UML crisis! An educational perspective
Bibliographic record
Abstract
Context: Unified Modelling Language (UML) is not only a standard modelling language in the software industry but also the “lingua franca” of software engineering education. However, teaching and learning UML have been both identified as difficult tasks. Aim: This study aims to have a practical and deep understanding of what causes the difficulties in UML education (in addition to the known complexity of UML), and cor- respondingly to propose actionable mitigation strategies. Method: we conducted critical reflection on our educational activities, teaching materials, and students’ learning effects in a software engineering conversion programme, followed by consulting ex- ternal UML experts. Results: We observed and demonstrated a vicious cycle of the UML crisis in education, which in turn drove us to preliminarily propose a set of potential tactics for breaking the vicious cycle and addressing the UML crisis. Conclusions: Practitioners’ lack of good UML knowledge and skills could have been out of educators’ control. Breaking the vicious cycle of the UML crisis in education requires collaborative efforts across the whole community.
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.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".