Faculty-student pedagogical partnership in the virtual classroom: Lessons from COVID-19
Bibliographic record
Abstract
Kong had its small but significant beginnings in 2014 1 .Fitting into the "pedagogic consultancy" quadrant of Healey et al.'s (2016) Students as Partners (SaP) conceptual model, the program does not involve curriculum design, but rather aims to more directly enhance teaching and learning in the classroom.Trained and paid student partners (SPs) each work with a faculty member for a semester at a time, conducting regular classroom observations, writing post-observation reflection reports, and then dialoguing with their faculty partners (FPs) in weekly meetings, considering classroom dynamics, practices, and pedagogical issues from their differing teacher/student perspectives.SPs also meet regularly together with the program leader(s) throughout the semester for ongoing support and training.More details on how the program is run are outlined in Pounder et al. (2016).The first semester of 2020 in Hong Kong threw out unexpected challenges for our team.Hong Kong was one of the first places hit by COVID-19.By the second week of the semester, local universities were suddenly and unexpectedly thrown into an online learning mode, with no warning or preparation time.Rather than hoping for a resumption of live classes, we decided to adapt the program to the online mode.SPs were initially very skeptical.After all, how could they perform their central role of observation and giving constructive feedback when there were no live classes to observe and when teachers were adopting a variety of
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".