Open book assessment: performance and student perceptions in a UK veterinary programme
Bibliographic record
Abstract
The Covid-19 pandemic declared in March 2020 led to many academic institutions moving to online teaching and remote assessment, including use of the Open Book (OB) assessment format. The aim of this study was to investigate the impact of an OB format on student performance, and to explore students’ perceptions of OB assessment at a UK veterinary school. A mixed methods approach was utilised across three phases of study. Student performance data were compared for Closed Book (CB) and OB conditions. A questionnaire investigated students’ perceptions of OB assessments and subsequent focus groups further explored students’ experiences. Overall mean marks increased with the change from CB to OB. Students who scored higher in CB assessments tended to have a smaller increase in marks between the formats than those with lower average marks. Students reported less stress associated with the OB format and described a deeper approach to learning. However, a focus on organisation of resources reflects a strategic approach, and concerns regarding examination integrity should not be overlooked. This study highlighted the benefits of the OB format to student learning, wellbeing and performance and the findings can be used to inform educators considering the format within a programme of assessment.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".