UML Mentor: A Tool for Interactive and Collaborative Software Design Education
Bibliographic record
Abstract
This lightning talk describes a homegrown digital educational tool, Unified Modeling Language Mentor (UML Mentor), that allows students to participate in software design challenges and create UML diagrams. Introducing design patterns in an undergraduate object-oriented software design course offers a unique opportunity to embed good design techniques, which can be transferred to real-world scenarios. UML Mentor encourages students to evaluate software design challenges from diverse perspectives by experimenting and reflecting through UML diagram creation. The software design challenges consist of a description, use cases, and expected functionality. Each challenge describes a program for which the students are expected to create a UML class diagram. Once students have completed creating the UML diagram for a challenge, they can post it for others to review. We recognize that providing feedback on UML diagrams can be time-consuming for CS educators, especially because there can be multiple valid design patterns acceptable for a challenge. As a result, in UML Mentor, students can collaborate and provide formative peer feedback through comments. To encourage community building and mentoring in the classroom, the original creator of a UML diagram can mark some peer comments as 'helpful' to show gratitude towards the commentator. Our tool helps students build confidence in creating UML diagrams according to diverse design patterns and facilitates peer feedback. During the talk, we will do a walk-through of an example software design challenge, showcase implemented features, and gather participant input and critique on UML Mentor to improve and inform future releases.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.019 |
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".