Block-based or graph-based? Why not both? Designing a hybrid programming environment for end-users
Bibliographic record
Abstract
Abstract End-user programmers need programming tools that are easy to learn and use. Development environments for end-users often support one of two visual modalities: block-based programming or data-flow programming. In this work, we discuss differences in how these modalities represent programs, and why existing block-based programming tools are better suited for imperative tasks while data-flow programming better supports nested expressions. We focus on robot programming as an end-user scenario that requires both imperative and expressions-based code in the same program. To study how end-user tools can better support this scenario, we propose two programming system designs: one that changes how blocks represent nested expressions, and one that combines block-based and data-flow programming in the same hybrid environment. We compared these designs in a controlled experiment with 113 end-user participants who solved programming and program comprehension tasks using one of the two environments. Both groups indicated a small preference for the hybrid system in direct comparison, but participants who used blocks to solve tasks performed better on average than hybrid system users and gave higher usability ratings. These findings suggest that despite the appeal of data-flow programming, a well-adapted block-based programming interface can lead end-users to more programming success.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".