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Record W4401924571 · doi:10.1177/23792981241267744

Back to the Future: Implementing Large-Scale Oral Exams

2024· article· en· W4401924571 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Teaching Review · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsWestern University
Fundersnot available
KeywordsConversationLimitingAnalyticssortScale (ratio)Frame (networking)Computer sciencePsychologyMedical educationMathematics educationData scienceMedicineEngineering

Abstract

fetched live from OpenAlex

We did something unthinkable: we used an oral exam in our undergraduate business analytics course where course enrollment exceeded 600 students. ConVOEs, Concurrent Video-Based Oral Exams, are assessments conducted through a learning management system whereby all students simultaneously engage in an exam and submit a video of themselves responding to each question. By limiting the time frame over which the assessment can be completed, ConVOEs resemble a sort of conversation, as students must respond in real time to each question and demonstrate their understanding of course material in their own words. The conception of this format, though initially motivated by the shift to online learning during the pandemic, is particularly relevant as richer forms of communication are highly sought by employers yet absent from many business courses, and serves to highlight the old adage, “Everything old is new again.”

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.008

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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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