Assessment-focused Model for Monitoring Student Attributes
Bibliographic record
Abstract
Faculty at the University of New Brunswick have worked collaboratively to develop a streamlined monitoring process for graduate attributes intended to be easy to understand, efficient, and comply with intentions laid out by the Canadian Engineering Accreditation Board. The monitoring process is made up of two parts: An assessment-focused model for monitoring student progress, and a course mapping exercise for monitoring learning opportunities. In monitoring student progress, typical student assessments are used as opportunities for students to demonstrate that expectations are being met in the context of attributes. This provides a transparent mechanism for instructors to produce evidence that their students are developing attributes. To date, expectations for six of the twelve attributes have been articulated in a rubric, and four of the attributes have been tracked. Our experience thus far indicates that our monitoring process allows us 1) to uniformly express expectations regarding graduating student attributes across programs, 2) to indentify assessments which provide opportunities for our students to demonstrate the behaviors outlined in our expectations, and 3) to use results of the assessments to easily summarize data about the attributes of our graduating students.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".