A Survey of Tool Use in Modeling Education
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
We present the results of a survey of tool use in software modeling education conducted from December 2016 to March 2017. The survey was conducted among 150 professors who taught modeling in 30 countries from all regions of the world. Professors reported using 32 modeling tools. Top motivations for choosing tools are simplicity of learning and installing, as well as the tools being free and supporting the most important notations. Top complaints about tools included not interacting with other tools, not supporting sufficient modeling aspects, and being complex to use. Seven of the tools were used by more than one professor as their main tools, and we analyzed these in more depth. Among these 7, lack of feedback about models emerged as another key weakness. The tools varied very considerably regarding which of these strengths and weaknesses they exhibited. The key lessons from the paper are a) that tool developers have many opportunities to improve their products, and b) that educators might benefit from introducing students to multiple different tools.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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