You say tomato, I say tomato, let's <i>not</i> call the whole thing off: the challenge of user experience design in distributed learning environments
Bibliographic record
Abstract
Purpose The purpose of this paper is to chronicle new user experience (UX) design approaches being pioneered in an international, multi‐institution, multi‐sector, cross‐project initiative called the Fluid Project, covering the strengths and shortcomings of these approaches and the lessons learned about design and development in distributed communities. Design/methodology/approach Open source and community source software development projects have not fulfilled their promise of innovation and natively optimized tools and applications in large part due to a lack of integrated UX design and development processes. Fluid has developed a UX approach that aims to address the need to accommodate the huge diversity of users and contexts in academic communities as well as the critical need to improve the user experience. Findings It has been found that the Fluid approach challenges common or traditional notions integral to teaching in higher education, software design, user interaction design methods, usability research and accessibility strategies. It proposes greater individual control over the UX than most users may be ready to assume despite obvious benefits. An unexpected UX challenge is creating tools and applications that prompt and support users in configuring their systems to their personal needs and contexts. Originality/value Fluid has designed and prototyped new UX design methods, pedagogical practices, and usability and accessibility approaches to suit the context of distributed academic communities and open source development, while at the same time producing a UX system of benefit to the mashup or integration of any set of disparate tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".