Location Matters: Using Online Writing Tutorials to Enhance Knowledge Production
Bibliographic record
Abstract
Students enrolled in asynchronous online courses explore much of the subject matter through computer-mediated discussion. In this context, students must often negotiate complex factors such as the course content, the assignment goals, their audience, disciplinary expectations, and the writing process. Writing Centres offer students support services to help them succeed in these text-heavy courses. Typically, students come to Writing Centres in person for help with their critical reading and writing assignments; however, increasingly, tutors are asked to participate in online settings to assist student learning. A question associated with online tutoring practices is whether students improve their writing skills when they are given the opportunity to get feedback from a tutor and from peers. How can a cooperative, collaborative pedagogical approach to computer-mediated tutoring support students and improve teaching? This paper examines a pedagogical exploration where one tutor interacted asynchronously with students by posting weekly writing activities. Students were asked to respond individually and collaboratively to each activity. I argue that when a tutor in an online course provides feedback, the collaboration creates a new online ecology of reflection and collaboration that may benefit students in their growth as writers. This exploration can be a useful writing pedagogy that can assist instructors by making stronger connections between students’ writing knowledge and writing practices.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".