What do we need from modeling tools for teaching? A survey of the community of higher-education modeling teachers
Bibliographic record
Abstract
Abstract We report on an international survey of 59 higher-education teachers of software modeling and model-driven engineering regarding the modeling languages and tools they use, the pedagogic approaches they employ, as well as their desires for features and properties in improved modeling tools for teaching. The survey revealed divergent opinions regarding satisfaction with existing tools, with preferred teaching methods, and with currently used modeling tools. But there was agreement on the need for better user experience in tools, more powerful capabilities, better documentation, and comprehensive libraries of examples. There was a dichotomy between a large majority who want to teach modeling using the core UML-based diagram types, versus smaller groups who want to focus either on formal languages or model transformation. The number of modeling tools in use is large, but educators are not aware of most tools, indicating a very fragmented market. We conclude that there is a need for the community to work toward a smaller set of usable and useful tools. Our analysis will inform the development of better tools and pedagogies for teaching modeling and model-driven engineering.
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.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".