Co-designing web-based tools for graduate students: a narrative account of a participatory design action research
Bibliographic record
Abstract
Graduate programs worldwide exhibit a high dropout rate, with numerous contributing factors. Among these, isolation and lack of writing support are significant. This methodological article explores the design process of two web applications supporting graduate students' social and academic needs. It was a collaborative user experience design project involving researchers (including research coordinators), potential users, a non-profit organization, and a web developer. We present the main phases of the study, highlighting the development challenges we faced and their resolutions. The discussion offers six key co-design process lessons to benefit future design research endeavors: 1) take the time to build team cohesion, 2) both types of co-design sessions (knowledge and descriptive) are helpful, but at different moments, 3) do not be afraid to tackle difficult problems with your co-researchers and to admit your limitations, 4) know the co-researchers’ strengths and competencies, 5) fluid communication takes time but is essential and 6) don't be afraid to politically engage your co-researchers.
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 | 0.072 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".