Can guided decomposition help end-users write larger block-based programs? a mobile robot experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Block-based programming environments, already popular in computer science education, have been successfully used to make programming accessible to end-users in domains like robotics, mobile apps, and even DevOps. Most studies of these applications have examined small programs that fit within a single screen, yet real-world programs often grow large, and editing these large block-based programs quickly becomes unwieldy. Traditional programming language features, like functions, allow programmers to decompose their programs. Unfortunately, both previous work, and our own findings, suggest that end-users rarely use these features, resulting in large monolithic code blocks that are hard to understand. In this work, we introduce a block-based system that provides users with a hierarchical, domain-specific program structure and requires them to decompose their programs accordingly. Through a user study with 92 users, we compared this approach, which we call guided program decomposition, to a traditional system that supports functions, but does not require decomposition. We found that while almost all users could successfully complete smaller tasks, those who decomposed their programs were significantly more successful as the tasks grew larger. As expected, most users without guided decomposition did not decompose their programs, resulting in poor performance on larger problems. In comparison, users of guided decomposition performed significantly better on the same tasks. Though this study investigated only a limited selection of tasks in one specific domain, it suggests that guided decomposition can benefit end-user programmers. While no single decomposition strategy fits all domains, we believe that similar domain-specific sub-hierarchies could be found for other application areas, increasing the scale of code end-users can create and understand.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| 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 it