The Intersection of Student Assessment and Faculty Learning
Bibliographic record
Abstract
The primary aim of institutional learning outcomes assessment is the creation of a culture of assessment where faculty use evidence-based data to validate and improve teaching and learning for the benefit of students. Faculty are key to these processes and yet, they are often woefully disengaged from them. This paper presents findings from an action research project that utilized a collaborative self-study approach to engage faculty in the strategic assessment of institutional learning (SAIL). SAIL is an immersive professional development opportunity that bridged quality assurance with meaningful improvements in the classroom. Findings indicated that cross-disciplinary dialogue about assessment increased faculty awareness of the (mis)alignment between course, program, and institutional learning aims while also identifying and informing potential gaps in curriculum and program design. SAIL is an excellent mechanism to engage faculty in an immersive assessment of student achievement that may then lead to meaningful improvement in teaching and learning.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".