Assessment-focused Model for Monitoring Student Attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it