Bibliographic record
Abstract
With the increasing adoption of Model-Based Software Engineering (MBSE) to handle the complexity of modern software systems in industry and inclusion of modelling topics in academic curricula, it is no longer a question of whether to use MBSE but how to use it. Acquiring modelling skills to properly build and use models with the help of modelling formalisms are non-trivial learning objectives, which novice modellers struggle to achieve for several reasons. For example, it is difficult for novice modellers to learn to use their abstraction abilities. Also, due to high student-teacher ratios in a typical classroom setting, novice modellers may not receive personalized and timely feedback on their modelling decisions. These issues hinder the novice modellers in improving their modelling skills. Furthermore, a lack of modelling skills among modellers inhibits the adoption and practice of modelling in industry. Therefore, an automated and intelligent solution is required to help modellers and other practitioners in improving their modelling skills. This doctoral research builds an automated and intelligent solution for one modelling formalism - domain models, in an avatar of a domain modelling bot. The bot automatically extracts domain models from problem descriptions written in natural language and generates intelligent recommendations, particularly for teaching modelling literacy to novice modellers. For this domain modelling bot, we leverage the capabilities of various Artificial Intelligence techniques such as Natural Language Processing and Machine Learning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".