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Where is the Support? Learning Support for Multimodal Digital Writing Assignments by Writing Centres in Canadian Higher Education

2021· article· en· W3204366983 on OpenAlexaffvenueabout
Stephanie Bell, Brian Hotson

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsYork University
Fundersnot available
KeywordsPreparednessHigher educationContext (archaeology)PedagogyAcademic writingPsychologyMedical educationSociologyPolitical scienceMedicineGeography

Abstract

fetched live from OpenAlex

Writing centres play a vital role in supporting all forms of student academic writing in higher education (HE) institutions, including digital writing projects (DWPs)—multiliterate and multimodal, often video-and-audio-based projects, produced using digital technologies. The importance of writing support for multimodal composing is evident in emerging research on both the multi-skilled practices of writer-designers and the conceptual shifts involved in their adoption. Currently, no research exists regarding the Canadian context of writing centre support for DWPs. To address this, we conducted two surveys: one of 22 Canadian writing centres asking about DWPs prevalence, technology and skills readiness, and DWP awareness; and one of faculty at a large Canadian university, asking about DWPs prevalence and frequency and types of DWP assignments. We find a significant disconnect between the number of DWPs being assigned by faculty and the number being supported in writing centres. We also find a significant lack of writing centre preparedness for supporting DWPs. This paper calls, with some urgency, for writing centres to invest in the reality of student writing in Canadian HE, to begin developing instructional materials, equipment, and skilled staff to support DWPs.

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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 teacher head, not a consensus.

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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueThe Canadian Journal for the Scholarship of Teaching and LearningSame topicDiscourse Analysis in Language StudiesFrench-language works237,207