How Do Course-Based Assessments Change in The Shift to Emergency Remote Teaching? Sustainable Assessment Strategies Through an Authenticity Lens
Bibliographic record
Abstract
The shift from face-to-face (F2F) to emergency remote teaching (ERT) in response to COVID-19 has presented concerns for assessment in student learning. This study presents the comparison of a health science curriculum in F2F and ERT settings regarding assessments (count, type, authenticity) using our Authentic Assessment Tool and institutionally standardized course syllabi. Five hundred and seventeen assessments in 61 courses in ERT were inventoried (count, type) and subsequently categorized as 1 (low), 2 (moderate), or 3 (high) on core authenticity characteristics: realism, cognitive challenge, evaluative judgement criteria and feedback. These data were compared to a recent curriculum-wide F2F scan (457 assessments in 62 courses). Results show in the shift to ERT, the total number of both tests and assignments increased with a greater proportion of marks comprised of assignments (44% ERT versus 37% F2F). Curriculum-wide authenticity scores were similar (1.8 ± 0.4 ERT versus 1.8 ± 0.6 F2F), although this trend was because nearly an equal proportion of courses increased and decreased authenticity. The largest number of courses (n=30) making improvements on individual characteristics of authenticity did so regarding the dimension feedback. This work presents modest yet actionable items to achieve authenticity for consideration in assessment design as institutions begin to produce and consider policies regarding course structure and assessment design in the post-COVID educational context.
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 | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".