Triangulated authentic assessment in the HEQCO Learning Outcomes Assessment Consortium
Bibliographic record
Abstract
The Higher Education Quality Council ofOntario (HEQCO) has established a consortium ofinstitutions committed to the development of usefullearning outcomes assessment techniques and to theirwide-scale implementation in their institutions. Queen'sUniversity is one of three universities and three collegesof the consortium, and the Faculty of Engineering andApplied Science (FEAS) is participating due to familiaritywith assessing learning outcomes as part of accreditation.The specific learning outcomes that are of interest toQueen's are Critical Thinking, Problem Solving,Communication and Lifelong learning.The goal of this three-year project is to assess theaforementioned general learning outcomes and cognitiveskills using three assessment methods simultaneously:embedded course assessment, using meta--rubrics toscore student artifacts, and using standardizedtests/surveys. The study will document cost and timerequired to access each of these methods in specificcourses, analyze correlation between scores from thethree methods, and evaluate developments of the genericlearning outcomes over the duration of a program. Weaim to ensure that the work of outcomes assessment issustainable, works within standard course contexts, andcan be integrated into regular course activities. Thepaper identifies the goals of the project, currentapproach, and an example of data collection in one firstyearengineering design course.
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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 | 0.269 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".