Age-Inclusive Integrated Development Environments for End-Users
Bibliographic record
Abstract
Computation increasingly pervades modern life in both the professional and personal realms. An example in the personal realm is the maker movement, which has helped expose many end-users to programming. To ensure equitable access to these new programming domains, it is important to ensure that the tools being promoted to these communities can be used broadly. In this paper, we investigate a tinkering-focused integrated development environment for makers who are engaged specifically in customizing designs for hand knitting. Through a controlled experiment with 91 end-users, 32 of whom were over age 50, we identified trends in how differently-aged participants worked through their maker design tasks with our integrated development environment. While older participants found it more challenging to complete assigned design tasks, participants of all ages were more likely to succeed if they decomposed tasks into partially correct programs. However, we found that successful participants of all ages exhibited common traits of engagement, experimentation, and curiosity. Users found the environment engaging and favoured visual feedback both when making progress and when stuck. Our results provide insights into how development environments can be designed to more inclusively support a broader cross-section of end-users.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".