The usability of a visual, flow-based programming environment for non-programmers
Bibliographic record
Abstract
Living with “Big Data” gives us the advantage of being able to exploit this wealth of data sources and derive useful insights to make better decisions, enhance productivity, and optimize resources. However, this advantage is limited to a small group of professionals, with the rest of the population unable to access this data. Lack of support for non-professionals creates the need for data manipulation tools to support all sectors of society without acquiring complex technical skills. “Kit” is a visual flow-based programming environment that aims to facilitate manipulation and visualization for all citizens, particularly non-programmers, enabling them to have hands on data in an easy manner. This study evaluates Kit’s usability by having non-programmers involved in various evaluation activities to assess their ability to solve data-related problems using a prototype of the environment. The results provided useful insights to improve the design of data manipulation tools aiming to support non-programmers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".