A Multi-Institutional Alternative Assessment Faculty Learning Community: Supporting Teaching in Higher Education
Bibliographic record
Abstract
Faculty learning communities (FLCs) provide opportunities for professional development for faculty, teaching staff, and educational developers in a collaborative and open environment. In this essay, we describe our experience organizing, facilitating, and participating in a multi-institutional FLC with a theme of alternative assessments in STEM. We describe our goals and objectives, the recruitment process and composition, and the structure and scholarly process of the FLC. Based on focus groups conducted with FLC participants, we discuss our collective experience, highlighting outcomes and lessons learned and identifying challenges. Finally, we provide our recommendations for organizing and facilitating multi-institutional FLCs. Primary Image: Close-up photo of a laptop screen with one person’s hand shown using the trackpad and another person’s hand shown pointing at something on the screen, by user John Schnobrich on Unsplash.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".