Persevering During a Pandemic: The Resilience of Assessment Professionals During Challenging Times
Bibliographic record
Abstract
As many schools and institutions of higher learning moved instruction online due to COVID-19, assessment of student learning outcomes also followed suit in various forms. Reports of assessment activities conducted in different institutions since March 2020 have started to emerge in the literature. This special issue of Intersection: A Journal at the Intersection of Assessment and Learning in collaboration with AALHE’s Emerging Dialogues publication, seeks to add to the scholarly conversation on the topic by bringing together case studies of assessment practices at different institutions during COVID-19. These assessment practices apply both to activities in the front lines, i.e., embedded in courses, and those behind the scenes, that is those involved with supporting instructors in evaluating learning outcomes.
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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 | 0.035 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.035 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.004 | 0.043 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".