Exploring the Benefits of Online Labs for On-Campus Teaching
Bibliographic record
Abstract
With the move to online teaching and learning in response to COVID-19, online biology labs are no longer a niche endeavor and more of us than ever now have some experience teaching in this mode.As we look forward to a return to campus, how might our online teaching experiences inform our face-to-face teaching?Researchers have investigated the changes in attitudes and strategies of instructors in a variety of disciplines who have returned to face-to-face teaching after having taught online.For example, Kearns (2016) found that instructors became more aware of the potential applications of online technologies, saw less of a distinction between in-class and out-ofclass learning activities, and demonstrated an increased focus on how students learn, while Andrews Graham (2019) documented changes in instructors' communication strategies, instructional practices, and perceived roles in the classroom.This panel discussion explored the theme of 'transferable benefits of online teaching' in the context of laboratory teaching; panelists shared insights and specific examples of how experience with online labs can make our face-toface labs better.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".