Moving toward student-faculty partnership in systems-level assessment: A qualitative analysis
Bibliographic record
Abstract
Partnership models have been effective across many areas of higher education such as involving students as teaching and learning consultants, in course design and redesign, and as co-instructors. However, there are few systems-level (i.e., entire programs or institutions) examples of partnership work and virtually none in systems-level assessment. Systems-level assessment models, such as program-level assessment in the United States, are used to inform broad changes to academic programs. Thus, student input may be crucial. This study sought to explore the broad factors that underlie potential student-faculty partnership efforts in systems-level assessment. Participants were faculty and staff members based in the United States and the United Kingdom who engaged in student-faculty partnerships at the program and/or classroom level. Qualitative coding and analyses of interviews with participants resulted in seven primary themes. This study examines patterns evident in student-faculty partnership work across several areas of higher education and begins to lay the foundation for a theory of student-faculty partnership in systems-level assessment.
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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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".