What do we need from modeling tools for teaching? A survey of the community of higher-education modeling teachers
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
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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