Using Synchronous Labs to Build Online Peer Learning Communities and Maintain a MeaningfulLaboratory Experience
Bibliographic record
Abstract
All laboratory educators were thrown into a similar situation by the COVID-19 pandemic.We were all challenged by the sudden shift away from our in-person active learning, hands-on lab activities and student interactions.The need to quickly switch to new online teaching approaches forced us to re-examine and re-prioritize our teaching strategies.To help guide us, we sought inspiration from teaching discussions with colleagues, workshops, consultation with course developers, and established online teaching practices.Converting labs to online resulted in novel approaches that enabled student learning and interaction.Common themes of group work, ability of instructors to easily view student work in progress, use of color and images in lab exercises, shared Excel sheets, and use of peer review emerged during the online lab experience.After our brief presentations, we would like to hear what has worked well for you in a group discussion, with cameras on please.Aspects of these strategies may be useful for us to continue in future, in-person labs.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".