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Record W3187117911 · doi:10.18260/1-2--37103

Evaluating Peer-led Feedback in Asynchronous Design Critiques: A Question-centered Approach

2024· article· en· W3187117911 on OpenAlexaff
Ada Hurst, Christine Duong, Meagan Flus, Gregory Litster, Jordan Nickel, Aaron Dai

Bibliographic record

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAsynchronous communicationComputer sciencePeer reviewHuman–computer interactionComputer networkPolitical science

Abstract

fetched live from OpenAlex

Evaluating peer-led feedback in asynchronous design critiques: a question-centred approach

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)0.120
metaresearch head score (Gemma)0.380
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.380
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.004
Science and technology studies0.0020.003
Scholarly communication0.0080.005
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.264
GPT teacher head0.464
Teacher spread0.200 · 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.

Study designObservational
DomainEvaluation
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

Citations1
Published2024
Admission routes1
Has abstractyes

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