Covidaze Juggle: Undergraduate Teaching Assistants (TAs) with a Large Online Freshmen Course
Bibliographic record
Abstract
As undergraduate students in a Health Sciences Program we were selected as teaching assistants (TAs) in a freshman introductory Cellular and Molecular Biology course that we had all taken in a standard format. The course was tightly focused on cell communication ( Adv Physiol Educ 36: 13–19, 2012, Biochem Mol Biol Educ. 2013 May‐Jun;41(3):145‐55). The new version was offered synchronously on‐line to 273 students who were in different time zones (within Canada and abroad, Africa, Asia). Didactic sessions (both flipped/non‐flipped) were followed by TA sessions (60‐90 mins.) designed to help students consolidate content and prepare them for active assessments used (The FASEB Journal, 31: 575.2‐575.2.). Each tutorial Group had on the average, twenty students. For the tutorials, we met them in virtual break‐out rooms where we had considerable flexibility to organize our sessions. Larger groups were reconvened to meet the instructors either on the same day or on a separate session. These sessions served to further consolidate their learning. In addition, we had the options of organizing office hours on our own to deal with our students. We were taking several of our own on‐line courses in parallel. These dual obligations as teachers in one course and learners for several others posed many challenges. As teachers, we had to foster engagement, promote interactions, gauge comprehension, maintain enthusiasm, identify individual learning needs despite lack of verbal, non‐verbal cues as many students remained both silent and invisible and also deal with technical glitches. To prepare for our own courses we faced similar technical issues, maintained enthusiasm, battled online fatigue, engaged with our Professors and TAs, dealt with conflicting schedules, found resources, remained flexible, and stayed focused as the lack of a distinct campus environment blurred boundaries between home and academia. We adapted rapidly to cope with these concurrent contrary demands.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.028 |
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".