Bibliographic record
Abstract
Project-based learning (PBL) has become a common practice engineering schools, often used in the context of design projects. Team based design projects allow the assessment of a broad range of graduate attributes, such as teamwork, communication, professionalism, ethics, project management, problem solving and design. Assessment of these skills is often qualitative, making assessment more difficult and varied than technical, quantitatively assessed subjects.Most often when we grade the outputs of team-based projects, we assess the team as a whole; assigning one grade to the entire team. Whether it is project reports, formative and summative, presentations or group assignments, team members share a mark. Tools such as peer and self-evaluations and contribution attestations are sometimes used to modify the marks assigned to individuals, relating the relative engagement of students within the team, but they do not clearly link the direct learning outcomes of individuals to specific attributes. Shared grading is done for several reasons. Logistically, it is significantly less workload to mark a single report per group, than to mark individual reports. Second, in professional work, the output of a team is what is important, and is the primary indicator of success. In an academic environment however, it is the specific learning outcomes of the individuals that we wish to assess.
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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".