Teaching Interaction using State Diagrams
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.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".