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Record W2979335464 · doi:10.31468/cjsdwr.737

Reflecting on Assessment: Strategies and Tools for Measuring the Impact of a Canadian WAC Program

2019· article· en· W2979335464 on OpenAlex
Michael Kaler, Tyler Evans-Tokaryk

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWriting assessmentExposition (narrative)Context (archaeology)Variety (cybernetics)Process (computing)Computer scienceEngineering ethicsReflection (computer programming)Engineering managementPedagogyEngineeringSociologyArtificial intelligenceHistory

Abstract

fetched live from OpenAlex

This paper provides an overview of the process and tools we have developed for assessing the impact of writing development projects carried out in a wide variety of courses at our university. It begins with an overview of writing studies in Canada to provide context for our approach to writing instruction and writing program assessment. It then offers a case study of a specific writing development project in a large first-year humanities course, a detailed explanation of the methods we used to measure the efficacy of that project, and an exposition of the way in which this assessment was used to drive reflection on the project and enhancement of it. The paper concludes with summary of the lessons we have learned regarding writing program assessment that navigates between creating a standardized process and responding to the unique needs of multiple projects, as well as a discussion of the benefits of such assessment for writing pedagogy research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.275
GPT teacher head0.534
Teacher spread0.259 · 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