Towards a better understanding of interactions with a domain modeling assistant
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
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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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 | 0.000 |
| Open science | 0.000 | 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