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Record W3173854273 · doi:10.24908/pceea.vi0.14946

COORDINATING THE INSTRUCTION OF FOUR ONLINE COURSES

2021· article· en· W3173854273 on OpenAlexaffvenue
Seach Chyr Goh, Jannik Haruo Eikenaar, M. R. K. Shirazi

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsScheduleMedical educationCoronavirus disease 2019 (COVID-19)Class (philosophy)GlobeHealth scienceMathematics educationCohortPandemicPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

The University of British Columbia Vantage College offers a pathway for academically qualified international students who do not yet meet the English language admission requirements fordirect entry into UBC. In the summer of 2020, during the COVID-19 pandemic, we taught four courses to a cohort of 64 students scattered across the globe. The courses were taught online and asynchronously, raising coordination challenges in terms of class schedule and delivery, assessment, and student support. To address those challenges, we developed a highly structured weekly schedule, specifying lecture and assessment days, as well as regular, synchronous office hours. We met weekly to keep each other updated about the progress of students. Students falling behind in multiple courses were reported in an “early alert” system: a university-specific resource through which students are contacted by health and wellness staff. A midterm survey was conducted and the feedback was generally positive. Final results in the courses were varied, with some comparable

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)0.011
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0050.001
Scholarly communication0.0060.001
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.017

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 head0.054
GPT teacher head0.358
Teacher spread0.304 · 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 designNot applicable
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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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