T ransitioning a L arge Biology Class from a n i n Person t o a n Online Exam During the COVID 19 Pandemic
Bibliographic record
Abstract
As occurred in many Universities worldwide, the response to the COVID-19 pandemic required us, professors at Western University (London, Canada), to quickly convert a first-year biology course with over 1200 enrolled students from an in-classroom format to an on-line format. This transition included the course exams. While the first multiple-choice exam in February 2020 was in-person and proctored, we changed the second multiple-choice exam in March 2020 so that it was completed by students online at home without a proctor. We had concerns about this online conversion, including whether the grades would represent student understanding of the course material when access to peers and other resources during the exam was not monitored. In this report we show student scores on the online exam were highly correlated with their prior in-person exam. A similar correlation was observed with prior first-year students who took similar exams in February and March 2019 which were both in-person and proctored. These results provide some reassurance that it is possible to rapidly transition the delivery of an exam from an in-person format to an online format without compromising the exam process.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".