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Covidaze Juggle: Undergraduate Teaching Assistants (TAs) with a Large Online Freshmen Course

2021· article· en· W3166555804 on OpenAlexaffabout
Shadi Sadeghian, Jinny Kim, J.B. Penn, Dominic Hass, Wenteng Hou, Sofia Ivanesivic, Declan Lavoie, Jung Woo Park, Aaron Wen, Sophie Zarb, Jake Colautti, Elaine Lynn‐Ee Ho, David Klitovchenko, Aryana Zarandi

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSession (web analytics)EnthusiasmFlexibility (engineering)Flipped classroomComprehensionPsychologyPresentation (obstetrics)Mathematics educationMedical educationComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0040.002
Open science0.0030.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0530.028

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.

Opus teacher head0.041
GPT teacher head0.382
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes2
Has abstractyes

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