Towards a better understanding of interactions with a domain modeling assistant
Bibliographic record
Abstract
The enrolment of software engineering students has increased rapidly in the past few years following industry demand. At the same time, model-driven engineering (MDE) continues to become relevant to more domains like embedded systems and machine learning. It is therefore important to teach students MDE skills in an effective manner to prepare them for future careers in academia and industry. The use of interactive online tools can help instructors deliver course material to more students in a more efficient manner, allowing them to offload repetitive or tedious tasks to these systems and focus on other teaching activities that cannot be easily automated. Interactive online tools can provide students with a more engaging learning experience than static resources like books or written exercises. Domain modeling with class diagrams is a fundamental modeling activity in MDE. While there exist multiple modeling tools that allow students to build a domain model, none of them offer an interactive learning experience. In this paper, we explore the interactions between a student modeler and an interactive domain modeling assistant with the aim of better understanding the required interaction. We illustrate desired interactions with three examples and then formalize them in a metamodel. Based on the metamodel, we explain how to form a corpus of learning material that supports the assistant interactions.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".