Covidaze Juggle: Undergraduate Teaching Assistants (TAs) with a Large Online Freshmen Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it