Where is the Support? Learning Support for Multimodal Digital Writing Assignments by Writing Centres in Canadian Higher Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".