniques typically encountered in classical HCI research to the evaluation of nonutilitarian applications. The nature of musical performance requires that designers reevaluate their definitions of user “goals,” “tasks,” and “needs.” Furthermore, within the context of performance, the importance of creativity and enjoyment naturally supersedes that of efficiency, yet these concepts are more difficult to evaluate or quantify accurately. To address these challenges, this article contributes a set of key principles for the user-driven design and evaluation of novel interactive musical systems, along with a survey of evaluation techniques offered by new directions in HCI, ludology, interactive arts, and social-science research. Our goal is to help lay the foundation for designers of new musical interfaces to begin developing and customizing their own methodologies for measuring, in a concrete and systematic fashion, those critical aspects of the user experience that are often considered too nebulous for assessment.
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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 | 0.138 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".