Does a classroom-based curriculum offer authentic assessments? A strategy to uncover their prevalence and incorporate opportunities for authenticity
Bibliographic record
Abstract
Authentic assessment is revered to support student learning, but it is typically described within the context of work-integrated learning and professional schools, leaving one to question whether a classroom-based curriculum can offer authentic assessments. This study documented the prevalence of authentic assessments throughout a complete health science undergraduate curriculum in accordance with the four core dimensions of authentic assessment: realism, cognitive challenge, evaluative judgement criteria and feedback. Using a literature-informed authentic assessment tool and institutionally standardized course syllabi, 455 assessments in 62 courses were classified as low, moderate or high on core authentic assessment dimensions. Results show that few assessments scored high across all core dimensions (<1% of all assessments), as there was considerable variability across dimensions. Feedback had the weakest dimensional authenticity score. Authentic assessments were more prevalent within upper-year, small capstone courses, although they were not precluded from early-year, large classrooms. Assignments were significantly more authentic than tests, though tests were more dominant in the curriculum (63% marks from tests versus 37% from assignments). This work serves as a model for others seeking to review assessments in their curriculum and provides evidence from a large, representative BSc program to make practical recommendations to promote authenticity of assessments.
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 | 0.028 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".