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
To make computational thinking appealing to young learners, initial programming instruction looks very different now than a decade ago, with increasing use of graphics and robots both real and virtual. After the first steps, children want to create interactive programs, and they need a model for this. State diagrams provide such a model. This paper documents the design and implementation of a Model-Driven Engineering tool, SD Draw, that allows even primary-aged children to draw and understand state diagrams, and create modifiable app templates in the Elm programming language using the model-view-update pattern standard in Elm programs. We have tested this with grade 4 and 5 students. In our initial test, we discovered that children quickly understand the motivation and use of state diagrams using this tool, and will independently discover abstract states even if they are only taught to model using concrete states. To determine whether this approach is appropriate for children of this age we wanted to know: do children understand state diagrams, do they understand the role of reachability, and are they engaged by them? We found that they are able to translate between different representations of state diagrams, strongly indicating that they do understand them. We found with confidence p<0.001 that they do understand reachability by refuting the null hypothesis that they are creating diagrams randomly. And we found that they were engaged by the concept, with many students continuing to develop their diagrams on their own time after school and on the weekend.
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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