INTEGRATING UCD WITHIN AN AGILE SOFTWARE DEVELOPMENT PROCESS IN AN EDUCATIONAL SETTING
Bibliographic record
Abstract
The Canadian Engineering Accreditation Boardhas defined 12 attributes that an institution must demonstrategraduates of its engineering program possess. We are inpursuit of the attribute "Design” dealing with the students’ability to select candidate engineering design solutions fordevelopment, with three indicators relating to how candidatesolutions are selected. Our approach to teaching “Design” isbased on “learning outcomes” rather than “teaching inputs”.In this paper, we describe the learning outcomes of teaching anewly proposed Integrated User Centered Design (UCD)-Agile Process in the context of a one term project coursewherein teams of undergraduate students apply what theyhave learnt about Agile software development and UserInterface (UI) design in the context of a real-world projectwith actual clients. The Integrated UCD-Agile Processincludes upfront design of the UI in parallel with developmentof the functionalities, UI design specialists for each sprint andusability testing of all UI design decisions
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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".